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Search results “Data mining drug development companies”
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: 5407 The Royal Institution
Big data aids drug development
 
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Pharmaceutical companies partner on big data project to inform drug development
Big Data in Medicine: Unearthing Unexpected Drug Side Effects
 
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Russ Altman, a Stanford professor of bioengineering, genetics and medicine (and computer science, by courtesy) is using data mining methods to discover new drug interactions and side effects. Altman spoke about his research at the first EngX, a mini-conference of leading ideas from Stanford Engineering.
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: 772 UCI Open
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: 235 XLDBConf
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: 516 Swiss Text
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: 1560 Intel Business
webinar recording: SeeSAR as a tool for more efficient drug pipelining
 
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Today's drug discovery pipelines are under a lot of pressure. Although in the past years (since 2010) the approval rate of the FDA has slightly increased, pharma companies are still searching for ways to make drug discovery more efficient, especially reducing attrition rates later on the clinic. Fail fast, fail cheap is still very well valid! Therefore, reducing potentially failing NMEs as early and as cheaply as possible has become of paramount importance in the drug discovery process. This includes, but is not limited to, the application of sophisticated software tools to avoid as many experiments as possible. However, there is a conundrum, as software tools used by chemists are mostly too complicated to use, and feature way more functionality than necessary. Enter SeeSAR, an easy to use but very sophisticated tool to design molecules in 3D right there, in the active site. SeeSAR will predict, which improvement is indeed an improvement, and which change will likely lead to failures. Now with inclusion of ADME/T properties (from the well-known StarDrop feature by Optibrium), the chemist gets an overview of the most important parameters of the drug discovery process: affinity, LLE, LE, MW, BBB, hERG, log D, log P, solubility etc. We will lead you through the design process with SeeSAR and show you how you too can make your drug discovery more efficient! http://www.biosolveit.de/SeeSAR
Views: 413 BioSolveITTutorials
10 Surprisingly High Paying Jobs
 
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We did the math on the grunt-to-grat ratio for you, with 10 jobs you wouldn't expect to be well-paying, and the details on what it takes to land the position, in this episode of The Infographics Show, 10 Surprisingly High Paying Jobs. ⭐SUBSCRIBE: http://bit.ly/2glTFyc ⭐ MILITARY PLAYLIST —► http://bit.ly/MilitaryComparisons WEBSITE (You can suggest a topic): http://theinfographicsshow.com SUPPORT US: Patreon.......► https://www.patreon.com/theinfographicsshow CHAT: DISCORD.....►https://discord.gg/sh5JwUw SOCIAL: Facebook...► https://facebook.com/TheInfographicsShow Instagram..►https://www.instagram.com/theinfographicsshow Twitter........► https://twitter.com/TheInfoShow Subreddit...► http://reddit.com/r/TheInfographicsShow -------------------------------------------------------------------------- Sources for this episode:
Views: 4635912 The Infographics Show
Jinling Sui (Flately Discovery): Building the Bioinformatic Platform for Drug Discovery
 
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Building a suitable bioinformatic platform for small molecule R&D with high throughput screening (HTS) is challenging for a small biotech startup with limited experiences and resource. At Flatley Discovery Lab, we started with desktop-based data process and library management applications in our initial chemical library build-up and HTS data processing. We have grown into an Oracle-based system, and implemented additional commercial packages on the platform. The system is designed and tailored to meet exactly the need of our biologists and chemists. With desktop-based component remains to be the front end for HTS raw data processing and data QC, advanced data analysis and visualization tools are introduced to support our SAR and lead development efforts. In only a few years since its inception, Flatley Discovery Lab has discovered several exciting CFTR modulators in the development pipeline toward a new and efficacious treatment for Cystic Fibrosis.
Views: 468 ChemAxon
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: 960 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: 157 Bob Perry
Transforming drug discovery – the pathway to innovation
 
