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John P Overington (Medicines Discovery Catapult): Data Mining Small Molecule Drug Discovery
 
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Despite having more information and technology than at any point in history, drug discovery is becoming harder. It is tempting to believe that there was ‘low hanging fruit’ in the past, and that previous generations had easier to treat diseases, simpler biology and a large number of drug-like leads to optimize. Regardless of the cause, there is now a pressing need to understand fundamental complex biological systems, especially those linked to disease pathology. The most definitive tools for illuminating biology for this are often small molecules, and there is now intense interest in developing, in a cost effective way, potent, well distributed and selective chemical probes, then applying these to understand the role of novel genes, potentially leading to a new medicine. Underlying the development of chemical probes and drug leads, is what is known from the past, and what general rules can be learnt that are useful in the future. The presentation will detail the background and development of two large, now public domain, chemical biology databases – ChEMBL and SureChEMBL. These databases, in particular ChEMBL have led to the development of many new algorithms for target prediction, chemical library design, etc. Next four examples of data mining of ChEMBL and other public domain data will be described. 1) A framework to anticipate and integrate into compound design processes the effect of mutations in the target – this is of special importance in the area of anti-infective and anti-cancer drugs where resistance is a significant healthcare issue. 2) An analysis of drug properties according to target class for the antibiotics, where differences in physicochemical properties can be correlated in target properties. 3) Addressing the problem of target validation using genetics, which could de-risk the development of chemical tools and leads, and place novel targets into an appropriate therapeutic setting. 4) Is the concept of ‘Druggability’ real, or has it led to restriction in the number of systems that the community is prepared to work on?
Views: 450 ChemAxon
Data Mining: How You're Revealing More Than You Think
 
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Data mining recently made big news with the Cambridge Analytica scandal, but it is not just for ads and politics. It can help doctors spot fatal infections and it can even predict massacres in the Congo. Hosted by: Stefan Chin Head to https://scishowfinds.com/ for hand selected artifacts of the universe! ---------- Support SciShow by becoming a patron on Patreon: https://www.patreon.com/scishow ---------- Dooblydoo thanks go to the following Patreon supporters: Lazarus G, Sam Lutfi, Nicholas Smith, D.A. Noe, سلطان الخليفي, Piya Shedden, KatieMarie Magnone, Scott Satovsky Jr, Charles Southerland, Patrick D. Ashmore, Tim Curwick, charles george, Kevin Bealer, Chris Peters ---------- Looking for SciShow elsewhere on the internet? Facebook: http://www.facebook.com/scishow Twitter: http://www.twitter.com/scishow Tumblr: http://scishow.tumblr.com Instagram: http://instagram.com/thescishow ---------- Sources: https://www.aaai.org/ojs/index.php/aimagazine/article/viewArticle/1230 https://www.theregister.co.uk/2006/08/15/beer_diapers/ https://www.theatlantic.com/technology/archive/2012/04/everything-you-wanted-to-know-about-data-mining-but-were-afraid-to-ask/255388/ https://www.economist.com/node/15557465 https://blogs.scientificamerican.com/guest-blog/9-bizarre-and-surprising-insights-from-data-science/ https://qz.com/584287/data-scientists-keep-forgetting-the-one-rule-every-researcher-should-know-by-heart/ https://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853 http://dml.cs.byu.edu/~cgc/docs/mldm_tools/Reading/DMSuccessStories.html http://content.time.com/time/magazine/article/0,9171,2058205,00.html https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=all&_r=0 https://www2.deloitte.com/content/dam/Deloitte/de/Documents/deloitte-analytics/Deloitte_Predictive-Maintenance_PositionPaper.pdf https://www.cs.helsinki.fi/u/htoivone/pubs/advances.pdf http://cecs.louisville.edu/datamining/PDF/0471228524.pdf https://bits.blogs.nytimes.com/2012/03/28/bizarre-insights-from-big-data https://scholar.harvard.edu/files/todd_rogers/files/political_campaigns_and_big_data_0.pdf https://insights.spotify.com/us/2015/09/30/50-strangest-genre-names/ https://www.theguardian.com/news/2005/jan/12/food.foodanddrink1 https://adexchanger.com/data-exchanges/real-world-data-science-how-ebay-and-placed-put-theory-into-practice/ https://www.theverge.com/2015/9/30/9416579/spotify-discover-weekly-online-music-curation-interview http://blog.galvanize.com/spotify-discover-weekly-data-science/ Audio Source: https://freesound.org/people/makosan/sounds/135191/ Image Source: https://commons.wikimedia.org/wiki/File:Swiss_average.png
Views: 149340 SciShow
ChemAxon - a chemical and biological software development company
 
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ChemAxon is a cheminformatics and bioinformatics company providing software solutions for life sciences and other industries relying on chemical and biological research. This short introduction emphasizes the key strengths of the company: 1. We understand scientific and IT issues 2. Our software has an outstanding knowledge in chemistry 3. Open and freely accessible documentations and sources 4. Platform independent and cloud based solutions 5. Quick and prompt support and consultancy
Views: 519 ChemAxon
What is Data Mining - Data Science Jargon for Beginners
 
