Deep learning-based computational approach for predicting ncRNAs-disease associations in metaplastic breast cancer diagnosis
BMC Cancer
volume 25, Article number: 830 (2025)
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Non-coding RNAs (ncRNAs) play a crucial role in breast cancer progression, necessitating advanced computational approaches for precise disease classification. This study introduces a Deep Reinforcement Learning (DRL)-based framework for predicting ncRNA–disease associations in metaplastic breast cancer (MBC) using a multi-dimensional descriptor system (ncRNADS) integrating 550 sequence-based features and 1,150 target gene descriptors (miRDB score ≥ 90). The model achieved 96.20% accuracy, 96.48% precision, 96.10% recall, and a 96.29% F1-score, outperforming traditional classifiers such as support vector machines (SVM) and neural networks. Feature selection and optimization reduced dimensionality by 42.5% (4,430 to 2,545 features) while maintaining high accuracy, demonstrating computational efficiency. External validation confirmed model specificity to breast cancer subtypes (87–96.5% accuracy) and minimal cross-reactivity with unrelated diseases like Alzheimer’s (8–9% accuracy), ensuring robustness. SHAP analysis identified key sequence motifs (e.g., “UUG”) and structural free energy (ΔG = − 12.3 kcal/mol) as critical predictors, validated by PCA (82% variance) and t-SNE clustering. Survival analysis using TCGA data revealed prognostic significance for MALAT1, HOTAIR, and NEAT1 (associated with poor survival, HR = 1.76–2.71) and GAS5 (protective effect, HR = 0.60). The DRL model demonstrated rapid training (0.08 s/epoch) and cloud deployment compatibility, underscoring its scalability for large-scale applications. These findings establish ncRNA-driven classification as a cornerstone for precision oncology, enabling patient stratification, survival prediction, and therapeutic target identification in MBC.
Ahmad, S., Zafar, I., Shafiq, S. et al. Deep learning-based computational approach for predicting ncRNAs-disease associations in metaplastic breast cancer diagnosis.
BMC Cancer 25, 830 (2025). https://doi.org/10.1186/s12885-025-14113-z
https://doi.org/10.1186/s12885-025-14113-z bu içeriği en az 2500 kelime olacak şekilde ve alt başlıklar ve madde içermiyecek şekilde ünlü bir science magazine için İngilizce olarak yeniden yaz. Teknik açıklamalar içersin ve viral olacak şekilde İngilizce yaz. Haber dışında başka bir şey içermesin. Haber içerisinde en az 14 paragraf ve her bir paragrafta da en az 50 kelime olsun. Cevapta sadece haber olsun. Ayrıca haberi yazdıktan sonra içerikten yararlanarak aşağıdaki başlıkların bilgisi var ise haberin altında doldur. Eğer bilgi yoksa ilgili kısmı yazma.:
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Ahmad, S., Zafar, I., Shafiq, S. et al. Deep learning-based computational approach for predicting ncRNAs-disease associations in metaplastic breast cancer diagnosis.
BMC Cancer 25, 830 (2025). https://doi.org/10.1186/s12885-025-14113-z
Image Credits: Scienmag.com
DOI: https://doi.org/10.1186/s12885-025-14113-z
Keywords
Tags: advanced cancer diagnosticsbreast cancer progression factorscancer classification accuracy improvementcomputational biology methodsdeep learning in cancer diagnosisdeep reinforcement learning applicationsfeature selection in machine learningmetaplastic breast cancer researchmulti-dimensional descriptor systemsncRNA disease associationsnon-coding RNA significancepredictive modeling in oncology