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with Prof Chas Bountra & Dr Wen Hwa Lee Immense ingenuity and unprecedented levels of funding are available for drug discovery, yet pharmaceutical research and development is failing to produce the medicines society requires. New organisational models of drug discovery are clearly needed, and members of the Oxford Martin Programme on Affordable Medicines will contend that open science approaches represent a promising path forward. Join in on twitter with #oxmartintalks Oxford Martin School, University of Oxford www.oxfordmartin.ox.ac.uk
Unlocking Patient Data for Pharma & Life Sciences - Distributed: Health
 
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Life science companies and pharmaceutical manufacturers are tackling increasingly complex diseases. As this trend continues, the ability to exchange data with all parties about scientific research, clinical impact, regulatory compliance and commercialization will become mission critical. This session will explore how blockchain technology can redefine the way life science companies and pharmaceutical manufacturers share data. Nishan Kulatilaka - Associate Director in Applied Technology, Merck Fredric Santiago - Boehringer Ingelheim Pharmaceuticals Mark Treshock - Associate Partner, Life Sciences & Healthcare, IBM Dominique Hurley - VP, Innovation and Strategic Partnerships, HealthVerity
Views: 136 Distributed
Tonestra token introduction
 
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INTRODUCING ...Tonestra Tonestra is the healthcare and biometric blockchain project that is designed to transform information sharing and record keeping in healthcare industry Tonestra offers the first complete blockchain healthcare project where scientist and healthcare receivers can have their data securely stored for utilization by healthcare practitioners. Tonestra gives: 1. Healthcare data contributors absolute right to own their data and how they use it 2. Contributors financial gain and benefits by providing access to industry users, such as biotechnology research companies and medicinal drug developers for a payment 3. open access for The development of an ecosystem where all users continuously work for the development of the platform for the benefit of all The tonestra token(tnr) Tonestra token TNR will be the dynamic currency fuel payment unit which will power the system platform. The tokens will be attached to data storage and in transmitting information across the Tonestra network and ecosystem. Five million Tonestra TNR tokens have beeen created and their Token Standard is Ethereum ERC20 smart contract Be a part of this project by investing in the THE TONESTRA token. Tonestra token is listed and presently traded on the crex24 exchange( crex24.com) and will soon be listed on other major cryptocurrency exchangers for more info visit www.tonestra.com
Views: 6892 Tonestra TNR token
Reasons to study the Master in Data mining applied to the Medicine
 
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Discover the reasons to study the Master in Data Mining applied to the Medicine of the hand of his academic coordinators. It is a unique formative program for professionals of the Health that they want to interpret and to extract profit of the information of his patients with the help of the technology.
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: 4503 Perficient, Inc.
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.
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
Is Open Source Drug Discovery Practical? (2/4)
 
