Sandipan Paul Arnab

SHADES, a deep learning framework, distinguishes evolutionary processes like neutrality, selection, and introgression using haplotype data from humans, outperforming existing methods.

The paper integrates positive selection signatures with genetic data to identify metabolically relevant loci in South Asians, finding the MAPT locus associated with insulin resistance.

This paper introduces PULSe, a semi-supervised method for detecting selective sweeps using positive-unlabeled learning, showing high performance across diverse genomic contexts.

This study introduces TrIdent, a transfer learning tool for detecting adaptive genomic regions, showing improved performance over existing methods in various genetic and demographic settings.

This paper introduces T-REx, a method using tensor decomposition to extract features from haplotype images for detecting natural selection, showing high accuracy in simulated scenarios.

This paper applies time-frequency analysis to genomic summary statistics and uses convolutional neural networks to detect natural selection signals with high accuracy.