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).

• Detection of alternative splicing: deep sequencing or deep learning?

  PMID 41520225 · PMC12790623 · Briefings in bioinformatics · 2026 · 8 claims · 8 setups

  Full-text index only

  Sequence-based deep learning tools (AlphaGenome, SpliceAI, DeepSplice) show potential for initial hypothesis development and as additional filters in standard RNA-seq pipelines, especially when sequencing depth is limited.

  dilated dcm sequence subsampled bulk rna-seq deep

• Prediction of Alzheimer's disease-specific phospholipase c gamma-1 SNV by deep learning-based approach for high-throughput screening.

  PMID 33397809 · PMC7826347 · Proceedings of the National Academy of Sciences of the United States of America · 2021 · 7 claims · 7 setups

  Has reproduction · 48

  An AD-specific frameshift insertion SNV in exon 27 of mouse PLCγ1 causes abnormal exon skipping (exons 26-30) during mRNA maturation in 5xFAD cortex

  cortex forebrain plcγ1 bulk rna-seq-based western

• Adaptive learning embedding features to improve the predictive performance of SARS-CoV-2 phosphorylation sites.

  PMID 37847658 · PMC10628388 · Bioinformatics (Oxford, England) · 2023 · 8 claims · 6 setups

  Has reproduction · 67

  PSPred-ALE outperforms state-of-the-art SARS-CoV-2 phosphorylation site predictors (e.g. DeepIPs) and handcrafted feature-based methods in benchmarking comparisons

  a549 sequence same bulk pathway gene

• Colorectal microenvironment determines the prognosis of colorectal cancer.

  PMID 41495419 · PMC12868731 · Experimental & molecular medicine · 2026 · 8 claims · 7 setups

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  The colorectal microenvironment (classified via NBT gene expression as tumor-supportive vs healthy) can serve as a prognostic biomarker predicting cancer invasiveness and recurrence

  paired nbt slide bulk bulk 16s single-cell

• A generic reference defined by consensus peaks for single-cell ATAC-seq data analysis.

  PMID 41663439 · PMC12996591 · Nature communications · 2026 · 7 claims · 7 setups

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  Aggregating peaks from 624 high-quality bulk ATAC-seq datasets defines ~1.4 million observed consensus peaks (cPeaks) covering ~30% of the genome.

  bulk scatac-seq cnn gm12878 bulk scatac-seq deep

• Genetic alternative splicing regulation mapping of cartilage and synovium reveals tissue-specific mechanisms of joint-related traits.

  PMID 41820402 · PMC13121699 · Nature communications · 2026 · 8 claims · 8 setups

  Full-text index only

  Generated a splicing quantitative trait loci (sQTL) resource for human cartilage and synovium from over 200 donors

  degenerated synovium matched paired bulk genotyping differential

• The evolution of gene regulation in mammalian cerebellum development.

  PMID 41610256 · PMC7618896 · Science (New York, N.Y.) · 2026 · 8 claims · 8 setups

  Full-text index only

  Combined single-nucleus RNA-seq and ATAC-seq atlases of cerebellum development were generated/integrated across six mammals (human, bonobo, macaque, marmoset, mouse, opossum)

  developing opossum marmoset cultures single-nucleus bulk gene

• Accelerating rare disease diagnostics by linking DNA and RNA through an explainable and interactive RNA-guided workflow.

  PMID 41685349 · PMC12891912 · NAR genomics and bioinformatics · 2026 · 8 claims · 6 setups

  Has reproduction

  A streamlined RNA-guided workflow combining OUTRIDER, FRASER, Borzoi, and MOLGENIS VIP was developed to identify gene-disease associations by linking outlier gene expression/splicing to prioritized patient-level variants

  udn blood clinvar wgs wgs bulk rna-seq

• Annotation-free prediction of immunotherapy response in melanoma using single-cell transcriptomic data.

  PMID 41758825 · PMC12948085 · PloS one · 2026 · 8 claims · 6 setups

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  AI-based predictive models built on unannotated scRNA-seq data (cell-by-gene expression matrices) can classify melanoma patients as ICI responders vs. non-responders

  cd45+ scrna-seq independent scrna-seq single-gene-level pathway-level