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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 2 of 4 - see below for details Rob Don (DNDi) presentation: DNDi aims to get drugs into the clinic. Much work is pre competitive, much of it is not open because of contractual obligations, but the aim is ultimately to provide the final medicine inexpensively. Whether we should aim for programmed obsolescence of PDPs, i.e. a different, more sustainable model. The ongoing challenge of meeting R&D costs. Concerns of osdd: i) incentives - open source drug discovery would mainly be outside the industrial sector, while academia sector is not typically focussed on proper drug discovery programs. Publications can be incentives to take part. Could modify the tenure process to acknowledge drug discovery programs. An Idea: Collective postgrad degrees on a drug discovery program. An incentive is to see there is a guaranteed path through drug development to the patient. The osdd process needs leadership, meaningful scientific questions, rapid turnaround and momentum. Lluis Ballell (GSK Tres Cantos) presentation: Work with PDP partners. Differences between open and traditional methods of drug discovery. Tres Cantos helps through provision of infrastructure and people, managed by the OpenLab Foundation, funding through EU and Wellcome Trust. Also contribute to data sharing, e.g. major antimalarial data set, through ChEMBL. Works with WIPO to try to reconcile openness with the IP framework that binds them. A search for balance: Tres Cantos patents operate on the principle that anything arising from research on NTDs should be royalty free. Comment from Mat Todd that the provision of the open data helped secure government funding for the OSM project because of the quality of the starting points, and that the personal input from scientists in GSK (which are personal, so expensive) have helped. Chas Bountra (Structural Genomics Consortium, University of Oxford) presentation: Problem of massive duplication of effort in drug discovery research, e.g. in kinase research. SGC works on novel human proteins only. All outputs are published immediately and given away. Funded by pharma: 8-9 companies each contributing $8M. Generates much collaborative work. The transparency generates trust. Disclosure of data does not prevent traditional journal publication. Working on bromodomain inhibitors, e.g. with Jay Bradner. SGC released the structure of the inhibitor, stimulating a large amount of activity, including industrial activity and VC funding. Drug discovery as a lottery because of high attrition rates, arising from poor target validation prior to the clinic. The secrecy is wasting financial and human resources. Idea is to create a new PPP to generate clinical candidates that will be taken to patients in the absence of IP, as a "knowledge creation endeavor". Proof of clinical mechanism (POCM). By avoiding IP one can access the best academics quickly. Targets selected by industry. Involvement of patient groups. Industry later develops proprietary assets. This open approach as the only way to really solve big issues such as Alzheimer's. beta-amyloid. 29 years of research on this one target, $30Bn spent, no candidates. Tom Bombelles (WIPO) presentation: The role of WIPO to support IP systems that deliver societal benefits. IP not an end in itself, but rather as a tool to support innovation. 20th Century IP system has developed powerful and useful products but is perhaps not delivering in the way it used to. Not perfect, e.g. in not delivering benefits to all sectors of society equally. In the area of NTDs: WIPO:Research as an open innovation (not open source) initiative. Database - members can advertise the availability of IP assets including services such as screening, to those engaging in research in this area. Allows for transfer of IP assets in order to try to stimulate activity. Patents are intended to make information public so that others can use the information. In return, the inventor gets exclusive rights. The IP system makes the transfer of proprietary knowledge possible. But should exist within a multitude of models. Patents are one part of IP, but there are more components. Example of facilitation: UCSF working with Merck on schistosomiasis through a WIPO agreement to help navigate the Valley of Death. WIPO Research has 33 collaborations at the time of this meeting. New knowledge in a WIPO Research project: patenting is encouraged, but royalty-free licensing is required.
Views: 222 OSDDMalaria
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
GrossTo Net Predictive Modeling - Pharmaceutical
 
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Gross-To-Net (GTN) Predictive Modeling is a web-based forecasting application developed by Inferential Designs for small to mid-size pharmaceutical companies that market their products into government and/or commercial channels with which they contract. In addition to user developed forecasts, the model features multiple pre-defined forecasting options using statistical and quantitative methods that provide guidance to the user with a single click of the mouse. All options render instant results and provide strong emphasis on those calculations most meaningful to a managed markets analysis.
Alexander Steudle (Certara): D360 - The Pharmaceutical Data Analytics and Scientific Platform
 
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Certara biosimulation (model based drug development and informatics) software portfolio is the most wide-ranging in the industry. Combining legacy brands including Pharsight, Simcyp, Tripos and Certara, these technologies are used across the drug development life cycle. Pharmacologists, toxicologists, biostatisticians, pre-clinical scientists, chemists, geneticists, and other scientists use Certara software to inform key safety and efficacy decisions, including target identification and optimization, dosing, trial design, target validation, compound triaging, comparison with competitor compounds, and mechanistic drug performance. Certara’s D360 R&D data platform solves data challenges faced by scientists and IT staff throughout biopharmaceutical organizations by integrating and leveraging the large amount of diverse scientific data generated by sponsor companies and their outsourced partners to optimize crucial decisions.
Views: 266 ChemAxon
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.
Insilico Medicine Stands Out at 2015 GTC Emerging Companies Summit
 