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In this video I am going to give a simple and beginner definition of what data mining is in data analytics. The data science industry is very complicated, so I want to define data mining for you today. ► Full Playlist Explaining Data Jargon ( https://www.youtube.com/playlist?list=PL_9qmWdi19yDhnzqVCAhA4ALqDoqjeUOr ) ► http://jobsinthefuture.com/index.php/2017/11/25/what-is-data-mining-data-science-jargon-for-beginners/ Data Mining. This term is nearly self explanatory, but let's dig into it (haha, dig into it, data mining) and define data mining a little more to clarify any details. Data Miners explore large sets of data in order to discover patterns in the sets. Data miners look for patterns in order to define medical, buying habits, food shortages, etc... If you are going into the field of Data Analytics you will most certainly be doing a great deal of data mining. Data mining is a mass scale version of looking through thousands of people's daily biographies. What I mean by "looking through people's biographies" is you will be trying to understand how people are responding the the situation you are researching via data. Let's say your company releases a new drug to the market. This drug has been tested to stop the process of breakdown in joints that often leads to rheumatoid arthritis. Your drug ships out to 10,000 trial patients. Now you have a 10,000 person data set to manage. As the trial operates and the patients report their daily experience with the new drug you are being flooded with data about the drug. It is your job as the data miner to find the patterns and insights in order to accurately determine whether the drug is safe or not, the drug needs improvements, or perhaps the drug is not as effective as the company had hoped. In a nutshell data mining is a data analysts daily routine of researching data sets in order to learn from the data. Don't miss the Full review on Data Analytics defined and how to get a job! --- http://jobsinthefuture.com/index.php/2017/10/21/data-analyst-salary-and-how-to-become-a-data-analyst/ ------- SOCIAL Twitter ► @jobsinthefuture Facebook ►/jobsinthefuture Instagram ►@Jobsinthefuture WHERE I LEARN: (affiliate links) Lynda.com ► http://bit.ly/2rQB2u4 edX.org ► http://fxo.co/4y00 MY FAVORITE GEAR: (affiliate links) Camera ► http://amzn.to/2BWvE9o CamStand ► http://amzn.to/2BWsv9M Compute ► http://amzn.to/2zPeLvs Mouse ► http://amzn.to/2C0T9hq TubeBuddy ► https://www.tubebuddy.com/bengkaiser ► Download the Ultimate Guide Now! ( https://www.getdrip.com/forms/883303253/submissions/new ) Thanks for Supporting Our Channel! DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This help support the channel and allows us to continue to make videos like this. Thank you for the support!
Views: 578 Ben G Kaiser
C4X Discovery Investors Overview with CEO Clive Dix
 
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LON:C4XD Clive Dix giving an investor led overview of the business. We recently filmed Clive Dix who gave us an overview of C4X Discovery (LON:C4XD). C4XD are an AIM listed business which develops technologies for use in drug discovery. A product called Taxonomy 3 identifies genes associated with disease. A second technology called Conformetrix identifies molecules which will work against those proteins. The company use data mining, learning technologies and recently released a virtual reality product which helps chemists visualise molecules and proteins. C4X develop molecules to the point where they can be licensed, rather than takin them to the clinic themselves. Using this deal format, they recently signed a deal with Indivior which provides a $10m upfront payment with the potential for a further $284, based on milestones. An investor relations film production from Five Minute Pitch TV
Mining electronic health records and the web for drug repurposing,  Kira Radinsky (eBay | Technion)
 
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Check out all the Strata Data Conference keynotes, sessions, and tutorials here: https://www.safaribooksonline.com/library/view/strata-data-conference/9781491985373/ Researchers decide on exploratory targets for drug repurposing—the process of applying known drugs in new ways to treat diseases—based on trends in research and observations on small numbers of cases, leading to potentially costly biases of focus and neglect. Kira Radinsky offers an overview of a system that jointly mines 10 years of nationwide medical records of more than 1.5 million people and extracts medical knowledge from Wikipedia to help reduce spurious correlations and provide guidance about drug repurposing. The resulting system seeks to identify potential biological processes to justify potential influences between medications and target diseases via links on a graph constructed from Wikipedia data. Kira shares results of the system on two studies on drug repurposing for hypertension and diabetes. In both cases, the algorithm identified drug families that were previously unknown, and clinical opinion by experts in the field and clinical trials on those drug families suggest that these drugs show promise. Subscribe to O'Reilly on YouTube: http://goo.gl/n3QSYi Follow O'Reilly on: Twitter: http://twitter.com/oreillymedia Facebook: http://facebook.com/OReilly Instagram: https://www.instagram.com/oreillymedia LinkedIn: https://www.linkedin.com/company-beta/8459/
Views: 3821 O'Reilly
Mining the FDA Adverse Event Reporting System with Oracle Empirica Signal
 
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Learn how to identify safety and pharmacovigilance signals by data mining FAERS with Oracle's Empirica Signal. -- Ever since the European Union (EU) introduced new legislation that requires life sciences companies to proactively detect, prioritize, and evaluate safety signals, there has been an increased interest, not only from sponsors and CROs in the EU, but globally, in pharmacovigilance systems that can assist with the signal management process. Please join Perficient's Chris Wocosky, an expert in signal detection and management, for this video in which she discussed how your organization can use Empirica Signal, Oracle's state-of-the-art signal detection system to data mine the existing FDA Adverse Event Reporting System (FAERS) to determine safety signals. This video will help you to better understand how this solution can be used in daily pharmacovigilance activities. To view this webinar in its entirety, please visit: https://cc.readytalk.com/r/7ekwxbm7q33t&eom Stay on top of Life Sciences technologies by following us here: Twitter: http://www.twitter.com/Perficient_LS Facebook: http://www.facebook.com/Perficient LinkedIn: http://www.linkedin.com/company/165444 Google+: https://plus.google.com/+Perficient SlideShare: http://www.slideshare.net/PerficientInc
Views: 4932 Perficient, Inc.
ADDoPT | Big data analytics to accelerate UK digital drug design
 
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Adrian Toland and Christelle Gendrin from the STFC Hartree Centre give their first thoughts on how the ADDoPT (Advanced Digital Design of Pharmaceutical Therapeutics) project is aiming to address key challenges for the pharmaceutical industry. Adrian Toland and Christelle Gendrin from the STFC Hartree Centre give their first thoughts on how the ADDoPT (Advanced Digital Design of Pharmaceutical Therapeutics) project is aiming to address key challenges for the UK pharmaceutical industry. Announced on 28th January 2016, the ADDoPT project is a £20.4M four-year collaboration aiming to develop advanced digital design techniques that will reduce drug development time and cost. For a full list of partners and more information, please see: http://www.stfc.ac.uk/news/hartree-centre-collaboration/ Follow us on Twitter: @HartreeCentre Connect with us on LinkedIn: http://www.linkedin.com/company/stfc-hartree-centre For more information on the STFC Hartree Centre: http://www.stfc.ac.uk/hartree
LIBER-C4C Event September, 2013 - A Workshop on Text & Data mining for Data Driven Innovation
 
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LIBER organised a workshop, with the support of C4C, on the future potential and challenges of Text and Data Mining (TDM) at the British Library in London. The Workshop looked at the potential of TDM to support European research and industrial activity/new startup companies. A list of distinguished speakers made a convincing case for TDM. As one speaker said, the Web is useless without it. Check the event summary on the LIBER website: http://www.libereurope.eu/blog/the-perfect-swell-at-the-british-library
Big Data: Actionable Information
 
