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This research advances machine learning applications in genomics by refining soft-labeling techniques to scale DNA read classification from single cells to whole-body cell-type deconvolution. The study addresses critical methodological improvements, including correcting evaluation leakage and enhancing confidence-weighted loss functions for more robust biological inference. By providing open code and data, the work facilitates reproducibility and sets a new standard for integrating AI into large-scale genomic analysis.
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Data-Driven Soft Labeling Scales DNA Read Classification to Whole-Body Cell-Type Deconvolution

arXiv1 min read

arXiv:2607.04987v3 Announce Type: replace Abstract: Revised following peer review. We expanded baseline comparisons, corrected evaluation leakage and read-boundary handling, clarified the confidence-weighted loss, and added sensitivity analyses for pooling and region selection. We also expanded TCS failure-mode and limitations analyses, added a discussion section, and provided code and data links for reproducibility.

Abstract:Revised following peer review. We expanded baseline comparisons, corrected evaluation leakage and read-boundary handling, clarified the confidence-weighted loss, and added sensitivity analyses for pooling and region selection. We also expanded TCS failure-mode and limitations analyses, added a discussion section, and provided code and data links for reproducibility.

Machine Learning (cs.LG); Genomics (q-bio.GN); Quantitative Methods (q-bio.QM)

arXiv:2607.04987 [cs.LG]

(or arXiv:2607.04987v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.04987 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 144 words

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