How Cognitive Computing can be Applied in Life Sciences Research

By augmenting human task performance with artificial intelligence, cognitive computing models are a novel way of conducting effective life sciences research programs.

FREMONT, CA: Life sciences researchers are under increased pressure to innovate faster. Data offers the promise of unlocking insights and accelerating breakthroughs. Although more data is available than ever, only a fraction of it is being integrated, analyzed, and understood. The complexity lies in harnessing volumes of data, integrating the data from hundreds of sources, and understanding their formats. New technologies like cognitive computing promise to address this challenge because cognitive solutions are tailored to integrate and analyze large datasets. Read on to know more.

Cognitive technology can understand different data types, including lab values in a structured database or the text of a scientific publication. Cognitive solutions are trained to analyze and understand technical, industry-specific content and use advanced reasoning, predictive modeling, and machine learning techniques to advance research faster. It greatly supports life sciences research.

Decisions in life sciences and medicine depend heavily on evidence. Cognitive technology helps support confident decisions about where to focus research by making the researcher's evidence accessible. Further, the technology leverages predictive analytics to infer connections for which there may not yet be direct evidence. In this case, a cognitive solution relies on a researcher to offer a known set of items like genes related to the disease. The technology also combines its ability to observe, interpret, and evaluate with novel data representation approaches. Thus, it creates a list of the potential genes, drug, or disease candidates that a researcher decides is worth taking to experiments.

Cognitive technology's potential to ingest various data varieties and understand, evaluate, and learn can unlock novel insights. This technology solution may enhance areas like life sciences, which are in dire demand for accelerated innovation. Cognitive computing may also add value in identifying and coding adverse event reports from the and published articles.

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