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With support from the National Science Foundation and major technology companies such as IBM and Hewlett Packard, Professor Elke Rundensteiner leads a research group focused on very large database and information systems in support of advanced applications in business, engineering, and the sciences. Much of her current work focuses on Big Data and the development of tools to extract actionable information out of complex data sets. In the area of health care, for example, she and her team are working with the Food and Drug Administration to develop machine learning algorithms that can pore over reports on incidents involving drugs and medical devices. Because of inconsistencies in how the reports are filled out, they are difficult to analyze, meaning possible health threats may go unnoticed. Rundensteiner's tools learn to locate and rank the relevant information, which can help important—and, possibly, life-saving—patterns rise to the surface.
Views: 572 WPI
Big data aids drug development
 
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Pharmaceutical companies partner on big data project to inform drug development
Kira Radinsky: Using Data to Predict Genocide, Disease, Riots, Drug Effects, and More
 
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At the Jewish Funders Network 2018 International Conference, Dr. Kira Radinsky (Chief Scientist and Director of Data Science at eBay) discussed her work using data to predict events and issues from ebola and cholera to genocide and riots, and model complex systems of causality like drug effects. Dr. Kira Radinsky joined eBay in 2016 after the acquisition of her company, SalesPredict, and serves today as the director of data science of eBay where she builds the next generation machine learning solutions that will transform e-commerce. Kira gained international recognition for her work at the Technion and Microsoft Research for developing predictive algorithms that recognized the early warning signs of globally impactful events such as epidemics and political unrest. In 2013, she was named one of MIT Technology Review’s 35 Young Innovators Under 35 and in 2015 Forbes included her as “30 Under 30 Rising Stars in Enterprise Tech”. She is a frequent presenter at global tech and industry conferences, including TEDx, WWW, and Strata, and she published in the Harvard Business Review. She also serves as visiting professor at the Technion, focusing on the application of predictive data mining in medicine.
Can Data Make a Medicine? - with Patrick Vallance
 
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Patrick Vallance, President of Research and Development at GlaxoSmithKline, explores how real-time data and open-source information can improve the world of medicine. Watch the Q&A: https://www.youtube.com/watch?v=aClJ1QZYsVw Subscribe for regular science videos: http://bit.ly/RiSubscRibe Patrick Vallance is President, R&D at GlaxoSmithKline (GSK). Prior to this, he was Senior Vice President, Medicines Discovery and Development. Patrick joined the company in May 2006 as Head of Drug Discovery. He is a member of the Board and the Corporate Executive Team. Prior to joining GSK, Patrick was a clinical academic and as Professor of Medicine led the Division of Medicine at University College London. He has worked in clinical medicine, general internal medicine, cardiovascular medicine and clinical pharmacology for over 20 years. He has an international reputation as a vascular biologist and clinician scientist. His research has spanned structural and molecular studies, in vitro and in vivo experimental pharmacology and clinical studies including the use of large-scale patient databases. At GSK, he has transformed the discovery engine to focus on therapy areas that are underpinned by the most promising and mature science, and which offer fresh insights into diseases. He is also responsible for creating small, empowered research teams, called Discovery Performance Units. This approach has led to a new wave of medicines for diseases from malaria through to cancer treatments. He has championed open innovation and transparency of clinical trial results. This Discourse was filmed at the Ri on 26 May 2017. Subscribe for regular science videos: http://bit.ly/RiSubscRibe The Ri is on Twitter: http://twitter.com/ri_science and Facebook: http://www.facebook.com/royalinstitution and Tumblr: http://ri-science.tumblr.com/ Our editorial policy: http://www.rigb.org/home/editorial-policy Subscribe for the latest science videos: http://bit.ly/RiNewsletter
Views: 5688 The Royal Institution
Clinical Trials Technology: Bringing it all together with PAREXEL®
 
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PAREXEL® Informatics, Corporate Vice President Ken Faulkner discusses his company’s unique Perceptive® MyTrials solution. All along your new drug’s development path, PAREXEL® Informatics has technological innovations that make the process faster, more precise, more trackable, and more productive. PAREXEL® leads the industry in creating integrated platforms and applications specifically designed to improve how biopharmaceutical companies perform clinical trials, control and share data, track and report patient outcomes, and manage regulatory information worldwide. Technology: another example of how PAREXEL® is bringing it all together. Learn more at: https://www.parexel.com/solutions/informatics/ See how PAREXEL brings it all together: Consulting: https://www.youtube.com/watch?v=giZotfdHRhw Clinical Research Services: https://www.youtube.com/watch?v=5EHb6Qd5JNQ
Views: 5309 PAREXEL International
Use of Big and Real-World Data by Pharma: More than Data Warehousing
 
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Real-world data—a subset of big data—is clinical data used for decision-making that did not come from a clinical trial. According to Aaron Galaznik, MD, senior director, real-world data and analytics at Pfizer, Inc, once integrated, the plethora of datasets will allow for a holistic view of disease processes and drivers that will identify patterns of use and opportunities to improve care in different patient populations. The synergistic combination of clinical and real-world data will ultimately improve the personalization of care patients receive. For more information, please visit http://www.npcnow.org/issues/comparative-effectiveness-research.
Views: 2136 npcnow
In vitro ADMET Considerations for Drug Discovery and Lead Generation
 
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Adrian Sheldon, PhD, Associate Director of In Vitro ADMET at Charles River Laboratories, Inc., presents at the Drug Development "Boot Camp": Practical Aspects of Positioning Your Research. This "boot camp" was hosted by The Ohio State University Comprehensive Cancer Center – James Cancer Hospital and Solove Research Institute.
Role of Epidemiological Data within the Drug Development Lifecycle: A Chronic Migraine Case Study
 
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A public health seminar recorded on October 17, 2011. Epidemiologists working in the pharmaceutical industry use the principles of descriptive epidemiology in addition to applied concepts and methods to assess the impact, use and effects of drugs in the population and in clinical trial settings. As there is a wide spectrum of data needs and requirements during any drug's lifecycle, a summary, including a real-world example, of the epidemiological data and methods utilized specifically during clinical development phase is timely. This presentation will provide a case study describing how epidemiological data was used to support the registration of onabotulinumtoxinA (BOTOX®, Allergan, Inc., Irvine, CA, USA) for treatment of headaches in adults with chronic migraine.
Views: 851 UCI Open
Intel ®Pharma Analytics Platform | Intel Business
 
