News Medical interviewed Dr. Rajat Mukherjee, Statistician, and Director of Data Science at Cytel to investigate the potential of data science in clinical development.
Data science is an important field in medicine as a marriage among many areas that are all complementary. Math, logic, statistical science, and informatics all come together to inform areas such as precision medicine and diagnostics, with medical applications ranging from oncology to infectious disease.
Good data science leads to major breakthroughs driven by real-world evidence of large patient population data sets. Data science also plays a key role in making research more applicable to real world scenarios by helping to accommodate for the heterogeneity and variability of genetics and environmental factors.
One example is in the study of biomarkers, which has become a hot button topic in recent years. There is a large variety of patients with diseases such as Alzheimer’s Disease, Parkinson’s, cancers and rare diseases, which makes searching for biomarkers a difficult process.
Inherited and environmental factors can all influence different genes, and finding consistent genetic biomarkers requires massive amounts of data and a strong data science team to sort the noise from the usable results.
Cytel uses biomedical images and signals to develop statistical classifiers that can inform research. This data is high dimensional, and so requires a lot of pre-processing to draw out the valuable information. Features are then extracted and selected as useful diagnostic classifiers, which are then formed into a model that can be scientifically tested in clinical trials.
This information is highly valuable in the scientific and medical community as it informs any number of areas, from the risks of contracting a condition to diagnostic strategies that can be used to assess its prognosis.
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About Rajat Mukherjee
Rajat Mukherjee has 15 years of professional experience as an industry and academic statistician, and brings a range of expert knowledge to Cytel’s customers. This includes work in pattern recognition problems for devices and biomarker discovery, Bayesian clinical trials, adaptive designs, and design and analysis of complex epidemiological studies.
His experience and expertise also includes statistical computing, survival analysis, longitudinal analysis, nonparametric and semiparametric inference, as well as statistical classification and high-dimensional data. Rajat has a strong background and interest in development and implementation of statistical methodology to real life medical problems.
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