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While deep learning was the topic of many conversation at last week’s 2015 GPU technology conference (GTC), the CEO of one start-up spoke passionately about using fast GPUs to fight cancer and push the human lifespan well beyond the generally-agreed ceiling of 120 years. Insilico Medicine, a Baltimore MD bioinformatics company, is one of 12 companies recognized as the hottest GPU-powered startups in the US. While other startups in the GTC Emerging Companies Summit have business models focused on self-driving cars, gaming, animation, or 3D imaging, Insilico Medicine is the only start-up using GPU technology to fight age-related diseases such as cancer or Alzheimer’s. "We live in a very exciting time when information technology is converging with biotechnology", said Alex Zhavoronkov, PhD, the CEO of Insilico Medicine. "We developed a bioinformatics platform and strategy to tackle humanity's greatest challenge - aging and age-related diseases and we're honored to be recognized by NVIDIA as a promising GPU-powered startup." Until the early 1990s, biology and related fields required very little experience with computers. Now, however, the vast amount of DNA and molecular data generated from the Human Genome Project and labs around the world have made high-powered computer analysis necessary. Because GPU-accelerated computing is the fastest way to analyze data, Insilico Medicine has received a great deal of attention in recent weeks from pharmaceutical companies and investors. Medical researchers agree GPU computing and computer science will play a key role in finding cures for age-related diseases and extending the human lifespan. “Aging is humanity's most complex and daunting medical problem and it has defeated us for too long, said Dr. Aubrey de Grey, a gerontology expert and the Chief Science Officer of the SENS Research Foundation. “We will overcome it with a trifecta of new technologies, new insights - and new data analysis tools, an area at which Insilico Medicine promises to be at the forefront.“ The 11 GPU-powered companies joining Insilico Medicine include Artomax, Ersatz Labs, FluiDyna, GeekSys, Intempora, NE Scientific, Pythia Systems, QM Scientific, Redshift Rendering Technologies, Replica Labs, and SYSTAP. "To analyze how cancer cells respond to certain treatments, you must run drug scoring algorithms on high-end big data computer systems." said Zhavoronkov. “and no one can manage this work more effectively than our team of experts in both computer science and bioscience.” About Insilico Medicine Insilico Medicine, Inc. is a Baltimore-based company utilizing advances in genomics and big data analysis for in silico drug discovery and drug repurposing for aging and age-related diseases. The company utilizes the GeroScope™, OncoFinder™ , Pathway Cloud Intelligence™ and PharmAtlas™ packages for aging and cancer research, pursues internal drug discovery programs, and provides services to pharmaceutical companies. If you have any questions about the science of aging, bioinformatics, or Insilico Medicine's plan to use GPUs in anti-aging research and drug discovery, please call (443) 451- 7212 or visit: www.insilicomedicine.com.
Biotech Showcase 2018: Drivers of innovation: Evolving models in rare disease drug development
 
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The growing pressure on drug companies to bring new rare disease therapies quickly and cost-effectively is giving rise to a variety of emerging business models. While there are a number of fully integrated pharmaceutical companies in the space, others are focusing on licensing a platform technology, acting more like an incubator, or driving an array of projects through a collaboration model. What do companies gain or lose through such strategies? How do they accelerate the development of new therapies? How do investors view these models?
Views: 230 EBDGROUPChannel
Stanford Webinar - Using Electronic Health Records for Better Care
 
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In the era of Electronic Health Records, it’s possible to examine the decision outcomes made by doctors and identify patterns of care by generating evidence from the collective experience of patients. In this webinar, Stanford Assistant Professor Nigam Shah will show you methods that transform unstructured patient notes into a de-identified, temporally ordered, patient-feature matrix. Four use-cases will be examined, which use the resulting de-identified data matrix to illustrate the learning of practice-based evidence from unstructured data in electronic medical records.. This webinar will teach you the practical value of: •Monitoring for adverse drug events •Identifying drug-drug interactions •Profiling the safety of off-label drug usage •Generating practice-based evidence for difficult-to-test clinical hypotheses. Presented by the Stanford Center for Professional Development (http://scpd.stanford.edu)
Views: 9415 stanfordonline
Prescription  Data Mining  and  You
 
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Created on March 27, 2009 using FlipShare.
Views: 1239 HCFAMA
FDA Involvement in Off-Label Drug Use: Debate between Richard Epstein and Ryan Abbott
 