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Intel's AI platform for pharmacological clinical trials. For more information please contact [email protected] Subscribe now to Intel Business on YouTube: http://intel.ly/intelitcenteryt About Intel Business: Get all the IT info you need, right here. From data center to devices, the Intel® Business Center has the resources, guidance, and expert insights you need to get your IT projects done right. Connect with Intel Business: Visit Intel Business's WEBSITE: http://intel.ly/itcenter Follow Intel Business on TWITTER: https://twitter.com/IntelITCenter Follow Intel Business on LINKEDIN: https://www.linkedin.com/showcase/intel-business Follow Intel Business on FACEBOOK: https://www.facebook.com/IntelBusiness Intel ®Pharma Analytics Platform | Intel Business https://www.youtube.com/intelitcenter
Views: 1999 Intel Business
What are the Biggest Challenges in Microbiome Drug Development?
 
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Next Microbiome Drug Development Summit will happen in Paris, January 2018, Find out more at: http://microbiome-europe.com/ Why Join the ‘Microbiome Movement’? Using insight gained from the most successful microbiome organizations across the globe, the only end to end drug development meeting is back to help accelerate emerging and well established companies discover, develop and deliver a new generation of microbiome-based therapeutics.
Views: 170 Hanson Wade
Natasha Jaques: "Recent advances in AI and machine learning" - English Version | Starsconf 2018
 
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Artificial intelligence is one of the most rapidly growing domains in computer science, due to accelerated progress in the areas of machine learning and deep learning. Companies are aggressively expanding the size of their machine learning teams, and constantly finding new ways to deploy these techniques in products. So why have machine learning and deep learning garnered so much attention recently? This talk will give a brief introduction to these topics, and go over some recent advances they have made possible. Natasha will then describe several of her own recent research projects, spanning machine learning for healthcare, creative AI, and reinforcement learning. For example, she will show how we can use machine learning to accurately predict a person’s happiness, stress, and health tomorrow, using data collected from their smartphone today. Deep learning can also successfully generate novel molecules to aid in drug discovery, novel music, and even learn to draw! Finally, she will show how endowing AI agents with social awareness can improve their ability to learn. Versión del video en español en: https://youtu.be/nvSwFoBCnsI
Views: 4368 StarsConf
BenevolentBio's Professor Jackie Hunter on Transforming Drug Discovery at Disrupt London 2016
 
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Professor Jackie Hunter of BenevolentBio chats with Sarah Buhr about using AI to trawl the global ocean of scientific and medical data for pharmaceutical R&D.
Views: 1071 TechCrunch
Drug Interactions - Data Mining Will Help FDA
 
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http://aintthatamazing.org With the help of Microsoft's data from searches on Bing, Google and Yahoo, unknown drug interactions have been found using data mining. Scientists found data showing drug interaction side effects before the FDA had been notified. This could be a remarkable tool. One hope is that search engine companies could analyze the data and ship the results to FDA, which would then screen the information and alert doctors of new potential risks. http://aintthatamazing.org
Views: 164 Bob Perry
webinar recording:  fragment-based drug discovery in an integrated informatics environment
 
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From the outset, Astex have committed to developing a highly integrated informatics platform for fragment-based drug discovery. This ensures scientists from all disciplines have all relevant data at their fingertips, and creates an environment that democratizes the drug-discovery process. Here, we will discuss a number of tools that we have developed to achieve this. These range from chemoinformatics approaches to improve our fragment library and assist in the hit-validation stages of a project, to the use of protein-ligand structural databases for hit-to-lead optimization. guest speaker: Marcel Verdonk, Senior Director, Astex Therapeutics, Cambridge, UK
Views: 447 BioSolveITTutorials
Drug Discovery, Biotech, and AI with Alex Zhavoronkov, CEO, Insilico Medicine (CXOTalk #327)
 
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Artificial intelligence offers the promise of better health, faster drug discovery and testing, to create improved medical outcomes for patients. We talk with a world expert on using AI in life sciences to discover and develop drugs faster and less expensively. AI and machine learning help select therapy candidates and assist with clinical trials. Read the complete transcript: https://www.cxotalk.com/episode/ai-medicine-life-sciences-drug-discovery Dr. Alex Zhavoronkov is the founder and CEO of Insilico Medicine, a leader in the next-generation artificial intelligence for drug discovery, biomarker development, and aging research. Prior to Insilico, he worked in senior roles at ATI Technologies, NeuroG Neuroinformatics, the Biogerontology Research Foundation and YLabs.AI. Since 2012 he published over 130 peer-reviewed research papers and 2 books. For six years in a row, he has organized the annual Aging Research for Drug Discovery and Artificial Intelligence for Healthcare forums at Basel Life/EMBO in Basel. Alex is an adjunct professor at the Buck Institute for Research on Aging.
Views: 10717 CXOTALK
XLDB2011 Drug Discovery in the Era of Big Data
 
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Speaker: Gregory McAllister (Novartis) XLDB 2011 http://www-conf.slac.stanford.edu/xldb2011 Copyright 2011 Stanford University This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. http://creativecommons.org/licenses/by-nc-nd/3.0/
Views: 241 XLDBConf
Work as a data scientist at Uppsala Monitoring Centre
 
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Uppsala Monitoring Centre is looking to recruit additional data scientists to our Research team. Are you curious and driven? Do you have a passion for developing and deploying machine learning and pattern discovery methods? Are you eager to engage in real-world data analysis to support wise therapeutic decisions in the use of medicines? Contact us on [email protected] Find out more on our LinkedIn page.... https://www.linkedin.com/company/uppsala-monitoring-centre/
Introduction to Medicines Discovery Catapult
 
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Medicines Discovery Catapult CEO, Chris Molloy, tells us about the Catapult and how it hopes to put patients at the heart of drug discovery.
Benefit risk assessment in pharmacovigilance
 
