{"product_id":"emerging-trends-in-computational-biology-biomedical-engineering-and-health-infor-ebook","title":"Emerging Trends in Computational Biology, Biomedical Engineering, and Health Informatics","description":"\u003cp\u003e\u003cstrong\u003e.- 26th International Conference on Bioinformatics \u0026amp; Computational Biology (BIOCOMP'25)\u003c\/strong\u003e\u003cbr\u003e.- Self-supervised Multimodal Pre-training for Lung Adenocarcinoma Overall Survival Prediction.\u003cbr\u003e.- Robust Unsupervised Classification of Drosophila Cell Dynamics Using DTW and Feature-Based Cluster Consensus.\u003cbr\u003e.- Risk Factors for the Development of Early Onset Cancers.\u003cbr\u003e.- Integrating Pharmacokinetics and Pharmacodynamics Modeling with Quantum Regression for Predicting Herbal Compound Toxicity.\u003cbr\u003e.- Neural Network Benchmarking on ECG Classification.\u003cbr\u003e.- BlurFisheye: A Technique for Enhanced Biological Network Visualization.\u003cbr\u003e.- Leveraging Local LLMs for Taxonomy-Aware Analysis of STRING-Supported Species.\u003cbr\u003e.- Detecting the Most Relevant Slices in MRI Parkinson Disease for Multi-Classification Using Evolutionary Algorithm.\u003cbr\u003e.- A Neural Network Approach for Predicting Cancer-Linked Transcriptional Activity from Epigenetic Signature.\u003cbr\u003e\u003cstrong\u003e.- 11th International Conference on Biomedical Engineering \u0026amp; 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