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On January 13, 2014 the Southwestern Law School Federalist Society student chapter hosted a debate about the FDA's role in regulating off-label drug use featuring Professor Richard Epstein, the Laurence A. Tisch Professor of Law at NYU and the Kirsten Bedford Senior Fellow at the Hoover Institution, and Ryan Abbott, Associate Professor of Law at Southwestern Law School and Visiting Assistant Professor of Medicine at the David Geffen School of Medicine at UCLA. Before a drug can be sold legally in the United States, the Food and Drug Administration (FDA) must approve it as safe and effective for a particular indication or use — the use then appears on the drug's label. Federal law, however, allows doctors to prescribe drugs that the FDA has approved for one indication for any other indication, even though the FDA never evaluated the safety or efficacy of the drug for that use. Off-label prescribing is an integral part of modern-day medicine. Patients may benefit when they receive drugs or devices in contexts not approved by the FDA. In fact, in some instances an off-label use may be the standard of care for a particular health problem. However, off-label prescribing can also harm patients, especially when an off-label use lacks a solid evidentiary basis. For this reason, the FDA forbids drug companies from promoting their own products for off-label use, except for certain activities such as disseminating research literature and sponsoring educational programs. In recent years, civil and criminal actions against drug companies for illegal promotion for off-label use have proliferated, leading to many large settlements. For example, in July 2012, GlaxoSmithKline pled guilty and paid $3 billion to resolve criminal and civil liability arising from the company's unlawful prescription drug promotion, failure to report safety data, and false price reporting practices. As a result of this recent litigation, many have questioned the FDA's current role in regulation of off-label use and whether more or less intervention is needed. This debate sought to address these very issues. Both Professors have written about FDA regulations. For example, Professor Epstein in his book, Overdose: How Excessive Government Regulation Stifles Pharmaceutical Innovation, and in an article in the Minnesota Law Review, "Against Permititis: Why Voluntary Organizations Should Regulate the Use of Cancer Drugs." Professor Abbott has written about FDA regulations in the Iowa Law Review, Big Data and Pharmacovigilance: Using Health Information Exchanges to Revolutionize Drug Safety, and he has an article forthcoming in the Duke Law Journal with Ian Ayres at Yale Law School on Mechanisms for Regulating Off-Label Drug Use.
Views: 2672 Ryan Abbott
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: 1028 KDD2018 video
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: 5077 PAREXEL International
Interviewing with McKinsey: Case study interview
 
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Learn what to expect during the case study interview. Hear what some recent hires did - and did not - do to prepare.
Views: 545303 McKinsey & Company
Vineti mission: Software and Cure for Cancer
 
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Backed by GE Ventures, Mayo Clinic Ventures, and DFJ, the Vineti team brings together a hybrid of experts in user-centric, regulated industry software and analytics development, as well as commercial therapeutics development and manufacturing. Vineti was founded to solve the key challenges patients, medical providers, pharmaceutical companies and regulators face in the delivery and commercialization of personalized medicine. Recently, U.S. Food and Drug Administration (FDA) approved cutting edge cancer therapy based on cell and gene Therapy. This talk will present Vineti mission and software product offering focused around Cell and Gene therapy. Vineti has already invested in building an Armenian software product development office. This talk will also cover role of Vineti Armenia. About the Speaker Razmik is a leading business software executive. He is currently CTO and Cofounder for Vineti. He was former CTO for EMC Documentum product family, leading Enterprise Content Management platform and solutions for Fortune 5000. Razmik was one of the original designers of Documentum platform. He was a founding member of Documentum team; starting with handful of people, growing to become a leading force in Enterprise Content Management industry with IPO in 1996 and eventual acquisition by EMC in 2003 for $1.7 Billion. Razmik is author of several patents and coauthor of several technical books. He is also investor and advisors to multiple high tech companies.
Views: 356 CSE AUA
EFPIA Director General Richard Bergström on the Commitments for Clinical Trial Data Sharing
 
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On July 24, EFPIA and PhRMA formally launched their Joint Principles for Responsible Clinical Trial Data Sharing to Benefit Patients. This commitment to data sharing will enhance research and scientific knowledge, advance patient care and improve public health. Under the Principles, biopharmaceutical companies will dramatically increase the amount of information.
Views: 275 EFPIA
Medical Coding with MedDRA
 