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Speaker: Ola Caster (2017) This two part talk covers the concepts, principles and methods in benefit-risk assessment and why it is an important area. Dr. Ola Caster has worked at UMC since 2007. His primary focus is research on quantitative methods for benefit-risk assessment. Another area of interest is exploratory analysis of VigiBase and other post-marketing data sources, for example to support the early detection of potential drug-drug interactions and drug dependence issues. Ola is also involved in some signal detection and analysis work. Handouts available here: https://bit.ly/2CJ3Byd Follow UMC: Twitter: http://www.twitter.com/UMCGlobalSafety Facebook: http://www.facebook.com/UppsalaMonitoringCentre
SIS Int Research, Data Mining ROI, Consumer Sentiment Towards Marketing (RBDR--1/27/12)
 
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Sponsored by: SSI http://www.surveysampling.com/forms/RBR-banner Today: 1) SIS' new report says data mining will be improving ROI. 2) The Notre Dame & Socratic Technologies' Index of Consumer Sentiment Towards Marketing is positive for the first time in 25 years.
Is Open Source Drug Discovery Practical? (3/4)
 
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"Is Open Source Drug Discovery Practical?" WHO/TDR HQ, Geneva, September 19th, 2013 Summary article: http://www.who.int/tdr/news/2013/odd/en/ Video 3 of 4 - see below for details Discussion session following presentations. Chair: Mat Todd, OSM (@mattoddchem). Chas Bountra (SGC): We know how to generate clinical candidates. The issue is: generating better validated targets. Do the same arguments apply across all diseases? Charlie Mowbray (DNDi): Phenotypic drug discovery vs rational (single-target based) drug discovery. Phenotypic good for infectious, not necessarily for human disease. Rob Don (DNDi): Infectious disease treatments have little economic value. Need more sustainable mechanisms for encouraging pharma to get involved in such research. Mat Todd: analogy of pro bono from law, applied to pharma, could be powerful. Lluis Ballell (GSK): Other pharma have not engaged strongly in open data/research. Public institutions have to realize they have to fund pharma for it to take part in more open schemes. Q from Bountra: Why can't we fund drug discovery in the public sector, particularly when you have a molecule that has worked in patients? Piero Olliaro (WHO/TDR): How can we optimize drug discovery/development through sharing, and create an ecosystem that encourages sharing? Need to reduce duplication in industry and academia. Mat Todd: Need now to try out several different models to see what works. Katie Athersuch (MSF): Aim is to get access to patients. The open, derisking is exciting, but the reward is privatized. Mat Todd: but industry is good at the later stages. Katie: How can we get rid of monopoly prices? Bountra (SGC): academia and industry have different strengths. Katie: the problem is still expense of patient access. Colombani (MMV): access is a core idea behind the osdd project in India that has not so far been tested. But also that it does not always happen that proprietary rights are given over to the commercial sector. Licensing is tailored to a particular project. Rob Don (DNDi): If you de-risk research with public funds (i.e. a product without the burden of the research costs) you will always find a pharma company willing to license something as a generic. Does not have to go back to being a proprietary compound. Example from DNDi: Coarsucam. Licenced to pharma as a generic. Olliaro: This model works for some diseases, but maybe not all. One needs to have a downstream funding model for this to work properly, such as the Global Fund. Bombelles (WIPO): Avoid the word "give" - tends not to be relevant - there are business negotiations. Current best solution appears to be that different institutions play their different roles, to their strengths. Private sector good at accepting risk - there are lots of expensive failures in drug discovery and the private sector has been good at accepting that failure rate. They have been good at financing failure. Who else can do that? MSF: The state does a lot of risk taking (mention of The Entrepreneurial State (Mazzucato) and recent Lancet paper indicating funding is: 60% industry, 30% public, 10% philanthropy). Very large investment by e.g. NIH where there is a lot of failure by the state. This makes private investment much less risky. Bountra: Agreed. Pharma says early discovery is too high risk. In Alzheimer's there have been many Phase 3 studies that have all failed. No company can afford to do the requisite studies. Again, the de-risking is the point where you have shown efficacy in patients. Lluis Ballell (GSK): Public sector research is now being guided to a large extent by tech transfer offices that want financial reward, which stifles innovation. Rob Don: Agreed - academic tech transfer offices inhibiting collaboration with DNDi. Puneet Kishor (Creative Commons): Maybe WIPO is seen to be working because there is a focus on the success stories (a particular metric of success) but what about the cases where it's failing? Bountra: The pharma industry has delivered some great successes. But today: we are not delivering drugs we need quickly enough, and no group can do it on their own. The solution: forget about IP early on. Bombelles (WIPO): Different sectors are good/bad at assuming risk of failure at different stages of drug discovery/development. In the US this has existed as cooperation between the NIH and industry. Nowadays innovation and access are strongly linked. Peter Murray-Rust (Open Knowledge Foundation, Cambridge Uni): Important to raise the pre competitive level. Possible impact of things like open hardware. After this meeting: need to define "open".
Views: 103 OSDDMalaria
Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks
 
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Authors: Shahar Harel (Technion); Kira Radinsky (Technion – Israel Institute of Technology) Abstract: Designing a new drug is a lengthy and expensive process. As the space of potential molecules is very large (10^23-10^60), a common technique during drug discovery is to start from a molecule which already has some of the desired properties. An interdisciplinary team of scientists generates hypothesis about the required changes to the prototype. In this work, we develop an algorithmic unsupervised-approach that automatically generates potential drug molecules given a prototype drug. We show that the molecules generated by the system are valid molecules and significantly different from the prototype drug. Out of the compounds generated by the system, we identified 35 FDA-approved drugs. As an example, our system generated Isoniazid - one of the main drugs for Tuberculosis. The system is currently being deployed for use in collaboration with pharmaceutical companies to further analyze the additional generated molecules. More on http://www.kdd.org/kdd2018/
Views: 1209 KDD2018 video
An Integrative Systems Biology Approach for Prioritizing Biomarkers and Drug Targets in Diseases
 
