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).

• 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

• 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

• 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

• 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

• Prediction of candidate primary immunodeficiency disease genes using a support vector machine learning approach.

  PMID 19801557 · PMC2780952 · DNA research : an international journal for rapid publication of reports on genes and genomes · 2009 · 6 claims · 3 setups

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  An SVM trained on 69 binary features of known PID genes can accurately classify PID vs non-PID genes and predict novel candidate PID genes

  genome training svm support leave-one-out sensitivity

• Molecular Classification Models for Triple Negative Breast Cancer Subtype Using Machine Learning.

  PMID 34575658 · PMC8472680 · Journal of personalized medicine · 2021 · 6 claims · 4 setups

  Has reproduction · 86

  A training gene set of 719 unique upregulated DEGs (subtype-specific) can be used to build ML models that classify TNBC into BLIA, BLIS, MES, and LAR subtypes.

  known tnbc unique unknown gene machine term

• Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum.

  PMID 16980117 · PMC7159276 · Lancet (London, England) · 2006 · 8 claims · 5 setups

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  An SVM classifier trained on serum proteomic profiles discriminated patients with active tuberculosis from controls with clinically overlapping conditions

  serum computational kda spin-column seldi-tof supervised peptide

• Accurate splice site prediction using support vector machines.

  PMID 18269701 · PMC2230508 · BMC bioinformatics · 2007 · 8 claims · 5 setups

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  Weighted degree (WD) kernel SVMs outperform Markov Chains, GeneSplicer and SpliceMachine for genome-wide splice site recognition

  nn269 dgsplicer caenorhabditis arabidopsis svm genome-wide

• 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

• An SVM-based system for predicting protein subnuclear localizations.

  PMID 16336650 · PMC1325059 · BMC bioinformatics · 2005 · 7 claims · 3 setups

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  New kernels defined on k-peptide vectors mapped by BLOSUM62-based high-scored pair matrices (D1, D2, D3) improve SVM discrimination of protein subnuclear localization compared to conventional k-peptide encodings.

  single-localization multi-localization same svm sequence

• 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

• Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics.

  PMID 18053132 · PMC2254445 · BMC bioinformatics · 2007 · 6 claims · 5 setups

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  The paired oligo-border kernel (POBK) combined with SVMs predicts peptide adsorption/elution in SAX-SPE and retention time in IP-RP-HPLC more accurately than existing methods.

  peptides peptide sax-spe ip-rp-hplc cross-run

• Pol II promoter prediction using characteristic 4-mer motifs: a machine learning approach.

  PMID 18834544 · PMC2575220 · BMC bioinformatics · 2008 · 8 claims · 8 setups

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  128 discriminating 4-mer motifs combined with an SVM (RBF kernel, LIBSVM) can distinguish promoter from non-promoter DNA sequences

  plant drosophila promoter same 4-mer tata blast

• 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

• SePaCS--a web-based application for classification of seroreactivity profiles.

  PMID 17478503 · PMC1933220 · Nucleic acids research · 2007 · 8 claims · 4 setups

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  SePaCS is a freely available web-based tool that trains and applies multiple classification methods (4 Naive Bayes variants, SVM with RBF kernel, LDA, DLDA) to seroreactivity profiles and outputs results as a summary table plus a detailed PDF report

  blood computational seroreactivity supervised mutual

• Genomic variation in myeloma: design, content, and initial application of the Bank On A Cure SNP Panel to detect associations with progression-free survival.

  PMID 18778477 · PMC2553089 · BMC medicine · 2008 · 7 claims · 7 setups

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  A custom BOAC SNP panel of 3404 SNPs in 983 genes was developed using the Affymetrix GeneChip Targeted Genotyping Platform, focused on non-synonymous coding SNPs and regulatory-region SNPs in candidate genes.

  coriell myeloma duplicate snp targeted genotyping machine

• A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data.

  PMID 19772600 · PMC2765429 · Journal of translational medicine · 2009 · 7 claims · 3 setups

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  The naive Bayes model with the wrapper-based feature selection approach performed best among all predictive models tested for distinguishing CFS from controls.

  subjects naive svm c4.5

• Discovering cancer genes by integrating network and functional properties.

  PMID 19765316 · PMC2758898 · BMC medical genomics · 2009 · 8 claims · 6 setups

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  Cancer genes have distinct PPI network topology (higher connectivity, higher clustering coefficient, shorter path length to known cancer genes) compared to non-cancer genes

  genes gene colon ppi pfam term

• Mining novel biomarkers for prognosis of gastric cancer with serum proteomics.

  PMID 19740432 · PMC2753349 · Journal of experimental & clinical cancer research : CR · 2009 · 7 claims · 4 setups

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  A 5-peak prognosis pattern (4474, 4542, 6443/6643, 4988, 6685 Da) predicts poor vs good prognosis in GC with higher sensitivity/specificity than CEA and TNM stage

  serum seldi seldi-tof-ms chemiluminescence bioinformatic