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).

• Interaction profile-based protein classification of death domain.

  PMID 15189571 · PMC459208 · BMC bioinformatics · 2004 · 7 claims · 6 setups

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  An SVM-based classifier using Residue Pair Interaction Profiles (RPIPs) can classify death domain superfamily members into subfamilies with 89% average cross-validation accuracy

  homology-modeled death homology putative rigid-body homology model

• Predicting enhancers in mammalian genomes using supervised hidden Markov models.

  PMID 30917778 · PMC6437899 · BMC bioinformatics · 2019 · 8 claims · 8 setups

  Has reproduction · 70

  eHMM predicts enhancers with high precision and recall comparable to state-of-the-art methods and consistently outperforms them in accuracy and resolution

  esc atac-seq chip-seq cage

• SpaGene: A Deep Adversarial Framework for Spatial Gene Imputation.

  PMID 42146899 · PMC13176606 · Computational and structural biotechnology journal · 2026 · 8 claims · 6 setups

  Has reproduction · 74

  SpaGene improves average PCC and SSIM and reduces RMSE compared to 6 baseline methods (SpaGE, gimVI, Tangram, VISTA, spRefine, stDiff) across 8 diverse ST-SC dataset pairs under gene-holdout evaluation.

  pairs nanostring st-sc osmfish_zeisel st-sc 5-fold spatially

• Local combinational variables: an approach used in DNA-binding helix-turn-helix motif prediction with sequence information.

  PMID 19651875 · PMC2761287 · Nucleic acids research · 2009 · 8 claims · 7 setups

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  The LCV approach predicts HTH motifs with 93.29% accuracy, 93.93% sensitivity and 92.66% specificity using only primary sequence information

  quintessentialset-88 hth same newly lcs svm traditional

• ProMiR II: a web server for the probabilistic prediction of clustered, nonclustered, conserved and nonconserved microRNAs.

  PMID 16845048 · PMC1538778 · Nucleic acids research · 2006 · 6 claims · 4 setups

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  ProMiR II improves on the original ProMiR by integrating free energy, G/C ratio, conservation score and entropy for more controllable miRNA prediction

  genome cytomegalovirus six probabilistic 5-fold directional

• DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning.

  PMID 33662629 · PMC9801043 · Genomics, proteomics & bioinformatics · 2022 · 8 claims · 8 setups

  Has reproduction · 50

  DeeReCT-APA quantitatively predicts the usage of all competing PASs of a gene simultaneously, rather than casting the problem as pairwise comparison like prior methods.

  fibroblast genomic same 3'-region 3'-mrna deep

• Dissecting microregulation of a master regulatory network.

  PMID 18294391 · PMC2289817 · BMC genomics · 2008 · 8 claims · 6 setups

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  143 human miRNAs (termed p53-miRs) each contain at least one putative p53 binding site within 10 kb flanking sequence and are predicted to target at least one known gene

  genome mirnas mirna-target predicted p53 mirna cross-referencing

• Hierarchical classification-based pan-cancer methylation analysis to classify primary cancer.

  PMID 38066424 · PMC10709847 · BMC bioinformatics · 2023 · 8 claims · 8 setups

  Has reproduction · 83

  CHCT, a two-tier hierarchical classification tool built from methylation data, accurately classifies primary cancer type across 30 cancer types.

  tumor clinical methylation clustered methylation unsupervised anova

• Machine-learning approaches for classifying haplogroup from Y chromosome STR data.

  PMID 18551166 · PMC2396484 · PLoS computational biology · 2008 · 8 claims · 5 setups

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  Y-STR allelic variability is partitioned more by differences among haplogroups than by differences among populations, suggesting Y-STRs carry haplogroup information

  globally same computational publicly y-str snp supervised

• Prodepth: predict residue depth by support vector regression approach from protein sequences only.

  PMID 19759917 · PMC2742725 · PloS one · 2009 · 8 claims · 8 setups

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  Residue depth can be reliably predicted solely from protein primary sequence using support vector regression on sequence-derived features.

  structure sequences sequence residue dssp psi-blast

• Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations.

  PMID 19654296 · PMC2763410 · Cancer research · 2009 · 7 claims · 7 setups

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  CHASM, a Random Forest-based computational method, was developed to identify and prioritize missense mutations likely to be functional drivers of tumor cell proliferation.

  missense same synthetic tp53 random support principal

• Multiomics and deep learning dissect regulatory syntax in human development.

  PMID 41951735 · PMC13216069 · Nature · 2026 · 8 claims · 8 setups

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  The Human Development Multiomic Atlas (HDMA) is a single-cell atlas of chromatin accessibility and gene expression from 817,740 fetal cells across 12 organs, spanning 203 cell types

  fetal annotated embryo chrombpnet share-seq chromvar activity-by-contact

• scTWAS: a powerful statistical framework for single-cell transcriptome-wide association studies.

  PMID 41820391 · PMC13121454 · Nature communications · 2026 · 8 claims · 5 setups

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  scTWAS uses a latent-variable expression-measurement model combined with a moment-based regression to more accurately estimate genetic regulation of gene expression from single-cell data, improving GReX prediction across cell types and datasets

  onek1k-derived onek1k-based immune brain simulated two-sample stage

• Enabling Single-Cell Drug Response Annotations from Bulk RNA-Seq Using SCAD.

  PMID 36762572 · PMC10104628 · Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023 · 7 claims · 7 setups

  Has reproduction · 81

  SCAD, a transfer learning framework integrating adversarial discriminative domain adaptation (ADDA), can infer single-cell drug sensitivities by transferring knowledge from bulk RNA-seq pharmacogenomic data (GDSC) to scRNA-seq target domains

  pan-cancer pc9 451lu a375 bulk scrna-seq computational