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Genomics’ Proprietary Statistical Analysis Tools and Integrated Multi-Phenotype Database to be used to Support Research and Development at Vertex Pharmaceuticals

Posted in Computational Biology/Systems and Bioinformatics, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Pharmaceutical Discovery, Pharmaceutical Drug Discovery, Pharmaceutical R&D Informatics, Pharmacogenomics on January 4, 2016| Leave a Comment »

Genomics’ Proprietary Statistical Analysis Tools and Integrated Multi-Phenotype Database to be used to Support Research and Development at Vertex Pharmaceuticals

Reporter: Aviva Lev-Ari, PhD, RN

 

 

Press release

04 January 2016

Genomics and Vertex Collaborate to Identify Target Therapeutic Pathways

 

Genomics’ Proprietary Statistical Analysis Tools and Integrated Multi-Phenotype Database to be used to Support Research and Development at Vertex Pharmaceuticals

 

Oxford, UK, 04 January 2016: Genomics plc (“Genomics”), a leading analysis company developing algorithms, data resources, and software solutions to uncover the relationships between genetic variation and human disease, today announced that Vertex Pharmaceuticals will use Genomics’ integrated database and state-of-the-art analysis tools to inform its drug research and development.  These tools aim to provide confidence in the rationale for targeting Vertex’s pathways of interest for the treatment of certain diseases and to identify potential safety concerns and repositioning opportunities.

 

Genomics has developed a unique analytical platform for genome analysis and interpretation. The platform combines proprietary algorithms and software with the Company’s integrated genome-phenome database and analytical expertise to learn about human biology.  Genomics has several existing partnerships with large pharmaceutical companies, and in clinical genomics is a Platform Partner for Genomics England, the company undertaking the 100,000 Genomes Project in the UK.

 

John Colenutt, CEO, Genomics plc, said: “Pharmaceutical and biotech companies are increasingly using human genetic data in research to increase the chance of success in drug development.  We are excited that Vertex has chosen to use Genomics’ proprietary technology, integrated database and tools to support them in this aim.”

 

Paul de Bakker, Ph.D., Head of Computational Genomics for Vertex said: “Vertex is focused on advancing research programs where disease mechanisms are validated by human biology.  Our collaboration with Genomics is aimed at obtaining insights into the genetic underpinnings of specific targets and diseases to help predict which potential medicines may have success moving from discovery research toward patients.”

 

ENDS

 

Photo: John Colenutt, CEO, Genomics plc. For a high resolution image please contact lorna.cuddon@zymecommunications.com

 

For further information please contact:

 

Zyme Communications

Lorna Cuddon

Tel: +44 (0)7811996942

Email: lorna.cuddon@zymecommunications.com

 

About Genomics plc http://www.genomicsplc.com/

Genomics was founded by four leading Oxford academics, including Professor Peter Donnelly, Director of The Wellcome Trust Centre for Human Genetics, and Professor Gil McVean, Director of The Big Data Institute. The Company has developed a unique platform for genomic sequence data analysis and interpretation which combines world-leading expertise in statistical analysis and data mining with a unique integrated database linking genotypes and phenotypes. Genomics England, the company running the UK project to undertake whole genome sequencing of 100,000 patients in the National Health Service, has appointed Genomics plc as a Platform Partner and has also awarded the Company three SBRI grants. Genomics plc is also working with four major pharmaceutical companies to bring the benefits of genomic analysis to their drug development processes. The Company is supported by major investors, including IP Group, Invesco Perpetual, Woodford Investment Management and Lansdowne Partners.

