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).

• A model-based approach to selection of tag SNPs.

  PMID 16776821 · PMC1525207 · BMC bioinformatics · 2006 · 7 claims · 5 setups

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  The Li and Stephens hidden Markov model outperforms other tested models (simple Markov, two-state HMM, HMM-4D, greedy GR-1/GR-2) in description code-length, tag set information content, and prediction of tagged SNPs.

  hapmap encode enr112 tag cross-validated entropy tagged

• Predicting survival outcomes using subsets of significant genes in prognostic marker studies with microarrays.

  PMID 16549007 · PMC1544357 · BMC bioinformatics · 2006 · 7 claims · 2 setups

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  A methodology combining Cox proportional hazards models with a compound covariate, cross-validated log partial likelihood (ACVL) for predictive accuracy, and permutation-based significance testing can identify an optimal subset of significant genes for survival prediction

  simulated diffuse simulation cdna

• Robust and efficient annotation of cell states through gene signature scoring.

  PMID 41708334 · PMC12951948 · Genome research · 2026 · 8 claims · 8 setups

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  Established scoring methods (Seurat, SCANPY, UCell, JASMINE) fail to provide robust and comparable score distributions across diverse signatures and experimental conditions, precluding accurate unsupervised cell-state annotation.

  pbmc colorectal esophageal crc scrna-seq cross-implementation signature

• Systematic benchmarking of tools for CpG methylation detection from nanopore sequencing.

  PMID 34103501 · PMC8187371 · Nature communications · 2021 · 8 claims · 6 setups

  Has reproduction · 99

  Existing Nanopore methylation detection tools present a tradeoff between false positives and false negatives and show high dispersion relative to expected methylation frequency values.

  coli individual lymphoblastoid na12878 nanopore per-read consensus

• Bayesian survival analysis in genetic association studies.

  PMID 18617538 · PMC2530885 · Bioinformatics (Oxford, England) · 2008 · 7 claims · 5 setups

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  A novel Bayesian method (BETA-Surv) extends prior case-control haplotype-clustering work to censored survival outcomes by clustering haplotypes via gene tree/perfect phylogeny topology and relative mutation age.

  simulated same sanad bayesian univariate partial

• Predicting failure rate of PCR in large genomes.

  PMID 18492719 · PMC2441781 · Nucleic acids research · 2008 · 7 claims · 8 setups

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  The number of predicted primer-binding sites in genomic DNA is the most important factor determining PCR failure.

  genomic genome pcr combined large-scale exact-match mismatch-tolerant

• Discovery of molecular subtypes in leiomyosarcoma through integrative molecular profiling.

  PMID 19901961 · PMC2820592 · Oncogene · 2010 · 8 claims · 6 setups

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  Unsupervised gene expression clustering identifies 3 reproducible molecular subtypes of LMS (Group I/muscle-enriched, Group II, Group III)

  lms independent soft gene array immunohistochemistry

• Multiomics and deep learning dissect regulatory syntax in human development.

  PMID 41951735 · PMC13216069 · Nature · 2026 · 8 claims · 8 setups

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  The Human Development Multiomic Atlas (HDMA) is a single-cell atlas of chromatin accessibility and gene expression from 817,740 fetal cells across 12 organs, spanning 203 cell types

  fetal annotated embryo chrombpnet share-seq chromvar activity-by-contact

• Differential Hsp90-dependent gene expression is strain-specific and common among yeast strains.

  PMID 37138775 · PMC10149407 · iScience · 2023 · 8 claims · 7 setups

  Has reproduction · 100

  Hsp90-dependent gene expression varies among different yeast strains

  diverse same yeast genomes rna-seq v-src growth

• Predicting positive p53 cancer rescue regions using Most Informative Positive (MIP) active learning.

  PMID 19756158 · PMC2742196 · PLoS computational biology · 2009 · 8 claims · 4 setups

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  MIP active learning is a novel active learning method that preferentially seeks informative Positive (functionally active) examples rather than only maximizing classifier accuracy.

  engineered p53 computational yeast-based regional homology