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).

• 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

• Development of proteomic patterns for detecting lung cancer.

  PMID 14757945 · PMC3851077 · Disease markers · 2003 · 8 claims · 3 setups

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  A decision tree classification algorithm built on three serum protein mass peaks (8122Da, 1452Da, 1610Da) can discriminate lung cancer patients from healthy controls

  serum pooled seldi-tof-ms decision reproducibility

• DeepGSEA: explainable deep gene set enrichment analysis for single-cell transcriptomic data.

  PMID 38950178 · PMC11236288 · Bioinformatics (Oxford, England) · 2024 · 8 claims · 2 setups

  Has reproduction · 40

  DeepGSEA is an explainable deep gene set enrichment analysis method built on interpretable, prototype-based neural networks.

  simulated real-world simulation scrna-seq-based

• Filtering high-throughput protein-protein interaction data using a combination of genomic features.

  PMID 15833142 · PMC1127019 · BMC bioinformatics · 2005 · 8 claims · 8 setups

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  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.

  saccharomyces bank caenorhabditis homo yeast mass naive

• Detection of venous thromboembolism by proteomic serum biomarkers.

  PMID 17579716 · PMC1891085 · PloS one · 2007 · 5 claims · 8 setups

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  A neural network-based classifier built from direct MALDI-TOF MS serum protein expression profiles can diagnose VTE with sensitivity/specificity that exceeds D-dimer assays

  serum anova-selected citrated depleted direct artificial d-dimer

• 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

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  A panel of 48 genomically characterized breast cancer cell lines can model patient tumour heterogeneity to identify biomarkers predicting response to PG-11047

  panel breast 250-gene candidate genome growth pathway

• Reference based annotation with GeneMapper.

  PMID 16600017 · PMC1557983 · Genome biology · 2006 · 7 claims · 6 setups

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  GeneMapper transfers reference gene annotations to target genomes with higher accuracy than GeneWise and Projector

  orthologous human-mouse chicken encode reference-based multi-species reciprocal

• 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

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  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.

  encode genes invertebrate initio comparative training

• Discovery and identification of potential biomarkers of papillary thyroid carcinoma.

  PMID 19785722 · PMC2761863 · Molecular cancer · 2009 · 8 claims · 7 setups

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  A 3-peak (m/z 9190, 6631, 8697 Da) SVM classification model discriminates PTC from non-cancer controls with high sensitivity and specificity

  serum hplc-purified trypsin-digested crude seldi-tof-ms solid-phase maldi-tof-ms

• 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

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  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.

  pre-mirnas test conserved-hairpin cross-species secondary svm cross-species

• Exogean: a framework for annotating protein-coding genes in eukaryotic genomic DNA.

  PMID 16925841 · PMC1810556 · Genome biology · 2006 · 8 claims · 5 setups

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  Exogean is a framework using directed acyclic coloured multigraphs (DACMs) to represent biological objects (mRNA, ESTs, protein alignments, exons) and iteratively combine them into complex protein-coding transcript models.

  genome sequences automatic manual sequence

• CONTRAST: a discriminative, phylogeny-free approach to multiple informant de novo gene prediction.

  PMID 18096039 · PMC2246271 · Genome biology · 2007 · 8 claims · 5 setups

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  CONTRAST predicts exact coding region structures for 65% more human genes than the previous state-of-the-art de novo predictor (N-SCAN)

  genome sequence drosophila novo gene coding

• 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

• Inferring combinatorial regulation of transcription in silico.

  PMID 15647509 · PMC546154 · Nucleic acids research · 2005 · 8 claims · 5 setups

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  Combining Cluster-Buster (TFBS cluster prediction) with GOSSIP (rigorous GO enrichment statistics with multiple-testing/FDR correction) predicts biological functions controlled by combinatorial transcription factor action, without prior knowledge of factor targets

  upstream monocytes same tfbs microarray permutation

• Combining comparative genomics with de novo motif discovery to identify human transcription factor DNA-binding motifs.

  PMID 17217514 · PMC1780116 · BMC bioinformatics · 2006 · 6 claims · 4 setups

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  A novel method combining 8-species comparative genomics with de novo motif discovery identifies human TF DNA-binding motifs overrepresented and conserved in upstream regions of co-regulated genes

  muscle-specific novo comparative

• 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

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  AUGUSTUS predicted significantly more genes correctly than any other ab initio program in EGASP

  genome human-mouse genes initio comparative extrinsic

• Uncertainty in the mating strategy of honeybees causes bias and unreliability in the estimates of genetic parameters.

  PMID 38632535 · PMC11022492 · Genetics, selection, evolution : GSE · 2024 · 7 claims · 3 setups

  Has reproduction · 98

  The most precise estimates of genetic parameters and genetic trends are obtained when breeding queens are mated with drones of a single DPQ that is correctly assigned in the pedigree (SS mating).

  simulated simulation 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

• Assignment of Streptococcus agalactiae isolates to clonal complexes using a small set of single nucleotide polymorphisms.

  PMID 18710585 · PMC2533671 · BMC microbiology · 2008 · 7 claims · 6 setups

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  A four-SNP set (glnA36, glnA429, glcK180, adhP111) identified via the Not-N algorithm plus empirical testing divides GBS into 10 groups concordant with eBURST-defined population structure.

  gbs clinical bioinformatic eburst kinetic

• 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