SOURCE

From: Lorna Cuddon <lorna.cuddon@zymecommunications.com>

Reply-To: <lorna.cuddon@zymecommunications.com>

Date: Monday, January 4, 2016 at 4:11 AM

To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Subject: Genomics and Vertex Collaborate to Identify Target Therapeutic Pathways

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From Turing to Watson

Posted in Academic Publishing, Curation, HealthCare IT, Image Processing/Computing, Innovations, Personal Health Applications: Tech Innovations serves HealhCare, Pharmaceutical R&D Informatics, Population Health Management, tagged computer history, IBM Watson on October 2, 2015| Leave a Comment »

From Turing to Watson

Larry H Bernstein, MD, FCAP, Curator

LPBI

From Turing to Watson: The Long-Burning Hype of Machine Learning

Thomas Slowe, Founder & CEO, Nervve

http://www.rdmag.com/articles/2015/10/turing-watson-long-burning-hype-machine-learning?

http://www.rdmag.com/sites/rdmag.com/files/MachineLearning_Infographicx500.jpg

Each year, technology industry watchers anxiously await the release of Gartner’s Hype Cycle to see what’s rising, what’s falling and what’s completely fizzled when it comes to emerging technologies. Many observers specifically look for the “peak of inflated expectations” to see which technologies have hit their high point when it comes to media saturation, but still need several years before reaching their true potential. While there are many well-known categories on this year’s list, including 3-D printing, virtual reality and wearables, they’re joined by one that comes with a bit more mystery—machine learning.

For people in technology, the definition of machine learning is relatively simple. Machine learning is a form of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.

However, if you ask someone on the street, you’re likely to get a blank stare. Some may be able to connect the dots to AI, or perhaps they can reference the technology’s most famous example to date, IBM Watson, thanks to its ability to defeat Jeopardy champions. In reality, machine learning plays a larger role in our everyday lives than people realize. From Siri and Cortana, to our Bluetooth car entertainment systems, to visual technology that can be trained to spot a specific piece of clothing on fuzzy surveillance video, these automated technologies many take for granted have been developed thanks to a machine’s ability to “learn” from prior interactions.

While it may seem machine learning has developed overnight thanks to huge investments and breakthroughs from major players in Silicon Valley, it has, in fact, gone through a long and meandering history over the past 80 plus years. It has morphed from the research focus of a few dozen engineers to the power behind some of today’s most widespread consumer technologies.

In the beginning
While AT&T Bell Labs’ development of the electronic speech synthesizer in 1936 may have been the first major breakthrough for machine learning, the more well-known achievement from this early era was the Turing Test in 1950. Alan Turing introduced the test in a paper he opened with the simple words “I propose to consider the question, ‘Can machines think?’” By proving that humans couldn’t always differentiate between a real person and a machine in basic textual conversations, Turing laid the groundwork for societal acceptance of the concept that machines can learn.

Major advancements in early machine learning weren’t limited to solely voice and text recognition. From the Rosenblatt Preceptron in 1957, to Larry Roberts Computer Visions dissertation in 1963, image also played a major role in molding future research within the artificial intelligence community.

Relatable examples
So when did machine learning truly evolve into a real-world technology? A few major breakthroughs occurred in the 1970s that made the concept much more relatable. In 1976, a license plate recognition system was invented in the U.K. at the Police Scientific Development Branch. While license plate software didn’t become more widely used until a couple of decades later, this type of computer vision technology was the basis for the more high-tech versions used by law enforcement and intelligence agencies today.

Machine learning and robotics collided when the Stanford Cart successfully crossed a chair-filled room without human intervention in 1979. The process took more than five hours, thanks to many pauses so the cart could process what it was seeing and plan a new route. While Google’s self-driving cars certainly don’t take that long to navigate the streets of San Francisco or Austin, machine learning technology brings us closer than ever before to realizing technology that was previously only imagined in movies.

The modern era
The past two decades of AI and machine learning have seen the technology grow. The power of neural networks was initially appreciated in mid-1980s, but computers were too weak to tap the value at a practical level. Geoffrey Hinton revived interest in neural networks in 2006 under the name of “Deep Learning” when he demonstrated a system that learned to classify handwritten digits with high accuracy. Even more impressive was the system’s ability to generate novel handwritten examples that it hadn’t been shown before.

