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).
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Discovery and identification of potential biomarkers of papillary thyroid carcinoma.
PMID 19785722 · PMC2761863 · Molecular cancer · 2009 · 8 claims · 7 setups
A 3-peak (m/z 9190, 6631, 8697 Da) SVM classification model discriminates PTC from non-cancer controls with high sensitivity and specificity
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Has reproduction · 80
Colorectal Cancer Prediction Based on Weighted Gene Co-Expression Network Analysis and Variational Auto-Encoder.
PMID 32825264 · PMC7563725 · Biomolecules · 2020 · 6 claims · 7 setups
Combining WGCNA-derived hub genes with a VAE-derived 10-dimensional representation as features for an SVM classifier achieves high accuracy (0.9692) and AUC (0.9981) for colorectal cancer prediction.
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Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine.
PMID 16381612 · PMC1360673 · BMC bioinformatics · 2005 · 7 claims · 7 setups
A 32-dimensional triplet structure-sequence feature vector combined with SVM (triplet-SVM) can distinguish real human pre-miRNAs from pseudo pre-miRNA hairpins with ~90% accuracy.
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An SVM-based system for predicting protein subnuclear localizations.
PMID 16336650 · PMC1325059 · BMC bioinformatics · 2005 · 7 claims · 3 setups
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.
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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
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
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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
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.
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Has reproduction · 86
Molecular Classification Models for Triple Negative Breast Cancer Subtype Using Machine Learning.
PMID 34575658 · PMC8472680 · Journal of personalized medicine · 2021 · 6 claims · 4 setups
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.
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Accurate splice site prediction using support vector machines.
PMID 18269701 · PMC2230508 · BMC bioinformatics · 2007 · 8 claims · 5 setups
Weighted degree (WD) kernel SVMs outperform Markov Chains, GeneSplicer and SpliceMachine for genome-wide splice site recognition
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A modified T-test feature selection method and its application on the HapMap genotype data.
PMID 18267305 · PMC5054219 · Genomics, proteomics & bioinformatics · 2007 · 7 claims · 4 setups
A modified t-test ranking measure, extended to handle nominal SNP genotype data via vector transformation, can effectively rank SNPs by their discriminative capability for population classification.
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Has reproduction · 96
Scalable Prediction of Acute Myeloid Leukemia Using High-Dimensional Machine Learning and Blood Transcriptomics.
PMID 31918046 · PMC6992905 · iScience · 2020 · 8 claims · 8 setups
Data-driven, high-dimensional ML approaches that learn multivariate signatures directly from genome-wide transcriptomic data (no prior gene selection) yield accurate and robust AML classifiers.
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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
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.
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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
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
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Has reproduction · 83
Gene-expression patterns in peripheral blood classify familial breast cancer susceptibility.
PMID 26538066 · PMC4634735 · BMC medical genomics · 2015 · 8 claims · 5 setups
A multigene peripheral-blood gene-expression biomarker accurately classifies which women from high-risk families develop familial breast cancer.
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BRCA1 and BRCA2 missense variants of high and low clinical significance influence lymphoblastoid cell line post-irradiation gene expression.
PMID 18497862 · PMC2375115 · PLoS genetics · 2008 · 8 claims · 6 setups
BRCA1 and BRCA2 pathogenic mutation carriers have similar post-irradiation LCL gene expression profiles to each other, more so than to BRCAX samples without an LCS variant
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Has reproduction · 59
Application of Machine Learning in Predicting Hepatic Metastasis or Primary Site in Gastroenteropancreatic Neuroendocrine Tumors.
PMID 37887568 · PMC10605255 · Current oncology (Toronto, Ont.) · 2023 · 8 claims · 7 setups
Multi-gene random forest models classify primary tumor vs. liver metastasis samples with 100% accuracy in training/test cohorts and >90% accuracy in an independent validation cohort
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Zebrafish whole-adult-organism chemogenomics for large-scale predictive and discovery chemical biology.
PMID 18618001 · PMC2442223 · PLoS genetics · 2008 · 8 claims · 6 setups
Zebrafish whole-adult-organism chemogenomics generates robust prediction models that discriminate P(H)AHs from ECs across independent experiments
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Discovering cancer genes by integrating network and functional properties.
PMID 19765316 · PMC2758898 · BMC medical genomics · 2009 · 8 claims · 6 setups
Cancer genes have distinct PPI network topology (higher connectivity, higher clustering coefficient, shorter path length to known cancer genes) compared to non-cancer genes
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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
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
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Emerging translational bioinformatics: knowledge-guided biomarker identification for cancer diagnostics.
PMID 19964620 · PMC5003034 · Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2009 · 8 claims · 3 setups
omniBiomarker, a web-based application, uses prior biological knowledge to identify the most biologically relevant gene ranking metric for a given clinical problem
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Supervised learning-based tagSNP selection for genome-wide disease classifications.
PMID 18366619 · PMC2386071 · BMC genomics · 2008 · 7 claims · 2 setups
SRFA (Supervised Recursive Feature Addition) is a novel feature selection method combining supervised learning and statistical redundancy measures for SNP selection