Healthcare and life sciences

Research and innovation addressing today's greatest health challenges.

 

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Enabling better health outcomes with science and technology

The IBM Research Healthcare and Life Sciences team is dedicated to exploring and developing new methodologies and improving processes for a broad range of health care challenges. From how we can help in the diagnosis of diseases, to managing population health, or a better understanding the human genome, the team blends a broad set of disciplines such as biology, chemistry, data analytics, AI and medicine to pursue their work.

Published in Science: New research into cancer immunotherapies

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Published in Science: New research into cancer immunotherapies

Cancer immunotherapies, treatment approaches which harness a person’s own immune system to target and kill cancer cells, are currently a major driver in the development of new cancer treatments. In research recently published in Science, scientists at IBM, Columbia University and the Memorial Sloan Kettering Cancer Center (MSKCC) have solved an important piece of the immunotherapy puzzle. MSKCC researchers discovered that genes associated with how killer T cells recognize cancer cells are essential for immunotherapies’ success.


Pioneering Microscopic Reality with New AI-powered Microscopes

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Pioneering Microscopic Reality with New AI-powered Microscopes

We are excited to unveil a new ecosystem of smart microscopes, including the microscopic reality system, at the IBM Think conference as part of IBM Research’s “5 in 5” technology predictions. We believe the invention’s future is a small autonomous microscope that could be placed in bodies of water to create 3D models of plankton and track their behavior in their natural environment. This could help in situations including oil spills and to predict threats such as red tides. We envision a network of these devices, to monitor the health of the environment, using plankton as the sensor. By embedding AI in the microscopes, their shape, size and behavior can be analyzed to determine their health. Collecting and sharing this information in the cloud can give us a tremendous insight into the health and operation of the ocean’s complex ecosystem.

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New collaboration with JDRF to investigate Type 1 diabetes

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New collaboration with JDRF to investigate Type 1 Diabetes

T1D affects approximately 1.25 million Americans, and it currently does not have a cure. The research collaboration between IBM and JDRF is to develop and apply machine learning methods to analyze years of global T1D research data and identify factors leading to the onset of T1D in children. This work is expected to create an entry point for T1D in the field of precision medicine, by combining JDRF’s connections to research teams around the globe and its subject matter expertise in T1D research with the technical capability and computing power of IBM.

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Supercomputers identify rare gene that may increase risk of Type 2 Diabetes

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Supercomputers identify rare gene that may increase risk of Type 2 Diabetes

Laxmi Parida, IBM research scientist and manager of the Computational Genomics Group, who is based in Yorktown Heights, New York, says using supercomputers to help analyze medical data will help doctors understand genetic links to diseases in more complicated cases. She says algorithms can untangle the web of genetic data that can hide important diagnostic clues.

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First-of-a-kind microbiome dataset published in Nature

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First-of-a-kind microbiome dataset published in Nature

It’s becoming increasingly clear that our health is influenced by our personal complement of microbes – our microbiome. Awareness of the microbiome has grown in leaps and bounds thanks to the massive capacity of scientific instruments that read the DNA of microbes. But many fundamental questions about microbes remain unanswered, even questions that seem like they should be easy. “Has anyone seen this microbe before?” “Where? “When?” After today, answering these questions will be a lot easier. The problem was not lack of data, but that each microbiome dataset was an island onto itself and not easily compared to the others. Working as a team of microbial ecologists, computational scientists, bioinformaticians, and statisticians, we analyzed the largest collection of microbiome data (by 100 times). In the current issue of Nature, we report the first-of-a-kind microbiome database that lets researchers track microbes across the planet, even if the microbes don’t have a name (as is usually the case).

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Read in Nature


New research in AI pushes frontiers in epileptic seizure prediction

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New research in AI pushes frontiers in epileptic seizure prediction

Recently, the Lancet’s EBioMedicine journal published a study led by scientists from IBM Research-Australia and the University of Melbourne marking important progress in personalized seizure forecasting. The findings, described in a paper titled ‘Epileptic Seizure Prediction using Big Data and Deep Learning: Toward a Mobile System,’ present new results in epileptic seizure prediction using deep learning algorithms deployed on a brain-inspired, mobile processor. Investigators found that the algorithm successfully predicted an average of 69 percent of seizures across patients, including patients who previously had no prediction indicators.

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Research areas

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Computational genomics

Progressing the intersection of algorithmics and genomics, using mathematical and statistical modeling.

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Nanobiology

Applying nanotechnology to biology and medicine, with a focus on precision diagnostics, the exploration of micro and nanoscale materials, and micro and nanofluidics.

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Healthcare informatics

Researching the application of data science across the entire spectrum of healthcare, including the health of individuals, populations and the healthcare system itself.

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Multi-scale modeling

Developing theoretical and computational frameworks to bridge the differences in spatial and temporal scale of human biology using mechanistic models, statistical models and machine learning.

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Drug discovery

Enabling and expediting the identification, discovery and design of safe and effective drugs using data science, new tools and approaches.

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Cognitive IoT and devices

Creating systems and interfaces that engage users and embed cognitive insights into everyday interaction

 
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Microbiome

Gaining insights into deeper genomic information from communities of micro-organisms in various applications

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Imaging Analytics

Using Machine Learning and AI to better analyze images including MRI, retinal and skin images

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