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Systems biology or pathway-based data analysis approaches allow the identification of networks of biological entities that may collectively define mechanisms and phenotypes, especially as they relate to disease. Herein, we applied an integrative systems biology workflow to hypothesize clinically relevant biomarkers and drugs targets for Alzheimer’s disease.1 Our workflow included several in silico approaches that integrate the prioritization of disease gene signatures, the analysis of disease-gene pathways and networks, and the ranking of putative drug targets based on their novelty scores (i.e., evaluating complete novelty, condition novelty or evidence of early development). We foresee this workflow as a universal tool for the prioritization of drug targets and biomarkers in complex diseases including, cancer, diabetes and many neurodiseases. In this session, we will be using MetaCore to compare and analyze the differentially expressed genes (DEGs) in 6 brain regions of Alzheimer’s disease patients calculated from the gene expression profiles reported in Gene Expression Omnibus (GEO) dataset GSE5281. Next, we will apply Causal Reasoning in MetaCore Key Pathway Advisor (MetaCore KPA) to identify upstream regulatory hubs that could be prioritized as drug targets and/or biomarkers. The gene and protein hits identified from the upstream key hub predictions and downstream enrichment analyses will be integrated and analyzed using the network building tools available in MetaCore to understand the underlying mechanisms. Finally, the prioritized hypotheses will be evaluated and putative drug targets will be ranked based on their novelty scores, using the Drug Research Advisor-Target Druggability (DRA-TD).2 At the end of this session we will be able to answer the following key questions: • What pathways and process networks are potentially disrupted in Alzheimer’s disease? • What upstream key regulatory hubs are potentially activated or inhibited in Alzheimer’s disease? • How to integrate results from upstream and downstream analyses to generate higher confidence, clinically-relevant hypotheses about drug targets and biomarkers? • How to evaluate the resulting hypotheses and score putative drug targets? References: 1) Hajjo, R & Willis, C. Systems biology approaches to omics data analysis in complex diseases. 253rd Am Chem Soc (ACS) Natl Meet (April 2-6, San Francisco) 2017, Abst BIOT 461. 2) Drug research Advisor, https://projectne.thomsonreuters.com/dra/, 2017.
Human genome’s frontier may hold keys to new drugs
 
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Human genome’s frontier may hold keys to new drugs The development of new drugs currently focuses on just 60 percent of potential drug targets, a new study indicates. The study, which builds on extensive data analysis conducted using super computers—a technique called data mining—has examined huge amounts of literature within the health and medical sciences and other evidence sources in order to identify both the most and least studied proteins for drug targets. Of the 20,000 proteins the researchers included in the study, they conclude that around 8,000 of these have not been mapped and studied by researchers or pharmaceutical companies. The study is believed to be the first to provide a comprehensive and useful picture of all the proteins that can be used to develop new drugs... Source: University of Copenhagen
Summit Corporation's Pye on positive data for DMD treatment
 
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Summit Corporation (LON:SUMM), a UK-based drug discovery and development company, has just reported some positive results from stage 1 trials of its new treatment for Duchenne Muscular Dystrophy. Director Dr Richard Pye tells Proactive Investors that although this is not a cure, it could help prevent muscle deterioration in this 'orphan' medical condition and prolong the life expectancy of young patients.
Computing cures: discovery through the lens of a computational microscope
 
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Computing cures: discovery through the lens of a computational microscope Air date: Wednesday, October 25, 2017, 3:00:00 PM Category: WALS - Wednesday Afternoon Lectures Runtime: 01:03:05 Description: NIH Director's Wednesday Afternoon Lecture Series Annual DeWitt Stetten Jr. Lecture Dr. Amaro’s scientific interests lie at the intersection of computer-aided drug discovery and biophysical simulation methods. She has a long-standing interest in incorporating structural and dynamical information derived from all-atom molecular dynamics simulations in drug discovery programs, and has worked in a variety of disease areas, including infectious diseases and cancer. Her lab’s work on p53 revealed a novel druggable pocket that clarified the mechanism of action for a compound in clinical trials. This work served as the basis for the formation of a start-up company related to the development of p53 reactivation drugs, Actavalon, Inc. Dr. Amaro is a co-founder, on the scientific advisory board, and an equity shareholder in Actavalon, Inc. Her scientific vision revolves around the continued development of molecular dynamics simulations in drug discovery programs, particularly in expanding the range and complexity of molecular constituents represented in such simulations, and novel multiscale methods for elucidating their time dependent dynamics. She is the director of the NIH P41 National Biomedical Computation Resource and a co-director of the NIH U01 Drug Design Data Resource. About the Annual DeWitt Stetten Jr. Lecture: Established by NIGMS in 1982 and presented annually in honor of Dr. Stetten, the third NIGMS director, this annual lecture is part of the Wednesday Afternoon Lecture Series. For more information go to https://oir.nih.gov/wals/2017-2018/computing-cures-discovery-through-lens-computational-microscope Author: Rommie E. Amaro, Ph.D., Professor and Shuler Scholar, UC San Diego Permanent link: https://videocast.nih.gov/launch.asp?23549
Views: 402 nihvcast
Open Pharma blockchain and crypto solutions for the medical industry
 
