Valcarcel, L. V. | bioRxiv Science

bioRxiv2022

gMCStool: automated network-based tool to search for metabolic vulnerabilities in cancer

The development of computational tools for the systematic prediction of metabolic vulnerabilities of cancer cells constitutes a central question in systems biology. Here, we present gMCStool, a freely accessible and online tool that allows us to carry out this task in a simple, efficient and intuitive environment. gMCStool exploits the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to synthetic lethality based on genome-scale metabolic networks, including a unique database of thousands of synthetic lethals computed from Human1, the most recent metabolic reconstruction of human cells. Based on RNA-seq data, gMCStool extends and improves our previously developed algorithms to predict, visualize and analyze metabolic essential genes in cancer, demonstrating a superior performance than competing algorithms in both accuracy and computational performance. A detailed illustration of gMCStool is presented for multiple myeloma (MM), an incurable hematological malignancy. gMCStool could identify a synthetic lethal that explains the dependency on CTP Synthase 1 (CTPS1) in a sub-group of MM patients. We provide in vitro experimental evidence that supports this hypothesis, which opens a new research area to treat MM.

systems biology↗

bioRxiv2020

BOSO: a novel feature selection algorithm for linear regression with high-dimensional data

MotivationWith the frenetic growth of high-dimensional datasets in different biomedical domains, there is an urgent need to develop predictive methods able to deal with this complexity. Feature selection is a relevant strategy in machine learning to address this challenge. ResultsWe introduce a novel feature selection algorithm for linear regression called BOSO (Bilevel Optimization Selector Operator). We conducted a benchmark of BOSO with key algorithms in the literature, finding a superior performance in highdimensional datasets. Proof-of-concept of BOSO for predicting drug sensitivity in cancer is presented. A detailed analysis is carried out for methotrexate, a well-studied drug targeting cancer metabolism. AvailabilityA Matlab implementation of BOSO is available as a Supplementary Material. Contactfplanes@tecnun.es Supplementary InformationSupplementary data are available at Bioinformatics online.

bioinformatics↗