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Learn about the best practices for medical coding using MedDRA, a global dictionary used by companies for regulatory activities. -- MedDRA is a global dictionary used by companies for regulatory activities. The dictionary, complete with clinically validated terminology, is used to classify adverse event information related to the use of drugs, devices, and other therapies. Coding the data to a standard set of MedDRA terms enables health authorities and the life sciences industry to more readily exchange and analyze data. MedDRA is considered the international standard for adverse event classification. However, while the data volume and standardization capabilities offered by the dictionary can provide significant benefits, the multi-axial design and data specificity can introduce considerable challenges that could lead to the inaccurate classification of data. Join BioPharm Systems' Dr. Rodney Lemery, vice president of safety and pharmacovigilance, and Caroline Halsey, director of project management, EMEA, for this free one-hour webinar that will explore the best practices for medical coding using MedDRA. To view this webinar in its entirety, please visit: http://www.biopharm.com or https://cc.readytalk.com/r/fqfuit5ql7qh. Twitter: http://www.twitter.com/BioPharmSystems Facebook: http://www.facebook.com/BioPharmSystems LinkedIn: http://www.linkedin.com/companies/biopharm-systems-inc Google+: https://plus.google.com/104105608638786200757
Views: 14908 BioPharmSystems
Linguamatics How Agios Uses Text Mining Webinar Preview
 
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Agios continues to advance drug programs in the clinic. For Agios, text mining plays a key role in these advances, providing critical information for decision-making at different stages in the drug discovery process, from bench to bedside. All pharmaceutical companies need an efficient and compliant system to manage safety data in a responsible way. To accomplish this, Agios is in the process of implementing an Adverse Event Reporting System (AERS). The AERS will support the collection of appropriate data as per FDA/global regulations. Natural language processing (NLP) is being used in multiple places in the workflow at Agios: to mine AE reports, extract case-data from call center records, and assist with initial coding of reported events and WHO drugs. Agios are using data generated by Linguamatics NLP text mining to help understand the progression of AEs in ongoing clinical trials.
Views: 47 BioITWorld
FDA Use of Big Data in Modeling and Simulations
 
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According to Jeffry Florian, PhD, Review Division of Pharmacometrics, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration (FDA), the use of big data in modeling and simulations is dependent upon the stage of drug development: pre-clinical, clinical and post-marketing. Dr. Florian discusses the types of questions posed by the FDA during each stage of drug development. For more information, visit http://www.npcnow.org/issues/comparative-effectiveness-research
Views: 470 npcnow
Best and top market research and analysis company services in India
 
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Visit @ http://noevos.com.Noevos Market Research & Analysis is a knowledge process insourcing company offering business and knowledge services to Life Science Research Organisations, CRO’s, Pharma, Database Management and Marketing companies.
Intelligent Open Data Models for Health Analysis - NHS and Pharmaceuticals
 
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Intelligent Open Data Models for Health Analytics in the NHS and Big Pharma. S&B MI discuss challenges of making the most of open data and how prepared data models can enable the process. More information: [email protected]
Views: 201 Esri
Big Data and Marketing Conference, Jerome Couturier (3H Partners)
 
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Jerome Couturier, chairman of 3H Partners challenged delegates to adopt a Big Data approach throughout the business process and in particular in the decision-making phase. He presented various ways that companies can gain value and build a competitive advantage by the use of big data in decision-making, demonstrating the example of a large pharmaceutical and of a leading sports equipment manufacturer.
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
Views: 32024 Siraj Raval
On Diversity, Complexity, and Regularization in Ensemble Models - Part 1
 
