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 novel deep learning-driven framework for improving lncRNA comprehensive annotation with LncADeep 2.0.

  PMID 41923359 · PMC13090826 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 8 setups

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  LncADeep 2.0 outperforms LncADeep and other existing tools for lncRNA identification on both GENCODE annotated transcripts and independent RNA-seq data

  lncrna hypertrophic gencode lncrna-protein-mirna mlp-based independent ablation

• GRIP-Lung: Generative Model of Response to Drug-Induced Perturbation in Lung Cancer.

  PMID 41977443 · PMC13072768 · International journal of molecular sciences · 2026 · 8 claims · 7 setups

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  GRIP-Lung, a GAN framework embedding cell-line identity and drug conditions, predicts drug-induced gene expression profiles in lung cancer cell lines

  lung nsclc a549 nci-60 bulk five-fold differential

• FLYNC: a machine-learning-driven framework for discovering long noncoding RNAs in Drosophila melanogaster.

  PMID 41551930 · PMC12805895 · NAR genomics and bioinformatics · 2026 · 7 claims · 8 setups

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  FLYNC, an explainable boosting machine (EBM) model, accurately predicts the probability that a newly identified RNA transcript in D. melanogaster is a lncRNA

  melanogaster drosophila automated ebm hyperparameter

• Towards precise classification of cancers based on robust gene functional expression profiles.

  PMID 15774002 · PMC1274255 · BMC bioinformatics · 2005 · 6 claims · 7 setups

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  Functional expression profiles (FEPs) achieve comparable or better classification performance than conventional gene expression profiles (GEPs) across four public microarray datasets

  nci60 lymphoma fep go-annotated cdna recursive hypergeometric

• 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

• Topologically inferring pathway activity toward precise cancer classification via integrating genomic and metabolomic data: prostate cancer as a case.

  PMID 26286638 · PMC4541321 · Scientific reports · 2015 · 6 claims · 6 setups

  Has reproduction · 76

  DRW-GM evaluates gene topological importance by directed random walk on a global gene–metabolite pathway graph integrating gene expression and metabolomic profiles to infer pathway activities

  prostate matched gse8511 gene metabolomic pathway

• Molecular Classification Models for Triple Negative Breast Cancer Subtype Using Machine Learning.

  PMID 34575658 · PMC8472680 · Journal of personalized medicine · 2021 · 7 claims · 4 setups

  Has reproduction · 86

  TNBC can be divided into four gene-expression-defined subtypes: BLIA, BLIS, MES, and LAR

  tnbc unique 719-gene gene term machine

• The functional landscape of alternative splicing in hematopoietic lineage commitment.

  PMID 41593078 · PMC12946277 · Nature communications · 2026 · 7 claims · 8 setups

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  FAScore, a Random Forest model integrating 19 dynamic, structural, and conservation features, predicts functional exon-skipping AS events with high accuracy in a species- and lineage-specific manner.

  umbilical bone fetal zebrafish bulk machine exon

• Neural network-assisted RNA velocity imputation for empowering transcript dynamics-based analyses.

  PMID 41736867 · PMC12927306 · iScience · 2026 · 8 claims · 8 setups

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  NARVI, a deep neural network trained on expression-velocity relationships of passed genes, can impute RNA velocity for dropped genes that conventional tools (e.g., scVelo, UniTVelo) fail to calculate

  hematopoietic lung erythroid bone scrna-seq cross-validation

• 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