ArticleCell proliferation2026
A Comprehensive Comparative Analysis of Sequence-Based Deep Learning Models for Single-Cell Genomics.
Article in Cell proliferation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
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Authors and funding
13 authors.
Funding
Abstract
To streamline the application of sequence-based DL methods in single-cell genomics, we established a two-layer CNN as our baseline model. We focus our benchmark on how data characteristics, hyperparameter optimization, and advanced model architectures impact performance across sequence-to-expression and sequence-to-regulation tasks. A key contribution of our study is the exploration of multi-task learning (MTL) frameworks for mitigate technical sparsity. We demonstrated that MTL significantly enhances the modeling of cellular heterogeneity, evaluating the effectiveness of task grouping and balancing strategies, with particular focus on the prediction of rare cell types. Our comprehensive comparative analysis provided an actionable framework and valuable insights for guiding future research endeavors and facilitating the development of the sequence-based DL models capable of superior predictive performance in single-cell genomics.
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.