Chris Sander

The paper introduces a hybrid framework to isolate stability-independent activity effects in enzymes using dual-graph deconvolution, validated with experimental data.

The paper investigates the relationship between cell shape, protein expression, and tissue state using spatial imaging and machine learning, finding a bidirectional link and identifying p53-positive tumor cells.

evedesign provides a unified framework for conditional biosequence design, enabling multiobjective workflows and experimental integration across various applications.

Pertpy provides a modular framework for analyzing large-scale perturbation single-cell experiments, offering tools for metadata annotation and perturbation distance calculations.

This study develops a computational pipeline for analyzing spatial proteomics data to identify tissue states associated with chemotherapy response in TNBC, revealing distinct spatial patterns and predictive markers.

MutationAssessor version 4 provides updated functional impact scores for mutations using expanded data and improved analysis methods, now integrated into cBioPortal.

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.

This study uses evolutionary models to design highly divergent protein variants with enhanced functional properties while maintaining structure.

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

The paper evaluates distance metrics for single-cell RNA-seq data and recommends mean squared error for comparing populations and evaluating gene expression models.

The paper introduces LanTERN, a fluorescent sensor engineered to bind lanthanides with high specificity and fluorescence response.

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.

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.

This paper uses deep learning to predict pancreatic cancer risk from clinical disease trajectories in two large datasets, achieving higher accuracy than models without temporal information.

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 an alignment-free deep generative model for protein design and variant effect prediction, showing improved performance over existing methods in challenging cases.

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.

PredictProtein provides protein sequence analysis tools, offering structure and function predictions with updates including deep learning and DNA/RNA/protein binding predictions.

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.

The paper develops a temporal modeling framework to link protein changes to drug responses in melanoma cells, identifying effective therapies through experimental validation.

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.

The paper introduces EVcouplings, a Python framework for coevolutionary sequence analysis that enables structure prediction and mutation effect assessment.

The paper uses genetic epistasis experiments to predict 3D protein structures with 86% accuracy for residue contacts and generates models within 2.4 Å of known structures for small proteins.

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

The paper identifies and characterizes coding and non-coding driver mutations across 2,500 cancer genomes, revealing new candidates and questioning previous findings.

MatchMiner automates matching cancer patients to precision medicine trials using genomic and clinical criteria, enabling efficient and accurate trial enrollment.