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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CellPredX, a computational framework for cross-data type, cross-sample, and cross-protocol cell type annotation through domain adaptation and deep metric learning.
PMID 41481570 · PMC12758788 · PLoS computational biology · 2026 · 8 claims · 7 setups
CellPredX is a unified semi-supervised framework integrating domain adaptation and deep metric learning to align heterogeneous embeddings for cross-modality cell type annotation.
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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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A generic reference defined by consensus peaks for single-cell ATAC-seq data analysis.
PMID 41663439 · PMC12996591 · Nature communications · 2026 · 7 claims · 7 setups
Aggregating peaks from 624 high-quality bulk ATAC-seq datasets defines ~1.4 million observed consensus peaks (cPeaks) covering ~30% of the genome.
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Partially shared multi-modal embedding learns holistic representation of cell state.
PMID 41741805 · PMC13021527 · Nature computational science · 2026 · 8 claims · 5 setups
APOLLO automatically learns partial information sharing between multiple data modalities using an autoencoder with a partially overlapping latent space trained via latent optimization.