Augustin Luna

The paper uses single-cell RNA sequencing to show heterogeneous SLFN11 expression in cancer cells, linking it to stress response and genome maintenance.

SCLC TumorMiner is a genomics platform that provides molecular classification, biomarker identification, and drug-response analysis for small cell lung cancer using integrated datasets from multiple institutions.

The paper introduces textToKnowledgeGraph, a tool using large language models to extract molecular interactions into BEL format for analysis in Cytoscape.

This paper identifies SEMA7A as a prognostic biomarker in adrenocortical carcinoma using transcriptome data from TCGA and NCI samples, showing its association with poor prognosis and specific signaling pathways.

The paper uses protein mass spectrometry to analyze drug responses in ovarian cancer cells, identifies resistance mechanisms, and proposes drug combinations and single agents to overcome resistance.

The paper harmonizes single-cell perturbation datasets and introduces E-statistics for analyzing perturbation effects across diverse data sources.

The paper introduces a database called COVIDpro that curates proteomic data from COVID-19 patients and provides tools for analyzing protein dysregulation to identify biomarkers and therapeutic targets.

The paper introduces ImogiMap, a statistical framework to identify tumor-immune interactions by analyzing their co-association with immune phenotypes, and identifies the CD86/CD70 axis as a potential immunotherapy target in endometrial carcinoma.

The paper presents a computational framework for analyzing SARS-CoV-2-host interactions to identify drug targets and model disease mechanisms.

This study identifies a 24-protein panel distinguishing TNBC from other breast cancers and shows proteomics improves drug sensitivity predictions.

The paper introduces Biofactoid, a tool that crowdsources structured biological knowledge from authors, enabling efficient human-computer interaction and expanding existing pathway databases.

The paper describes the development of a computational knowledge repository, the COVID-19 Disease Map, which curates SARS-CoV-2 virus-host interaction mechanisms and provides tools for their analysis and application.

The paper introduces a computational method to generate causal explanations for proteomic profiles by integrating molecular data with prior biological pathway knowledge, demonstrating its application in various biological contexts.

This paper introduces TargetScore, a method to identify adaptive responses and predict drug-induced vulnerabilities in cancer cells, validated through experiments on breast cancer cells.

The paper introduces a hybrid machine learning approach for perturbation biology to identify combinatorial therapeutic candidates using a melanoma cell line dataset.

NetBoxR identifies cancer-related biological processes using network analysis, combining multiple data types to find functional modules without relying on curated gene sets.

AlignmentViewer provides a web-based tool for visualizing and analyzing large protein family alignments, supporting evolutionary coupling analysis and 3D structure computation.

Pathway Commons updates its resource with integrated pathway data, tools, and software for biological pathway analysis.

This study rapidly generates proteomic data for NCI-60 cancer cell lines and identifies drug response determinants by integrating proteomic, genetic, and transcriptomic data.

CellMinerCDB integrates pharmacogenomic data from multiple cancer cell line databases to identify drug response determinants, reproducibility, and novel genomic signatures.

PaxtoolsR provides R access to pathway data from Pathway Commons, enabling extraction, merging, validation, and integration with local BioPAX datasets.

The paper identifies clear cell renal cell carcinoma as highly infiltrated by T-cells and finds three clinically relevant clusters associated with survival outcomes.