scientific-computing
24
cellular-senescence-agent
AI-powered analysis of cellular senescence for aging research, cancer therapy response, and senolytic drug development.
mdbabumiamssm
research
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scientific-computing
24
bio-metabolomics-msdial-preprocessing
MS-DIAL-based metabolomics preprocessing as alternative to XCMS. Covers peak detection, alignment, annotation, and export for downstream analysis. Use when processing MS-DIAL output files for R/Python analysis or when preferring GUI-based preprocessing.
mdbabumiamssm
research
scientific-computing
24
bio-metabolomics-normalization-qc
Quality control and normalization for metabolomics data. Covers QC-based correction, batch effect removal, and data transformation methods. Use when correcting technical variation in metabolomics data before statistical analysis.
mdbabumiamssm
research
scientific-computing
24
bio-metabolomics-statistical-analysis
Statistical analysis for metabolomics data. Covers univariate testing, multivariate methods (PCA, PLS-DA), and biomarker discovery. Use when identifying differentially abundant metabolites or building classification models.
mdbabumiamssm
research
scientific-computing
24
bio-metabolomics-xcms-preprocessing
XCMS3 workflow for LC-MS/MS metabolomics preprocessing. Covers peak detection, retention time alignment, correspondence (grouping), and gap filling. Use when processing raw LC-MS data into a feature table for untargeted metabolomics.
mdbabumiamssm
research
scientific-computing
24
metabolomics-de
Metabolomics differential analysis using univariate tests (t-test, FDR), multivariate methods (PCA, PLS-DA, OPLS-DA, sPLS-DA), Random Forest, and ROC analysis for biomarker discovery.
mdbabumiamssm
research
scientific-computing
24
metabolomics-normalization
Metabolomics data normalization, scaling and transformation.
mdbabumiamssm
research
scientific-computing
24
metabolomics-statistics
Statistical analysis for metabolomics — PCA, PLS-DA, clustering, and univariate tests.
mdbabumiamssm
research
scientific-computing
24
metabolomics-xcms-preprocessing
XCMS3 workflow for LC-MS/GC-MS metabolomics preprocessing. Peak detection (CentWave/MatchedFilter), RT alignment (Obiwarp), correspondence, gap filling, and CAMERA adduct/isotope annotation.
mdbabumiamssm
research
scientific-computing
24
microbiome-cancer-agent
AI-powered analysis of microbiome-cancer interactions including tumor microbiome profiling, immunotherapy response prediction, and microbiome-targeted therapeutic opportunities.
mdbabumiamssm
research
scientific-computing
24
proteomics-ptm
Post-translational modification analysis including phosphorylation, acetylation, and ubiquitination. Site localization, motif analysis, and quantitative PTM analysis with MSstatsPTM.
mdbabumiamssm
research
scientific-computing
24
bio-entrez-search
Search NCBI databases using Biopython Bio.Entrez. Use when finding records by keyword, building complex search queries, discovering database structure, or getting global query counts across databases.
mdbabumiamssm
research
scientific-computing
24
biomni-general-agent
Use the local Biomni checkout to orchestrate its 150+ biomedical tools, databases, and know-how workflows for complex research questions.
mdbabumiamssm
research
scientific-computing
24
bio-sequence-properties
Calculate sequence properties like GC content, molecular weight, isoelectric point, and GC skew using Biopython. Use when analyzing sequence composition, computing physical properties, or comparing sequences.
mdbabumiamssm
research
scientific-computing
24
spatial-condition
Experimental condition comparison using pseudobulk differential expression with proper multi-sample statistics.
mdbabumiamssm
research
scientific-computing
24
spatial-de
Differential expression analysis — find marker genes for clusters or compare two groups. Supports Wilcoxon rank-sum, t-test, and PyDESeq2 methods with publication-ready figures and CSV tables.
mdbabumiamssm
research
scientific-computing
24
bio-small-rna-seq-mirdeep2-analysis
Discover novel miRNAs and quantify known miRNAs using miRDeep2 de novo prediction from small RNA-seq data. Use when identifying new miRNAs or performing comprehensive miRNA profiling with discovery.
mdbabumiamssm
research
scientific-computing
24
bio-workflows-imc-pipeline
End-to-end imaging mass cytometry workflow from raw acquisitions to spatial cell analysis. Orchestrates image preprocessing, segmentation, phenotyping, and spatial statistics. Use when analyzing imaging mass cytometry data end-to-end.
mdbabumiamssm
research
scientific-computing
24
bio-workflows-metabolic-modeling-pipeline
End-to-end genome-scale metabolic modeling from genome sequence to flux predictions. Covers automated reconstruction with CarveMe, model validation with memote, FBA/FVA analysis, and gene essentiality prediction. Use when building metabolic models or predicting metabolic phenotypes from genomic data.
mdbabumiamssm
research
scientific-computing
24
bio-workflows-metabolomics-pipeline
End-to-end metabolomics workflow from raw MS data to pathway analysis. Orchestrates XCMS preprocessing, annotation, normalization, statistical analysis, and pathway mapping. Use when processing LC-MS metabolomics data.
mdbabumiamssm
research
scientific-computing
24
cosmic-database
Access COSMIC cancer mutation database. Query somatic mutations, Cancer Gene Census, mutational signatures, gene fusions, for cancer research and precision oncology. Requires authentication.
oimiragieo
research
scientific-computing
24
deepchem
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.
oimiragieo
research