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Meet Jim Nasr with Open Pharma, hear all about they’re Blockchain solutions addressing Supply Chain solutions, drug discovery, disease outbreaks, vaccines, medical history and so much more! #Win $100 To WIN you 🛑🛑🛑MUST🛑🛑🛑 👍LIKE👍✅SUBSCRIBE✅🗣COMMENT🗣📫PUBLIC ETHEREUM ADDRESS📫 🛑ALWAYS DO YOUR OWN RESEARCH AND ALWAYS SPEAK TO A FINANCIAL ADVISOR BEFORE EVER INVESTING🛑 💰WIN $100$ Simply, watch, subscribe, like and add the keyword in the comment field along with your public ETH address. ☝️YOU MUST respond with your PUBLIC ETHEREUM address and keyword in the comment field to win. 💪RELAX Peeps, these are just my opinions and anything contained within the video is for information or entertainment purposes only. Robert Beadles is the co-founder of Monarch Token. This is for educational purposes and not financial advice. Watch at your own risk and always seek professional financial and legal advice before investing in anything. 🛑————————————————————————————————————————————————————🛑 THIS IS ONLY MY OPINION, ACTUAL FACTS AND OPINIONS MAY VARY. INVEST AND LISTEN AT YOUR OWN RISK. 🛑————————————————————————————————————————————————————🛑 🙏In the comments let me know how you liked the video and if you want me to do a video on anything specific. Share the video with your friends that are crypto curious and be safe, have fun and GOD Bless! 🔗Links https://openpharma.blog https://amazingcrypto.com https://www.synchrogenix.com Crypto Beadles Telegram Group CryptoBeadlesGroup https://t.me/joinchat/HwVKolJJQjqy9FrPycPX4Q Our TGE https://monarchtoken.io Crypto Beadles: https://www.cryptobeadles.com Youtube Personalities: Cryptosomniac-https://www.youtube.com/channel/UCRQkQ8YlIY2LlTWGjdo1Opw Alt Coin Buzz- https://www.youtube.com/channel/UCGyqEtcGQQtXyUwvcy7Gmyg Crypto Love https://www.youtube.com/channel/UCu7Sre5A1NMV8J3s2FhluCw BitBoy Crypto: https://www.youtube.com/channel/UCjemQfjaXAzA-95RKoy9n_g Crypto Disruption: https://www.youtube.com/channel/UCJxYycmlCSHcLaY5h7u5kmg I Love Crypto: https://www.youtube.com/channel/UCiMgF08KQ4z-Gnu8o2BLOxA Miggity Miner: https://www.youtube.com/channel/UCDjgFHwnO1_YJk_UBTb3rqg Keith Wareing: https://www.youtube.com/user/optionxe Crypto Crow https://www.youtube.com/channel/UCwsRWmIL5XKqFtdytBfeX0g Crypto Zombie https://www.youtube.com/channel/UCiUnrCUGCJTCC7KjuW493Ww My Social Media: Linkedin-https://www.linkedin.com/in/robertbeadles Twitter-https://twitter.com/RobertBeadles Instagram-https://www.instagram.com/robertbeadles Facebook-https://www.facebook.com/robert.beadles GAB- https://gab.ai/RobertBeadles Facebook-https://www.facebook.com/cryptobeadles Steemit https://steemit.com/@cryptobeadles News https://www.AmazingCrypto.com https://bit.tube/CryptoBeadles *Some of these links may be affiliate links, meaning if you click and purchase something, I may receive a small commission at no additional cost to you. You may also receive a free bonus of coins etc by using the link as well. I only recommend companies and products I personally use, and any commissions help to pay for content creation. ** This is not financial advice and these are simply my own opinions, as such, this should not be treated as explicit financial, trading or otherwise investment advice. This is not explicit advice to buy these cryptos, do you own research.** ***Disclaimer: Statements on this site do not represent the views or policies of anyone other than myself. The information on this site is provided for discussion purposes only, and are not investing recommendations. Under no circumstances does this information represent a recommendation to buy or sell securities. ****ALWAYS check with professional tax service providers and legal professionals before you buy, sell or trade cryptos! Thanks for watching, God Bless and have an awesome one! #hedera #swirlds #open pharma
Views: 20195 Crypto Beadles
AI for Resumes
 
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Recruiting is a 200 billion dollar industry thats all about judging potential job candidates and seeing if they're a good fit for a position at a company. Recruiters receive thousands of resumes and are responsible for analyzing all of them. Theres essentially a massive amount of data that these humans have to parse through and find the best ones. This is easily a problem machine learning can solve, we'll build an app that can classify resumes into 27 different job categories using natural language processing via a convolutional neural network. I'll explain how in this video. Also its midterm time, so see the link below for the midterm assignment. Code for this video (with midterm): https://github.com/llSourcell/AI_for_Resumes Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More learning resources: https://medium.com/the-mission/how-i-turned-my-resume-into-a-bot-and-how-you-can-too-f03847352baa https://www.textkernel.com/challenges-behind-parsing-matching-cvs-jobs/ https://www.quora.com/How-do-I-develop-a-resume-parser-using-NLP-Natural-Language-Processing?utm_medium=organic&utm_source=google_rich_qa&utm_campaign=google_rich_qa https://dzone.com/articles/cv-r-cvs-retrieval-system-based-on-job-description https://www.slideshare.net/zainulsayed39/218-intelligent Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Sign up for the next course at The School of AI: https://www.theschool.ai And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 34936 Siraj Raval
Ucb – Data Is The New Drug   Case Solution & Analysis TheCaseSolution.com
 
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https://www.thecasesolutions.com This Case Is About Ucb – Data Is The New Drug Case Solution and Analysis Get Your Ucb – Data Is The New Drug Case Solution at TheCaseSolution.com https://www.thecasesolutions.com/ucb-data-is-the-new-drug-case-solution-81403
Lifesciences: Give your portfolio a booster shot
 
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There are over 150 companies listed on the ASX that fall under the Lifesciences banner. This includes medical device manufacturers, drug development companies and the emerging 'e-health' companies. However, the reality is that with the exception of a handful of names, like Cochlear and CSL Ltd, most of these stocks fly under the radar of most investors. Livewire hosted a discussion following the recent Bioshares Investment Summit to understand the opportunities available in this overlooked sector. Adam Allcock from Katana hosts Andy Gracey from Australian Ethical and Stuart Roberts from NDF Research. The panel discusses the rise of technology, what they look when choosing investments, managing risks and each panelist shares one of their top picks following the summit. So if your portfolio is in need of a booster shot why not tune in to this latest exclusive bought to you by Livewire.
Views: 3199 Livewire Markets
UCB’s core antibody discovery process
 
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UCB's core antibody discovery platform combines high-throughput B cell culture screening and the identification and isolation of single, antigen specific IgG-secreting B cells through a proprietary technique known as ‘fluorescent foci' method. Using state of the art automation to facilitate screening, extremely efficient interrogation of the natural antibody repertoire is made possible.
Views: 7922 UCB
O'Reilly Webcast: Deep Learning - The Biggest Data Science Breakthrough of the Decade
 
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*Recording From a Live Event Machine learning and AI have appeared on the front page of the New York Times three times in recent memory: 1) When a computer beat the world's #1 chess player 2) When Watson beat the world's best Jeopardy players 3) When deep learning algorithms won a chemo-informatics Kaggle competition. We all know about the first two... but what's that deep learning thing about? This happened in November of last year, and it represents a critical breakthrough in data science that every executive will need to know about and react to in the coming years. The NY Times said that these advances "hold implications not just for drug development, but for an array of applications, including marketing and law enforcement". In this webcast talk Jeremy Howard, Kaggle's president and chief scientist, will explain exactly what occurred, why it was front-page newsworthy for the New York Times, how it will impact business, and what you need to know to make these new algorithms work for you. About Jeremy Howard Jeremy Howard is the President and Chief Scientist at Kaggle. Previously, he founded FastMail (sold to Opera Software) and Optimal Decisions (sold to ChoicePoint - now called LexisNexis Risk Solutions). Prior to that he worked in management consulting, at McKinsey & Company and A.T. Kearney, but he is now nearly fully recovered. Jeremy's passion is applying algorithms to data. At FastMail he used algorithms to automate nearly every part of the business - as a result the company only needed a total of 3 full time staff, and got over a million signups. Optimal Decisions was a business entirely built to commercialise a new algorithm he designed for the optimal pricing of insurance. Jeremy competes regularly in data mining competitions, which he uses to test himself and stay on the leading edge of machine learning and predictive modelling technology. His competition performance history is available on his Kaggle profile page. If you have beaten Jeremy in a competition, he would appreciate it if you didn't rub it in too much. Produced by: Yasmina Greco
Views: 13339 O'Reilly
How Text Mining Tackles Key Challenges Facing Pharma, Biotech
 