This work started a wave of major Silicon Valley investment in the technology that now powers many of the consumer-focused technology we see today. From virtual assistants like Siri and Cortana, to virtual online chat agents and even to our GPS systems, machine learning-based technology influences almost every aspect of our daily interactions.However, one legacy company has made an outstanding and lasting impact when it comes to how the public perceives the topic of machine technology—IBM. From Deep Blue’s defeat of world chess champion Garry Kasparov in 1997, to Watson’s take down of legendary Ken Jennings on Jeopardy in 2011, one could argue IBM has done more to raise public awareness of a machine’s ability to learn than any other company. Watson’s achievements were particularly impressive, because it wasn’t just about its deep knowledge of questions and answers, but the ability to use natural language processing to understand Alex Trebek’s questions and signal its answer more quickly than humans.

The future
As we look into the future of machine learning, some may have visions of machine overlords eventually taking over the world. However, we believe we’re still several decades away from contemplating this scenario. What we can choose to focus on instead is where the technology will generate value in the next five to 10 years.

While entirely autonomous machines are unfeasible in the near future, “person-in-the-loop” approaches have shown enormous value. From digital services like learning how to beat video games, to physical world applications (like visually identifying possible infrastructure failures and helping doctors perform complicated surgeries), the societal possibilities for machine learning are endless. And when in doubt just ask Siri!

• CONFERENCE AGENDA ANNOUNCED:

The highly-anticipated educational tracks for the 2015 R&D 100 Awards & Technology Conference feature 28 sessions, plus keynote speakers Dean Kamen and Oak Ridge National Laboratory Director Thom Mason.  Learn more.

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Cancer Biology and Genomics for Disease Diagnosis (Vol. I) Now Available for Amazon Kindle

Posted in Academic Publishing, Biochemical pathways, Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Informatics, Cancer Prevention: Research & Programs, Cancer Screening, Cancer Surgery of the Heart, Cell Biology, Clinical & Translational, Clinical Diagnostics, Clinical Genomics, Curation, Curation methodology, De novo synthesis, Discovery process, Disease Biology, Drug Delivery Platform Technology, Enzymes and isoenzymes, Evolutionary cognition, Experimental validation, Explanatory, FDA, FDA Regulatory Affairs, Health Economics and Outcomes Research, HealthCare IT, Historical relevance, International Global Work in Pharmaceutical, Interventional Oncology: Radiofrequency Ablation, Transarterial Chemoembolization, Microwave Ablation and Irreversible Electroporation (IRE), Interviews with Scientific Leaders, Metabolism, Metabolomics, Molecular Genetics & Pharmaceutical, Open Access Journals, Personal Health Applications: Tech Innovations serves HealhCare, Pharmaceutical R&D Informatics, Pharmaceutical R&D Investment, Proteomics, REAL TIME Conference Coverage Twitter's Hashtags and Handles per Presentation/session, Scientific & Biotech Conferences: Press Coverage, Signaling & Cell Circuits, Small Molecules in Development of Therapeutic Drugs, Technology Advance Assessment of, Theoretic convergence, tagged Amazon Kindle, anti-cancer drugs, Aviva Lev-Ari, breast cancer, breast cancer genes, Cancer - General, cancer patient management, cancer risk, Cell Biology, Chemotherapy, Clinical trial, Conditions and Diseases, Content Curation, Curation, Curation methodology, DNA, DNA Sequencing, Food and Drug Administration, genetics, genome, genomics, Harvard Medical School, health outcomes research, Larry H. Bernstein, Medical imaging, Metabolomics, National Institutes of Health, personalised medicine, Personalized medicine, Prostate cancer, research, RNA, science, Stem cell on August 14, 2015| Leave a Comment »

Cancer Biology and Genomics for Disease Diagnosis (Vol. I) Now Available for Amazon Kindle

Reporter: Stephen J Williams, PhD

Article ID #179: Cancer Biology and Genomics for Disease Diagnosis (Vol. I) Now Available for Amazon Kindle. Published on 8/14/2015

WordCloud Image Produced by Adam Tubman

Leaders in Pharmaceutical Business Intelligence would like to announce the First volume of their BioMedical E-Book Series C: e-Books on Cancer & Oncology

Volume One: Cancer Biology and Genomics for Disease Diagnosis

which is now available on Amazon Kindle at                          http://www.amazon.com/dp/B013RVYR2K.

