Tootfinder

Boolean-network simplification and rule fitting to unravel chemotherapy resistance in non-small cell lung cancer
Alonso Espinoza, Eric Goles, Marco Montalva-Medel
https://arxiv.org/abs/2506.02525

Boolean-network simplification and rule fitting to unravel chemotherapy resistance in non-small cell lung cancer
Boolean networks are powerful frameworks for capturing the logic of gene-regulatory circuits, yet their combinatorial explosion hampers exhaustive analyses. Here, we present a systematic reduction of a 31-node Boolean model that describes cisplatin- and pemetrexed-resistance in non-small-cell lung cancer to a compact 9-node core that exactly reproduces the original attractor landscape. The streamlined network shrinks the state space by four orders of magnitude, enabling rapid exploration of cri…

Topological Machine Learning for Protein-Nucleic Acid Binding Affinity Changes Upon Mutation
Xiang Liu, Junjie Wee, Guo-Wei Wei
https://arxiv.org/abs/2505.22786

Topological Machine Learning for Protein-Nucleic Acid Binding Affinity Changes Upon Mutation
Understanding how protein mutations affect protein-nucleic acid binding is critical for unraveling disease mechanisms and advancing therapies. Current experimental approaches are laborious, and computational methods remain limited in accuracy. To address this challenge, we propose a novel topological machine learning model (TopoML) combining persistent Laplacian (from topological data analysis) with multi-perspective features: physicochemical properties, topological structures, and protein Tran…