This executive report is based on an in-depth study by the IBM Institute for Business Value research team. It looks at the current state of analytics across the healthcare ecosystem, including how organizations are implementing analytics strategies.
In the presentation below, Dr. Geoffrey E. Hinton, Distinguished Researcher at Google, discusses how recent advances in machine learning cast new light on two puzzling biological phenomena.
Insilico Medicine to Utilize Deep Learning for Drug Repurposing and Discovery in Cancer and Age-Related Disease
Insilico Medicine, a bioinformatics company dedicated to drug discovery for cancer and aging, has launched its proprietary DeepPharma (TM) platform. DeepPharma utilizes the latest advances in deep learning to improve computer analysis of massive structured multi-omics data banks and millions of tissue-specific pathway activation profiles.
A new research institute at UCLA may eventually provide doctors with tools to more accurately tailor medicines for individual patients, which could both improve quality of care and minimize the side effects associated with today’s medicine.
Qubole, the big data-as-a-service company, announced that Station X, a leading developer of technologies that make large-scale human genome management and analysis easier, is using Presto on Qubole’s cloud-based big data platform to power GenePool™, a powerful software-as-a-service solution for real-time analytics of genomic and medical information.
BIG DATA CASE STUDY UCLA announced a new institute to help medical and biology researchers make sense of Big Data. Millions upon millions of medical records and test results. Countless DNA sequences. Hard drives stuffed with images of all kinds – pictures of cells, scans of body parts. It’s all part of the deluge of […]
In your world – numbers and data can save lives. Minutes and seconds absolutely matter. Whether engaged in genome sequencing, drug design, product analysis or risk management, life sciences research teams need high-performance technical environments with the ability to process massive amounts of data and support increasingly sophisticated simulations and analyses.
For a long time, the industry’s biggest technical challenge was squeezing as many compute cycles as possible out of silicon chips so they could get on with solving the really important, and often gigantic problems in science and engineering faster than was ever thought possible. Now, by clustering computers to work together on problems, scientists are free to consider even larger and more complex real-world problems to compute, and data to analyze.
Accenture (NYSE: ACN) has launched Predictive Health Intelligence, a comprehensive set of analytics solutions to help global life sciences companies determine the precise combination of treatments and services that can lead to better patient, provider, and economic outcomes.
When Stanislav Dusko Ehrlich – a world expert in microbiology and a pioneer of metagenomics – and his team set out to create their next generation biotech research platform, they needed a technology solution to support their stringent capacity and performance requirements for big data analytics.