This e-Book is a comprehensive review of recent Original Research on Cancer & Genomics including related opportunities for Targeted Therapy written by Experts, Authors, Writers. This ebook highlights some of the recent trends and discoveries in cancer research and cancer treatment, with particular attention how new technological and informatics advancements have ushered in paradigm shifts in how we think about, diagnose, and treat cancer. The results of Original Research are gaining value added for the e-Reader by the Methodology of Curation. The e-Book’s articles have been published on the Open Access Online Scientific Journal, since April 2012.  All new articles on this subject, will continue to be incorporated, as published with periodical updates.

We invite e-Readers to write an Article Reviews on Amazon for this e-Book on Amazon. All forthcoming BioMed e-Book Titles can be viewed at:

http://pharmaceuticalintelligence.com/biomed-e-books/

Leaders in Pharmaceutical Business Intelligence, launched in April 2012 an Open Access Online Scientific Journal is a scientific, medical and business multi expert authoring environment in several domains of  life sciences, pharmaceutical, healthcare & medicine industries. The venture operates as an online scientific intellectual exchange at their website http://pharmaceuticalintelligence.com and for curation and reporting on frontiers in biomedical, biological sciences, healthcare economics, pharmacology, pharmaceuticals & medicine. In addition the venture publishes a Medical E-book Series available on Amazon’s Kindle platform.

Analyzing and sharing the vast and rapidly expanding volume of scientific knowledge has never been so crucial to innovation in the medical field. WE are addressing need of overcoming this scientific information overload by:

• delivering curation and summary interpretations of latest findings and innovations
• on an open-access, Web 2.0 platform with future goals of providing primarily concept-driven search in the near future
• providing a social platform for scientists and clinicians to enter into discussion using social media
• compiling recent discoveries and issues in yearly-updated Medical E-book Series on Amazon’s mobile Kindle platform

This curation offers better organization and visibility to the critical information useful for the next innovations in academic, clinical, and industrial research by providing these hybrid networks.

Table of Contents for Cancer Biology and Genomics for Disease Diagnosis

Preface

Introduction  The evolution of cancer therapy and cancer research: How we got here?

Part I. Historical Perspective of Cancer Demographics, Etiology, and Progress in Research

Chapter 1:  The Occurrence of Cancer in World Populations

Chapter 2.  Rapid Scientific Advances Changes Our View on How Cancer Forms

Chapter 3:  A Genetic Basis and Genetic Complexity of Cancer Emerge

Chapter 4: How Epigenetic and Metabolic Factors Affect Tumor Growth

Chapter 5: Advances in Breast and Gastrointestinal Cancer Research Supports Hope for Cure

Part II. Advent of Translational Medicine, “omics”, and Personalized Medicine Ushers in New Paradigms in Cancer Treatment and Advances in Drug Development

Chapter 6:  Treatment Strategies

Chapter 7:  Personalized Medicine and Targeted Therapy

Part III.Translational Medicine, Genomics, and New Technologies Converge to Improve Early Detection

Chapter 8:  Diagnosis                                     

Chapter 9:  Detection

Chapter 10:  Biomarkers

Chapter 11:  Imaging In Cancer

Chapter 12: Nanotechnology Imparts New Advances in Cancer Treatment, Detection, &  Imaging                                 

Epilogue by Larry H. Bernstein, MD, FACP: Envisioning New Insights in Cancer Translational Biology

 

