Abhishek Niroula

TL;DR: A new computational tool, PON-P2, for classification of amino acid substitutions in human proteins, which is a machine learning-based classifier and groups the variants into pathogenic, neutral and unknown classes, on the basis of random forest probability score.

TL;DR: The different types of predictors and their applications for variation interpretation are discussed, including generic tolerance (pathogenicity) predictors for filtering harmful variants and Gene/protein/disease‐specific tools available for some applications.

TL;DR: In this article, the authors performed an integrated analysis of gene mutations associated with hematologic malignancies detected in hematopoietic cells of healthy individuals, referred to as CH of indeterminate potential (CHIP), and found that myeloid and lymphoid gene mutations were associated with risk of lineage-specific malignancy.

TL;DR: Natural premature menopause may serve as a risk signal for predilection to develop CHIP and CHIP-associated cardiovascular disease among postmenopausal women.

TL;DR: While these tools had excellent performance, the poorest method predicted more than one third of the benign variants to be disease-causing, which allows choosing reliable methods for benign variant interpretation, for both research and clinical purposes, as well as provide a benchmark for method developers.