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While computational deconvolution offers a scalable alternative, current deep learning methods predominantly operate as \u201cblack boxes,\u201d neglecting the structural constraints of biological laws. This reliance on purely data-driven feature extraction often yields biologically incoherent predictions and limited mechanistic interpretability. iDCF (\n                    <jats:bold>I<\/jats:bold>\n                    nterpretable\n                    <jats:bold>D<\/jats:bold>\n                    econvolution of\n                    <jats:bold>C<\/jats:bold>\n                    ell\n                    <jats:bold>F<\/jats:bold>\n                    ractions) is a novel framework that enforces biological topology onto deep neural networks. The iDCF architecture employs a dual-stream design, synergizing a standard deep network with a knowledge-based sparse neural network (KSNN) explicitly masked by pathway definitions and protein-protein interaction (PPI) networks. In comprehensive benchmarks, iDCF achieves top-tier performance, consistently ranking among state-of-the-art methods in accuracy and robustness. iDCF integrates the SHapley Additive exPlanations (SHAP) framework, bridging the gap between computational inference and biological intuition. The model\u2019s decision logic is governed by established biological mechanisms rather than spurious statistical correlations, validating its reliability. Validations across clinical contexts, including Alzheimer\u2019s disease, ovarian cancer, and diabetes, demonstrate iDCF\u2019s ability to recover disease-relevant cellular dynamics. iDCF offers a high-performance, interpretable, and biologically grounded tool for deconvolving cell-type proportions, facilitating deeper insights into tissue heterogeneity in health and disease.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1014727","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T17:45:46Z","timestamp":1787852746000},"page":"e1014727","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["iDCF: Interpretable deconvolution of cell fractions via biologically-informed deep learning using scRNA-seq data"],"prefix":"10.1371","volume":"22","author":[{"given":"Hongjiang","family":"Guo","sequence":"first","affiliation":[{"name":"School of Computer Science and 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