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SF Bay ACM Data Mining Event: On Diversity, Complexity, and Regularization in Ensemble Models http://www.sfbayacm.org/?p=2164 See also Giovanni's book on the same subject: from Amazon ($27.76) http://www.amazon.com/Ensemble-Methods-Data-Mining-Predictions/dp/1608452840/ref=sr_1_1?ie=UTF8&qid=1295920281&sr=8-1 from publisher, PDF for $20.00 http://www.morganclaypool.com/action/doSearch?target=article&searchText=Giovanni+Seni&filter=all&x=0&y=0 Abstract: The discovery of ensemble methods is one of the most influential developments in Data Mining and Machine Learning in the past decade. These methods combine multiple models into a single predictive system that is more accurate than even the best of its components. The use of ensemble methods can provide a critical boost to existing systems addressing the hardest of industrial challenges -- from investment timing to drug discovery, from fraud detection to recommendation systems -- where predictive accuracy is vital. This talk, based on a recently published book by the speaker, offers a concise introduction to this breakthrough topic. After a sketch of the major concerns in predictive learning, the talk will give an overview of regularization, a key concept driving the superior performance of modern ensemble algorithms. It then takes a shortcut into the heart of the popular tree-based ensemble creation strategies using recent developments from the frontiers of statistics, where research efforts are now focused to explain and harness the mysteries of ensembles. Biography: Giovanni Seni is a Senior Scientist with Elder Research, Inc. (ERI) and directs ERI's Western office. As an active data mining practitioner in Silicon Valley, he has over 15 years R&D experience in statistical pattern recognition, data mining, and human-computer interaction applications. He has been a member of the technical staff at large technology companies, and a contributor at smaller organizations. He holds five US patents and has published over twenty conference and journal articles. His book with John Elder, "Ensemble Methods in Data Mining -- Improving accuracy through combining predictions", was published in February 2010 by Morgan & Claypool. Giovanni is also an adjunct faculty at the Computer Engineering Department of Santa Clara University, where he teaches an Introduction to Pattern Recognition and Data Mining class.
Data Science in Healthcare: Price Prediction and Personalized Recommendations | Data Dialogs 2016
 
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Unlike for most services or products, the price for a medical procedure is frequently unknown to a US healthcare consumer until they receive the bill after the procedure has already been performed. This lack of transparency results in wide price variance and an inefficient market. This talk will cover how Castlight accurately predicts out-of-pocket pricing in the face of complex insurance plans, contractual restrictions, and sparse, messy data. The talk will also cover how Castlight uses machine learning to make personalized recommendations to lower healthcare costs, improve outcomes, and improve utilization of health benefits. https://datadialogs.ischool.berkeley.edu/2016/schedule/data-science-healthcare-price-prediction-and-personalized-recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Robert Stewart Engineering VP and Chief Architect Castlight Health Robert Stewart is an Engineering VP and the Chief Architect at Castlight Health. Castlight's health benefits platform empowers people to make the best choices for their health, and helps companies make the most of their health benefits. He led the engineering teams that built Castlight Action, a fully-automated platform for benefits professionals to leverage data and predictive analytics to connect employees to the right benefits and programs. Robert previously held senior development and management positions at Voxify, Avaya, and Lucent Bell Labs, and has a Bachelor's Degree in Physics and Philosophy from Rice University. On most weekends he can be found cycling the East Bay hills.
Chotard - 3rd International Definiens Symposium
 
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Next Generation Histopathology: Definiens Software Applications in Discovery Research in the Pharmaceutical Industry CAROLE CHOTARD Institut de Recherches Servier, Croissy sur Seine, France The challenges facing the Pharmaceutical Industry are to develop innovative and safer therapeutic drugs for unmet medical needs. Based on physiopathological hypotheses, we are aiming to improve the characterization and validation of therapeutic targets and develop new drugs in cellular and animal models more predictive of the pathology in human. In this regard, within the Servier Research Institute, the Division of Molecular Physiopathology and Pharmacology is involved at different stages in the selection and validation of promising targets as well as new biomarkers from basic research to preclinical and clinical development. Working closely with multiple Research Groups (Diabetology, Cardiology, Neurology and Oncology) we support their target validation and drug discovery programs with new technologies and molecular expertise (Molecular Genetics, Transcriptomics, Proteomics, RT-PCR, Bioinformatics, High Content Analysis and Molecular Histopathology). In particular, in addition to the classical molecular pathology techniques such as Immunohistochemistry, In Situ Hybridization, we are integrating Automated Image Analysis systems in order to respond to the multiple and complex demands of image analysis where multiparametric data sets can quickly become overwhelming. Automated Image Analysis offers advantages over traditional manual methods of analysis by a non-biased process capable of evaluating multiple parameters. Moreover, once an image-analysis methodology is validated it can be applied to a large number of digitalized images, facilitating the collection of large amounts of data for statistical analysis. This presentation will focus on some of the rule-sets we have developed with Definiens (Developer and Tissue Studio) in our different Research axes.
Views: 138 DefiniensLifeTV
Activist Investor’s Have ‘Big Say’ in Mining Company Operations – Bloomberg Intelligence
 