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Re-Creation of the Big Pharma Model via Transformational Deals for Accelerating Innovations: Licensing vs In-house inventions

Posted in Pharmaceutical Analytics, Pharmaceutical Drug Discovery, Pharmaceutical Industry Competitive Intelligence, Pharmaceutical R&D Informatics, Pharmaceutical R&D Investment on July 27, 2015| 1 Comment »

Re-Creation of the Big Pharma Model via Transformational Deals for Accelerating Innovations: Licensing vs In-house inventions

Reporter: Aviva Lev-Ari, PhD, RN

SOURCE

http://www.wsj.com/articles/teva-to-buy-allergan-generics-for-40-5-billion-1437988044

Teva-Allergan Buy Likely Heralds More Big Deals Teva’s acquisition of Allergan’s generic-drugs unit for $40.5 billion is likely to trigger more deal-making in the already-frenzied health-care sector. Bernstein analyst Ronny Gal said investors had highlighted

• AbbVie,
• Amgen,
• Pfizer and
• Biogen as potential transaction partners.

“Allergan clearly spells interest in using this case for acquisitions,” he said. (denise.roland@wsj.com; @deniseroland)

SOURCE

Market Talk is a stream of real-time news and market analysis that is available on Dow Jones Newswires.

Allegan

“We will have the potential to add scale in existing therapeutic areas, expand into new therapeutic areas and geographies and evaluate strategic transformational deals as we continue to build on our position as the most dynamic branded growth pharma company,” Allergan CEO Brent Saunders said.

In a sign of its continuing ambition, Allergan announced a deal on Sunday, saying it will pay $560 million upfront for Naurex Inc. and its antidepressant-drug candidate.

SOURCE

http://www.wsj.com/articles/teva-to-buy-allergan-generics-for-40-5-billion-1437988044

Allergan: Pharma’s Biggest Dealmaker Is On The Hunt Again

by Matthew Herper Forbes Staff – My favorite Write @Forbes on Pharma and HealthCare

• Full Interview: Allergan’s CEO On Selling His Generics Business And What He’ll Buy Next5 hours ago
• Allergan To Buy Antidepressant Maker For $560 Million With Hopes Of Preventing Suicides1 day ago
• The FDA Approves A Potent And Pricey Cholesterol-Lowering Shot2 days ago
• The Anthem-Cigna Deal Raises A Big Question For The U.S. Insurance Business3 days ago
• 115 Doctors Propose This Solution For Terrifying Cancer Drug Prices4 days ago

For investors in the generic business, this may be a bit of a warning that stock prices are getting too heady. Saunders (I spoke to him between meetings this morning, as he went on just an hour of sleep) says he expects Teva stock to rise over the long-term, and thinks that the deal for Allergan will improve as that happens. But he also agrees that he’s getting an amazing multiple, and says that two factors led him to the “bittersweet” decision to sell: the great price, and the fact that consolidation among drug purchasers (CVS, Walgreens) and insurers (Aetna buying Humana, Anthem buying Cigna) led Saunders and Bisarro to realize that they had to either bulk up or get out. And they didn’t want to bulk up. Investors in Mylan Pharmaceuticals, which spurned Teva’s advances: Beware.

Saunders is obviously game. One thing that distinguishes him from Valeant billionaire Michael Pearson, the drug industry’s other great consolidator, is that Saunders comes to this from a different place. A decade ago, it looked like he was being groomed by Hassan to potentially take over Schering-Plough before that company ran into problems and got bought by Merck. Unlike Pearson, he’s not looking so much to dismantle the big pharma model as to re-create it.

SOURCE

http://www.forbes.com/sites/matthewherper/2015/07/27/allergan-pharmas-biggest-dealmaker-is-on-the-hunt-again/?utm_medium=email&utm_campaign=Daily%20Digest%20Send%20Control%202015-07-27&utm_source=Sailthru&utm_term=Daily%20Digest%20Horizon%20Control

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