Experiments

Searchable full-text extractions: founding hypothesis, core claims, experimental setups, key results and statistics — pulled out of each paper as structure. Search a cell line, an assay or an entity (e.g. HUH7) and find every paper that worked with it. This corpus stands on its own: most entries carry no reproduction assessment (yet).

• Bioinformatics Analysis of the Characteristics and Correlation of m6A Methylation in Breast Cancer Progression.

  PMID 35655723 · PMC9148239 · Contrast media & molecular imaging · 2022 · 8 claims · 8 setups

  Has reproduction · 48

  Breast cancer samples can be classified into 4 m6A subtypes (quiescent, m6A methylation, protein-binding, mixed) based on Z-score median of Set1 (writer+reader) and Set2 (eraser) gene expression

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• Elucidating the Prognostic and Therapeutic Implications of Insulin Resistance Genes in Breast Cancer: A Machine Learning-Powered Analysis.

  PMID 40427728 · PMC12109394 · Biology · 2025 · 8 claims · 8 setups

  Has reproduction · 84

  A seven-gene IRG prognostic signature (LIFR, EZR, TBC1D4, NSF, RPL5, SAA1, PGK1) predicts overall survival in breast cancer across training and four validation cohorts

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• Analyzing biomarker discovery: Estimating the reproducibility of biomarker sets.

  PMID 35901020 · PMC9333302 · PloS one · 2022 · 8 claims · 6 setups

  Has reproduction · 83

  Non-reproducibility is a common problem in biomarker discovery: independently derived biomarker sets for the same phenotype frequently share very few features across studies

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• Advances in genomic and pharmacokinetic profiling for clinical stratification of metastatic breast cancer.

  PMID 41369820 · PMC12799884 · Discover oncology · 2025 · 8 claims · 8 setups

  Has reproduction · 59

  Key genes AR, AKT1, UBC, CDH1, SMAD3, ROR1, and ROR2 are associated with chemotherapy resistance and poor prognosis in metastatic breast cancer

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• Parsimonious Gene Correlation Network Analysis (PGCNA): a tool to define modular gene co-expression for refined molecular stratification in cancer.

  PMID 30993001 · PMC6459838 · NPJ systems biology and applications · 2019 · 8 claims · 7 setups

  Has reproduction · 71

  Retaining only the top ~3 most correlated edges per gene (EPG3) combined with FastUnfold clustering (termed PGCNA) produces gene co-expression modules with significantly better separation and enrichment of known biology than using all edges or other clustering methods.

  breast colorectal brca representative gene comparative multi-omic

• iBRIDGE: A Data Integration Method to Identify Inflamed Tumors from Single-cell RNA-Seq Data and Differentiate Cell Type-Specific Markers of Immune-Cell Infiltration.

  PMID 37023414 · PMC10236149 · Cancer immunology research · 2023 · 8 claims · 8 setups

  Has reproduction · 94

  Malignant cells cluster by patient in scRNA-seq data while immune and stromal cells cluster by cell type, making malignant cells uniquely suited to carry patient-level inflamed/cold signal

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• A reference profile-free deconvolution method to infer cancer cell-intrinsic subtypes and tumor-type-specific stromal profiles.

  PMID 32111252 · PMC7049190 · Genome medicine · 2020 · 8 claims · 8 setups

  Has reproduction · 91

  DeClust is a reference-profile-free deconvolution method that incorporates molecular subtyping directly into the deconvolution process, outputting cohort-level cancer subtype and stromal reference profiles rather than per-individual profiles

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• DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data.

  PMID 34261540 · PMC8281595 · Genome medicine · 2021 · 8 claims · 7 setups

  Has reproduction · 84

  DeepProg is a novel ensemble framework of deep-learning and machine-learning approaches that robustly predicts patient survival subtypes using multi-omics data

  cancer breast hepatocarcinoma tcga multi-omics gene rna-seq

• Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection.

  PMID 31533612 · PMC6751684 · BMC bioinformatics · 2019 · 8 claims · 6 setups

  Has reproduction · 69

  An ensemble feature selection strategy using consensus of feature relevance across 8 classifier types identifies a 100-miRNA signature from a 1046-feature TCGA dataset

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• Fast, accurate, and racially unbiased pan-cancer tumor-only variant calling with tabular machine learning.

  PMID 36611079 · PMC9825621 · NPJ precision oncology · 2023 · 7 claims · 8 setups

  Has reproduction

  Tabular ML classifiers (TabNet, XGBoost, LightGBM) trained on tumor-only-derived features achieve state-of-the-art somatic vs germline classification, with AUC>94% on TCGA holdout and AUC>85% on metastatic melanoma.

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• scATD: a high-throughput and interpretable framework for single-cell cancer drug resistance prediction and biomarker identification.

  PMID 40501071 · PMC12159290 · Briefings in bioinformatics · 2025 · 8 claims · 6 setups

  Has reproduction · 62

  scATD enables high-throughput single-cell drug sensitivity prediction for new patients without model parameter retraining via bidirectional Bi-AdaIN style transfer

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• Identification and validation of a metabolic-related gene risk model predicting the prognosis of lung, colon, and breast cancers.

  PMID 39779736 · PMC11711664 · Scientific reports · 2025 · 8 claims · 8 setups

  Has reproduction · 74

  540 DEGs overlap across breast, colorectal, and lung cancers out of 11,384 DEGs analyzed

  breast colorectal lung common microarray kegg protein-protein

• SEMdag: Fast learning of Directed Acyclic Graphs via node or layer ordering.

  PMID 39775401 · PMC11709272 · PloS one · 2025 · 8 claims · 5 setups

  Has reproduction · 81

  SEMdag() is a two-step order-based algorithm for fast learning of high-dimensional linear SEMs, using knowledge-based (KB) or data-driven bottom-up (BU) node/layer ordering followed by penalized (L1) DAG estimation

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• Iterative class discovery and feature selection using Minimal Spanning Trees.

  PMID 15355552 · PMC520744 · BMC bioinformatics · 2004 · 7 claims · 5 setups

  Full-text index only

  Iterating between MST-based clustering and t-statistic feature selection removes noise genes step-wise while sharpening the sample clustering

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• Deciphering the Immune Microenvironment at the Forefront of Tumor Aggressiveness by Constructing a Regulatory Network with Single-Cell and Spatial Transcriptomic Data.

  PMID 38254989 · PMC10815467 · Genes · 2024 · 7 claims · 8 setups

  Has reproduction · 85

  High expression of transcription factors FOXA1 and EZH2 in malignant cells at the invasive front plays a key role in driving tumor progression

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• Hierarchical cell-type identifier accurately distinguishes immune-cell subtypes enabling precise profiling of tissue microenvironment with single-cell RNA-sequencing.

  PMID 36681937 · PMC10025442 · Briefings in bioinformatics · 2023 · 8 claims · 8 setups

  Has reproduction · 94

  HiCAT is a hierarchical, marker-based cell-type identifier that uses gene set analysis (GSA) scoring with markers structured in a three-level taxonomy tree (major-type, minor-type, subset)

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