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Dr. Jane Reed, head of life science strategy at Linguamatics, discusses how pharma and biotech companies use text analytics to reduce the time and cost of their clinical trials and get drugs to market faster. Founded in 2001 in Cambridge, UK, Linguamatics uses advanced Natural Language Processing (NLP) to read and understand both structured and unstructured data to quickly make connections between thousands of text-based sources for faster knowledge discovery and decision-making. See more at: https://businessvalueexchange.com/blog/2016/03/22/how-text-mining-tackles-key-challenges-facing-pharma-biotech-us/
Myths and Urban Legends in Drug Development Part II: Pre Clinical Myths
 
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Drs. Michelle Higgin and Lisa Lallos talk to hundreds of newly formed biotech companies every year and have the opportunity to hear their creative strategies for building value on extremely lean budgets. Many of those ideas are ground breaking, but some are based on misconceptions that lead to costly delays and missed investment opportunities. Knowing how to side step these landmines will help streamline the development path but also offer some new strategies. During the presentation, they share some truths, myths and legends spanning drug development–preclinical, CMC, clinical, regulatory and program management. This video series will help early biotech companies avoid these potential pitfalls while providing alternatives paths to IND and clinical studies.
Automated Drug Safety Processing
 
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Presentation at "SwissText 2016" 08.06.2016 in Winterthur. http://www.swisstext.org René Haltiner, Michel Plüss, Simon Felix, Jonas Schwammberger, Michael Kalt, Manfred Vogel - Automated Drug Safety Processing Abstract: We describe the architecture and choice of algorithms for an automated processing of Adverse Drug Reports (ADR). Pharmaceutical companies are legally obligated to monitor adverse drug reactions and report them to the national drug regulatory authorities. Due to the variety of data sources and formats these reports are mainly manually processed and mapped onto the standardized CIOMS form (Council for International Organizations of Medical Sciences). Key features of this form are recurring elements like health care professionals, drugs, diseases and patients. Our objective is the automatic processing of these documents which by regulation have to be written in English. In order to process free text we use OCR, named entity recognition and other NLP tools. Attributes like medical tests, findings and treatments are strongly interlinked and can be represented by an ontology. The processed data is validated by machine learning algorithms and finally exported to the CIOMS form.
Views: 602 Swiss Text
How a collaborative approach is key to faster drug development
 
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The centre of innovative medical discoveries. The Medicines Discovery Catapult is a new, national centre of applied R&D expertise to promote and support innovative, fast-to-patient drug discovery in the UK through collaborative projects across the community. MDC work with biopharma companies of all sizes, translational researchers, technology experts, patient groups and the contract research and finance sectors to help transform great ideas into commercial products and services for the wider health and wealth of the country. By developing and validating new ways of discovering new medicines, and promoting key talent and expertise across sectors, MDC can help the UK maintain its heritage position as a global leader in this key industry. Find out more with Adrian Dawkes of PharmaVentures, as he discusses the significance of having patients in the centre of drug discovery with Chris Molloy, CEO of MDC.
Sequence Bio Will Use AWS to Efficiently Store and Compute Data
 
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Sequence Bio uses AWS to host its data platform which will be used to analyze large amounts of data. Sequence Bio is a Newfoundland and Labrador biotechnology company that will use AWS to compute data to help identify patterns across disease and genetic groups to aid early stage drug discovery. Learn more: http://amzn.to/2rofAHf.
Views: 282 Amazon Web Services
Mereo BioPharma well financed & 'executing on its development programmes'
 
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Dr Denise Scots-Knight, chief executive of Mereo BioPharma Group Plc (LON:MPH), runs Proactive Investors through the background to the company and progress they're making with the four products within its existing portfolio. ''We founded the company in 2015 and the thesis then was that there are quite a lot of products sitting within pharmaceutical companies that for one reason or another, usually strategic, they're not developing .. and we could actually do a deal with the pharmaceutical companies to take those in and develop them''. ''It's three years now since we founded the company .... we've executed on our plan and we've delivered two very good sets of phase 2 data''. ''We've demonstrated that we can select these products and execute on development programmes so we've got some de-risking behind us but also we've got some great newsflow ahead of us so it's actually quite a good time for the company''.
Insights | Big data & cognitive technologies to transform pharmaceuticals
 
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Adrian Toland and Christelle Gendrin from the STFC Hartree Centre explain why they think big data analytics and cognitive technologies have the power to transform the pharmaceutical industry, from drug development through to manufacturing. The STFC Hartree Centre is a partner in the ADDoPT (Advanced Digital Design of Pharmaceutical Therapeutics) project, which aims to address key challenges for the UK pharmaceutical industry using high performance computing and big data analytics technologies. For a full list of partners and more information, please see: http://www.stfc.ac.uk/news/hartree-ce... Follow us on Twitter: @HartreeCentre Connect with us on LinkedIn: http://www.linkedin.com/company/stfc-... For more information on the STFC Hartree Centre: http://www.stfc.ac.uk/hartree
Big Pharma DOESN'T WANT You TO SEE! (Illuminati Exposed) 2018-2019
 
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Big Pharma DOESN'T WANT You TO SEE! (Illuminati Exposed 2018-2019 Share This Video & Subscribe To Stay Updated ! Follow Me On Twitter @Scarack Truther Music Prob. By Young Forever Beats https://www.youtube.com/channel/UCDpcgy02bcqxZMEaQIQw6pQ
Views: 10398 Scarack Truther

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