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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Has reproduction · 89
Graph Random Forest: A Graph Embedded Algorithm for Identifying Highly Connected Important Features.
PMID 37509188 · PMC10377046 · Biomolecules · 2023 · 8 claims · 3 setups
Graph Random Forest (GRF) embeds graph/network information directly into the decision-tree building process by splitting on features in the k-hop neighborhood of a data-driven head-splitting node.
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Has reproduction · 98
maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks.
PMID 36719906 · PMC9917285 · PLoS computational biology · 2023 · 8 claims · 6 setups
maxATAC is a suite of deep neural network models enabling state-of-the-art, genome-scale TFBS prediction from ATAC-seq, with models for 127 human transcription factors
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Has reproduction · 67
Cyrface: An interface from Cytoscape to R that provides a user interface to R packages.
PMID 24715956 · PMC3962008 · F1000Research · 2013 · 8 claims · 6 setups
Cyrface is a Cytoscape app/Java library providing a general interface from Cytoscape (Java) to any R function or package.
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Full-text index only
Gene loss rate: a probabilistic measure for the conservation of eukaryotic genes.
PMID 17158152 · PMC1802574 · Nucleic acids research · 2007 · 8 claims · 8 setups
GLR is a novel maximum-likelihood measure of gene loss rate that probabilistically weighs all possible ancestral phyletic patterns rather than relying on a single parsimonious reconstruction.
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Full-text index only
Prioritization of candidate cancer genes--an aid to oncogenomic studies.
PMID 18710882 · PMC2566894 · Nucleic acids research · 2008 · 8 claims · 8 setups
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