Steve Mount

Abstract In the era of precision medicine, performing comparative analysis over diverse patient populations is a fundamental step towards tailoring healthcare interventions. However, the critical aspect of equitably selecting molecular features across multiple patients is often overlooked. To address this challenge, we introduce FALAFL (FAir muLti-sAmple Feature seLection), an algorithmic approach based on combinatorial optimization.

Abstract Strings of nucleotides that carry biological information are typically described using sequence motifs that can be represented by weight matrices or consensus sequences. However, many biological signals in DNA or RNA are recognized by multiple factors in temporal sequence, consist of a mixture of sometimes dissimilar alternative motifs, or may be described better by base composition.