AUC sensitivity of APDCA to the sparse control parameter <i>k</i> under 5-fold cross-validation.

AUC sensitivity of APDCA to the sparse control parameter <i>k</i> under 5-fold cross-validation.

<p>Performance peaks at <i>k</i> = 75 and remains stable across a wide range.</p>

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Bibliographic Details
Main Author:	Yangsong He (13909371) (author)
Other Authors:	Zheng-Jian Bai (22574779) (author), Wai-Ki Ching (4685404) (author), Quan Zou (157931) (author), Yushan Qiu (4685401) (author)
Published:	2025
Subjects:	Biochemistry Sociology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified result :</ b regulate alternative splicing rank matrix factorization motivation :</ b interactions may disrupt individual data sources heterogeneous biological data false positives associated existing models struggle computational methods predict affect tumor progression rbp qki shows new method based infer rbp – diseases like cancer limited known associations predicting rbp – highlights key associations p </ u dc </ u </ u predicting associations effective method cancer metastasis key role key feature uses extrapolation top predictions sparsity constraints sparsity constraint sparse settings single gene scalable tool proposed algorithm promoting changes optimization framework oncosplicing database mesenchymal transition improve performance improve convergence experimental results discovering post comprehensive view case study binding proteins
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