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KITCO NEWS – Activist investors overhauled many mining boards during recent bear markets, including HudBay Minerals Inc. in 2009. Thanks to billionaire Carl Icahn, they may be back in the spotlight in the mining sector. This week, two board members from Icahn’s investment firm were announced to have been appointed by Freeport-McMoRan Inc (NYSE:FCX). ‘The value of these [mining] stocks has been absolutely destroyed, so a lot of these activists are saying wait a minute, it makes sense to break up these companies, and take out excesses,’ said Ken Hoffman, Global Head of Metals & Mining Research at Bloomberg Intelligence. ‘It is the beginning of making some sanity,’ he said in an interview with Kitco News. Shares in commodities group Glencore rose strongly Friday after the company said it is slashing its zinc production by a third and cutting jobs in response to a sharp fall in the price of the metal. ‘You are starting to see more and more companies act responsibly, and they are responding by cutting and understanding that China has major issues. What these activists will do is go to the management that was saying, ‘China will be fine,’ year after year, and say, ‘you need to act responsibly.’ Subscribe to TheStreetTV on YouTube: http://t.st/TheStreetTV For more content from TheStreet visit: http://thestreet.com Check out all our videos: http://youtube.com/user/TheStreetTV Follow TheStreet on Twitter: http://twitter.com/thestreet Like TheStreet on Facebook: http://facebook.com/TheStreet Follow TheStreet on LinkedIn: http://linkedin.com/company/theStreet Follow TheStreet on Google+: http://plus.google.com/+TheStreet
International Conference on Transcriptomics
 
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Transcriptomics is a cross-disciplinary area mainly concerned with transcriptome analysis and RNA-Seq. It emphasizes on how the next generation sequencing is replacing microarrays as the choice method for expression profiling and quantify overall gene expression level. Transcriptomics research provides a profitable business opportunity to several pharmaceutical and biotech companies as these technologies have wide application areas such as drug discovery and research followed by clinical diagnosis of various disorders. With the arrival of advanced technological platforms, it creates a need for effective bioinformatics tools and services in order to manage and analyse huge amount of data released from these technologies. Transcriptomics-2015 mainly focusses on the universities, institutes and major societies along with companies which hold a big market in transcriptome technologies.
Views: 364 OMICS International
Rx-360 White Paper on Traceability Data Exchange and Architecture
 
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Rx-360 is a consortium being developed by volunteers from the Pharmaceutical and Biotech industry which includes their suppliers. The purpose is to enhance the security of the pharmaceutical supply chain and to assure the quality and authenticity of the products moving through the supply chain. The individuals developing this concept are working in the best interest of patients. We are a non-profit organization with the mission to create and monitor a global quality system that meets the expectations of industry and regulators that assures patient safety by guaranteeing product quality and authenticity throughout the supply chain.
Views: 56 Rx-360 Consortium
Why investors are considering ImmuPharma, ahead of phase III data release
 
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We reveal the full City investor briefing to provide further insight into ImmuPharma’s drug platform. We focus predominantly on Lupuzor’s phase III lupus trial and also introduce our cancer and peptide programmes. Hear from ImmuPharma's Tim McCarthy, Chairman, Robert Zimmer, Chief Scientific Officer and Dimitri Dimitriou, Chief Executive Officer
Views: 2226 ImmuPharma

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