Applications of Machine Learning and Statistical Modeling in Precision Oncology

Dear Colleagues,

Molecular profiling (or tumor genomic profiling) of tumor biopsies plays an increasingly important role in cancer research, as well as in the treatment management of cancer patients. The introduction of next-generation sequencing technologies and the rising number of large-scale tumor molecular profiling programs across different cancer types hold the promise of improving diagnostics, prognostics and personalized treatment. As a result, we are generating tons of data, but these data are of no use unless we analyze them and find the biological patterns hidden within.

To deal with this, data science approaches—such as artificial intelligence, machine learning and statistical modeling, which help to turn information into knowledge in order to better understand human health—have become part of the vocabulary in biological and medical research. Due to this achievement, ways to pre-process, analyze, and infer knowledge have considerably changed in recent decades, whether in relation to transcriptomics, proteomics, epigenetics, sequencing data, clinical data, electronic health records, or medicine in general. 

In this issue, we will discuss some aspects of this revolution, with a special emphasis on bioinformatics, machine learning, statistical modeling, how the omics data are being analyzed and used to improve cancer treatment and management.

Dr. Sandeep K. Singhal
Guest Editor

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