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).

• CanPredict: a computational tool for predicting cancer-associated missense mutations.

  PMID 17537827 · PMC1933186 · Nucleic acids research · 2007 · 8 claims · 7 setups

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  CanPredict is a web application providing public access to a random forest classifier that combines SIFT, LogR.E-value, and GOSS scores to predict whether a missense mutation is cancer-associated

  missense observed sjoblom refseq cross-validation recurrence comparative

• Genomic data sampling and its effect on classification performance assessment.

  PMID 12553886 · PMC149349 · BMC bioinformatics · 2003 · 8 claims · 3 setups

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  Cross-validation, leave-one-out, and bootstrap are designed to reduce bias and variance in accuracy estimation from small samples.

  leukemia srbct splice-junction supervised

• nsSNPAnalyzer: identifying disease-associated nonsynonymous single nucleotide polymorphisms.

  PMID 15980516 · PMC1160133 · Nucleic acids research · 2005 · 6 claims · 4 setups

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  nsSNPAnalyzer is a web server that predicts whether a query nsSNP is disease-associated or functionally neutral using a Random Forest classifier combining structural and evolutionary information

  curated homologous structure random structural secondary

• VIRGO: computational prediction of gene functions.

  PMID 16845022 · PMC1538839 · Nucleic acids research · 2006 · 8 claims · 6 setups

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  VIRGO constructs a functional linkage network (FLN) from gene expression and molecular interaction data, labels genes with GO annotations, and propagates these labels to predict functions of unlabelled genes

  cerevisiae sapiens leave-one-out gene protein-protein

• InSite: a computational method for identifying protein-protein interaction binding sites on a proteome-wide scale.

  PMID 17868464 · PMC2375030 · Genome biology · 2007 · 8 claims · 8 setups

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  InSite predicts protein-pair-specific binding motifs ('Motif M on protein A binds to protein B') by integrating heterogeneous PPI and motif-motif interaction evidence within a Bayesian network trained by EM

  saccharomyces homo bank computational yeast tandem

• MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features.

  PMID 17553836 · PMC1933124 · Nucleic acids research · 2007 · 8 claims · 8 setups

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  A hybrid feature combining local contiguous triplet structure-sequence composition, MFE of the secondary structure, and P-value of a randomization test improves classification of real vs pseudo pre-miRNAs

  real combined pre-mirna training random independent repeated

• SNAP: predict effect of non-synonymous polymorphisms on function.

  PMID 17526529 · PMC1920242 · Nucleic acids research · 2007 · 7 claims · 8 setups

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  SNAP, a neural network-based method using sequence-derived information, predicts whether a non-synonymous SNP is neutral or non-neutral for protein function

  combined escherichia bacteriophage hiv-1 neural benchmark comparative

• 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

• A Decentralized Kidney Transplant Biopsy Classifier for Transplant Rejection Developed Using Genes of the Banff-Human Organ Transplant Panel.

  PMID 35619722 · PMC9128066 · Frontiers in immunology · 2022 · 6 claims · 6 setups

  Has reproduction · 90

  A random forest model trained solely on B-HOT panel genes (B-HOT Model) accurately classifies kidney transplant biopsies as NR, ABMR, or TCMR.

  kidney gene combined microarray machine sequential

• 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

• Searching for interpretable rules for disease mutations: a simulated annealing bump hunting strategy.

  PMID 16984653 · PMC1618409 · BMC bioinformatics · 2006 · 8 claims · 6 setups

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  The proposed feature set outperforms existing published feature sets for predicting effects of amino acid substitutions

  coli bacteriophage lac proteins svm simulated pearson

• Predicting the phenotypic effects of non-synonymous single nucleotide polymorphisms based on support vector machines.

  PMID 18005451 · PMC2216041 · BMC bioinformatics · 2007 · 8 claims · 5 setups

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  Parepro, an SVM-based method integrating three attribute sets (RD, MI, IE) derived from evolutionary and residue-property information, predicts whether an nsSNP is deleterious or neutral.

  sequences target svm benchmark homology

• Identification of deleterious non-synonymous single nucleotide polymorphisms using sequence-derived information.

  PMID 18588693 · PMC2446391 · BMC bioinformatics · 2008 · 8 claims · 5 setups

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  A decision tree built on 10 selected sequence-derived features classifies SAPs as Disease or Polymorphism with 82.6% accuracy and 0.607 MCC in cross-validation.

  sap sequence-derived humvar same decision independent greedy

• SNAP predicts effect of mutations on protein function.

  PMID 18757876 · PMC2562009 · Bioinformatics (Oxford, England) · 2008 · 8 claims · 3 setups

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  SNAP is a publicly available web-server implementation predicting functional effects (neutral/non-neutral) of single amino acid substitutions.

  mutant insulin neural alanine mutation

• Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties.

  PMID 16790052 · PMC1534064 · BMC bioinformatics · 2006 · 8 claims · 7 setups

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  The Sequential Minimal Optimization (SMO) SVM algorithm was the best-performing classifier among 26 WEKA classifiers for predicting catalytic residues

  benchmarking same pirsf pdb supervised wrapper sequence

• Broad network-based predictability of Saccharomyces cerevisiae gene loss-of-function phenotypes.

  PMID 18053250 · PMC2246260 · Genome biology · 2007 · 8 claims · 4 setups

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  Loss-of-function phenotypes in yeast are predictable from a gene's connections in a functional gene network via guilt-by-association.

  saccharomyces guilt-by-association comparison network-guided

• Boosting accuracy of automated classification of fluorescence microscope images for location proteomics.

  PMID 15207009 · PMC449699 · BMC bioinformatics · 2004 · 8 claims · 8 setups

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  New classifiers (SVMs, ensembles) and new wavelet-derived (Gabor, Daubechies) features improve recognition of protein subcellular location patterns over the previous neural network approach

  hela automated stepwise training-set-size

• Constructing support vector machine ensembles for cancer classification based on proteomic profiling.

  PMID 16689692 · PMC5173238 · Genomics, proteomics & bioinformatics · 2005 · 7 claims · 4 setups

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  CSVME, built by selecting a subset of base SVMs via SVM-RFE ranking and fusing them with a trained upper-layer SVM, achieves better classification performance than an ensemble of all base SVMs.

  serum computational seldi-tof svm-rfe-based 10-fold

• Prioritization of candidate cancer genes--an aid to oncogenomic studies.

  PMID 18710882 · PMC2566894 · Nucleic acids research · 2008 · 8 claims · 8 setups

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  Computational classifiers using combinations of protein conservation, gene structure, protein domains, protein interactions, and regulatory data can distinguish known cancer genes (CD/CR) from unlabelled human genes

  genes cancer interpro interactome averaged cross-validation domain

• 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