What question did this study set out to answer?

To develop lncAPNet for elucidating lncRNA and mRNA interactions in transcriptomic data.

December 22, 2025Open Access

lncAPNet enables the deciphering of lncRNA and mRNA connections in patient transcriptomic data

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To develop lncAPNet for elucidating lncRNA and mRNA interactions in transcriptomic data.
Integrated graph based nonlinear inference for lncRNA-mRNA interactions.
Used NetBID2 activity logic within a coexpression network.
Implemented a biologically informed sparse deep learning model (PASNet).
Identified lncRNA drivers associated with cancer progression.
Demonstrated application in Chronic Lymphocytic Leukemia and Prostate Adenocarcinoma case studies.

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Implication

Machine learning identifies lncRNA drivers of cancer progression in Chronic Lymphocytic Leukemia and Prostate Adenocarcinoma, suggesting novel biological insights.

Cite This Study

Vasileiou et al. (2025) studied this question.

synapsesocial.com/papers/69488bc877063b71e748cfd6 https://doi.org/10.64898/2025.12.18.695074

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Implication

Machine learning identifies lncRNA drivers of cancer progression in Chronic Lymphocytic Leukemia and Prostate Adenocarcinoma, suggesting novel biological insights.

lncAPNet enables the deciphering of lncRNA and mRNA connections in patient transcriptomic data

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Discussion

Implication

Cite This Study

lncAPNet enables the deciphering of lncRNA and mRNA connections in patient transcriptomic data

Key Points

Discussion

Implication

Cite This Study

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