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).

• Species-specific protein sequence and fold optimizations.

  PMID 12487631 · PMC139977 · BMC bioinformatics · 2002 · 7 claims · 7 setups

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  Environmental niche is a significant factor explaining variability in amino acid composition across 100 complete genomes

  predicted mean over orf principal hierarchical domain-based

• 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

• 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

• Ab initio identification of human microRNAs based on structure motifs.

  PMID 18088431 · PMC2238772 · BMC bioinformatics · 2007 · 8 claims · 7 setups

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  MiRPred predicts miRNA precursors ab initio using only predicted secondary structure motifs, ignoring nucleotide sequence

  genome mirna hairpins chromosome secondary linear five-fold

• 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

• ProMiR II: a web server for the probabilistic prediction of clustered, nonclustered, conserved and nonconserved microRNAs.

  PMID 16845048 · PMC1538778 · Nucleic acids research · 2006 · 6 claims · 4 setups

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  ProMiR II improves on the original ProMiR by integrating free energy, G/C ratio, conservation score and entropy for more controllable miRNA prediction

  genome cytomegalovirus six probabilistic 5-fold directional

• Predicting enhancers in mammalian genomes using supervised hidden Markov models.

  PMID 30917778 · PMC6437899 · BMC bioinformatics · 2019 · 8 claims · 8 setups

  Has reproduction · 70

  eHMM predicts enhancers with high precision and recall comparable to state-of-the-art methods and consistently outperforms them in accuracy and resolution

  esc atac-seq chip-seq cage

• A genome-wide survey of Major Histocompatibility Complex (MHC) genes and their paralogues in zebrafish.

  PMID 16271140 · PMC1309616 · BMC genomics · 2005 · 8 claims · 4 setups

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  149 putative MHC gene loci and their paralogues were identified in the zebrafish genome using sequence similarity searches against the Zv4 draft assembly.

  zebrafish sequence comparative cross-validation

• 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

• Using ESTs to improve the accuracy of de novo gene prediction.

  PMID 16817966 · PMC1534067 · BMC bioinformatics · 2006 · 8 claims · 8 setups

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  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

  elegans genome chromosome novo gene partial