79 citations · 259 across the 14 of their papers we have counts for
15 papers
Beyond Defensive Reporting: Machine Learning for Active Anti-Money Laundering Control in Insurance
Dara Goldar, Geir Kjetil Ferkingstad Sandve, Martin Jullum
Money laundering through insurance claims poses a threat to insurers both through fraudulent payouts and reputational and regulatory risk. Despite this, little research has examine…
Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data
Robert Frank, Michael Widrich, Rahmad Akbar +4
Generative machine learning models offer a powerful framework for therapeutic design by efficiently exploring large spaces of biological sequences enriched for desirable properties…
inMOTIFin: a lightweight end-to-end simulation software for regulatory sequences
Katalin Ferenc, Lorenzo Martini, Ieva Rauluseviciute +2
The accurate development, assessment, interpretation, and benchmarking of bioinformatics frameworks for analyzing transcriptional regulatory grammars rely on controlled simulations…
CAVACHON: a hierarchical variational autoencoder to integrate multi-modal single-cell data
Ping-Han Hsieh, Ru-Xiu Hsiao, Katalin Ferenc +6
Paired single-cell sequencing technologies enable the simultaneous measurement of complementary modalities of molecular data at single-cell resolution. Along with the advances in t…
ImmunoLingo: Linguistics-based formalization of the antibody language
Mai Ha Vu, Philippe A. Robert, Rahmad Akbar +4
Apparent parallels between natural language and biological sequence have led to a recent surge in the application of deep language models (LMs) to the analysis of antibody and othe…
Linguistically inspired roadmap for building biologically reliable protein language models
Mai Ha Vu, Rahmad Akbar, Philippe A. Robert +4
Deep neural-network-based language models (LMs) are increasingly applied to large-scale protein sequence data to predict protein function. However, being largely black-box models a…