Deep learning-based computational approach for predicting ncRNAs-disease associations in metaplastic breast cancer diagnosis.
Saleem Ahmad, Imran Zafar, Shaista Shafiq, Laila Sehar, Hafsa Khalil, Nida Matloob, Mehvish Hina, Sidra Tul Muntaha, Hamid Khan, Najeeb Ullah Khan, Samreen Rana, Ahsanullah Unar, Muhammad Azmat, Muhammad Shafiq, Yousef A Bin Jardan
May 2025 BMC CancerSynopsis of Social media discussions
The collective discussions highlight the groundbreaking nature of using deep reinforcement learning for cancer diagnostics, with phrases like 'highly effective model' and 'significant step forward,' reflecting both enthusiasm and recognition of the research’s potential impact. The tone emphasizes innovation and clinical relevance, demonstrating interest and engagement that suggest this could influence future cancer diagnostics.
Agreement
Moderate agreementMost discussions acknowledge the significance of the deep learning approach and its promising results for diagnosing metaplastic breast cancer.
Interest
High level of interestParticipants show a high level of curiosity and enthusiasm, often emphasizing the innovative aspects of the research.
Engagement
Moderate level of engagementComments include detailed analysis of the model's performance, its genetic markers, and future implications, indicating moderate to deep engagement.
Impact
High level of impactThe discussions suggest that the research could have meaningful effects on personalized cancer treatment and early diagnosis approaches.
Social Mentions
YouTube
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50
Total Likes
7
Extended Reach
168
Social Features
2
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Metaplastic Breast Cancer Global Alliance
https://t.co/rfp3jCL1aL, 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://t.co/QKgHhWT58q
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Posts referencing the article
AI-Driven ncRNA Analysis Enhances Metaplastic Breast Cancer Diagnosis
Our DNA produces noncoding RNAs that regulate gene expression. Researchers developed a Deep Reinforcement Learning model to analyze ncRNA features, achieving over 96% accuracy in classifying metaplastic breast cancer and advancing personalized cancer diagnostics.
May 19, 2025
50 views
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https://t.co/rfp3jCL1aL, 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://t.co/QKgHhWT58q
view full postMay 21, 2025
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Abstract Synopsis
- This study presents a deep learning (specifically Deep Reinforcement Learning) model that predicts how non-coding RNAs (ncRNAs) are linked to metaplastic breast cancer (MBC), using detailed genetic features to improve accuracy in diagnosing specific cancer types.
- The model achieved high performance metrics, like 96.20% accuracy, and was more effective than traditional methods such as SVMs, while also being computationally efficient through feature reduction techniques.
- External tests confirmed that the model is specific to breast cancer, identifying key genetic motifs and prognostic markers like MALAT1 and HOTAIR linked to patient survival, making it a promising tool for personalized cancer treatment and research.]
Metaplastic Breast Cancer Global Alliance
@MpBCGA (Twitter)