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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Ab initio identification of human microRNAs based on structure motifs.
PMID 18088431 · PMC2238772 · BMC bioinformatics · 2007 · 8 claims · 7 setups
MiRPred predicts miRNA precursors ab initio using only predicted secondary structure motifs, ignoring nucleotide sequence
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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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JIGSAW, GeneZilla, and GlimmerHMM: puzzling out the features of human genes in the ENCODE regions.
PMID 16925843 · PMC1810558 · Genome biology · 2006 · 8 claims · 4 setups
Adding model states for specific biological features (signal peptides, CpG islands, etc.) to non-comparative GHMM gene finders did little or nothing to enhance predictive accuracy, sometimes reducing it.
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Diversity of the parB and repA genes of the Burkholderia cepacia complex and their utility for rapid identification of Burkholderia cenocepacia.
PMID 18328098 · PMC2324101 · BMC microbiology · 2008 · 8 claims · 7 setups
repA sequences show distinct clustering of B. cenocepacia relative to other Bcc species, enabling design of a species-specific multiplex PCR
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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 · 7 setups
A multigene expression biomarker from PBMCs accurately classifies familial breast cancer (FBC) status
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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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Analysis of protein sequence and interaction data for candidate disease gene prediction.
PMID 17020920 · PMC1636487 · Nucleic acids research · 2006 · 8 claims · 7 setups
Combining CPS and CMP using known disease genes as input achieves sensitivity 0.52 and specificity 0.97, reducing candidate lists 13-fold
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AUGUSTUS at EGASP: using EST, protein and genomic alignments for improved gene prediction in the human genome.
PMID 16925833 · PMC1810548 · Genome biology · 2006 · 8 claims · 5 setups
AUGUSTUS predicted significantly more genes correctly than any other ab initio program in EGASP
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Using ESTs to improve the accuracy of de novo gene prediction.
PMID 16817966 · PMC1534067 · BMC bioinformatics · 2006 · 8 claims · 8 setups
TWINSCAN_EST combines EST alignments with TWINSCAN via a trainable 'ESTseq' representation and improves exact gene structure prediction accuracy on the whole C. elegans genome
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Identification of serum biomarkers for colon cancer by proteomic analysis.
PMID 16755300 · PMC2361335 · British journal of cancer · 2006 · 8 claims · 8 setups
Complement C3a des-arg, α1-antitrypsin and transferrin were identified as serum proteins with diagnostic potential for CRC.
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Pol II promoter prediction using characteristic 4-mer motifs: a machine learning approach.
PMID 18834544 · PMC2575220 · BMC bioinformatics · 2008 · 8 claims · 8 setups
128 discriminating 4-mer motifs combined with an SVM (RBF kernel, LIBSVM) can distinguish promoter from non-promoter DNA sequences
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Filtering high-throughput protein-protein interaction data using a combination of genomic features.
PMID 15833142 · PMC1127019 · BMC bioinformatics · 2005 · 8 claims · 8 setups
A combination of three genomic features (interacting Pfam domains, GO annotations, sequence homology) using naive Bayesian networks predicts true protein-protein interactions with high sensitivity and good specificity.
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Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum.
PMID 16980117 · PMC7159276 · Lancet (London, England) · 2006 · 8 claims · 5 setups
An SVM classifier trained on serum proteomic profiles discriminated patients with active tuberculosis from controls with clinically overlapping conditions
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Critical evaluation of drug response prediction models with DrEval.
PMID 42120410 · PMC13168506 · Nature communications · 2026 · 8 claims · 6 setups
DrEval is a living open-source benchmarking pipeline for unbiased, biologically meaningful evaluation of cancer drug response prediction models, integrating standardized preprocessing, hyperparameter tuning, statistically rigorous evaluation, cross-study benchmarks, and ablation studies.
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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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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
The LCV approach predicts HTH motifs with 93.29% accuracy, 93.93% sensitivity and 92.66% specificity using only primary sequence information
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Comprehensive analysis of the causal risk factor from hypertension associated with prognosis and therapeutic response in renal cell carcinoma by multi-omics analysis and validation.
PMID 41680825 · PMC12998095 · Biology direct · 2026 · 8 claims · 8 setups
A 48-gene cross-species hypertension (HTN) gene module identified from human and SHR rat scRNA-seq can classify ccRCC patients into two molecular subgroups with distinct survival and targeted therapy response
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Integrating complex genomic datasets and tumour cell sensitivity profiles to address a 'simple' question: which patients should get this drug?
PMID 20003409 · PMC2799438 · BMC medicine · 2009 · 8 claims · 5 setups
A panel of 48 genomically characterized breast cancer cell lines can model patient tumour heterogeneity to identify biomarkers predicting response to PG-11047
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Has reproduction · 78
Enhancing chemotherapy response prediction via matched colorectal tumor-organoid gene expression analysis and network-based biomarker selection.
PMID 39754813 · PMC11754497 · Translational oncology · 2025 · 6 claims · 8 setups
A consensus WGCNA approach combining matched tumor-organoid and independent organoid drug-response expression data identifies gene modules and hub genes predictive of 5-FU chemotherapy response
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Salivary proteomics for oral cancer biomarker discovery.
PMID 18829504 · PMC2877125 · Clinical cancer research : an official journal of the American Association for Cancer Research · 2008 · 8 claims · 5 setups
Subtractive salivary proteomics reveals proteins differentially present/expressed between OSCC patients and matched healthy controls.