{"product_id":"9783031897030","title":"Computational Intelligence Methods for Bioinformatics and Biostatistics : 19th International Meeting, CIBB 2024, Benevento, Italy, September 4-6, 2024, Revised Selected Papers (Lecture Notes in Computer Science 15276) (2025. xx, 328 S. XX, 328 p. 102 illu","description":"\u003cp\u003eThis volume LNCS 15276 constitutes the revised selected papers of the 19th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2024, held in Benevento, Italy, during September 4 6, 2024. \u003c\/p\u003e\u003cp\u003eThe 24 full papers and 3 short papers were carefully reviewed and selected from 28 submissions. They were organized in the following topical sections: Bioinformatics; Medical Informatics; Natural Language Processing (NLP) and Large Language Models (LLM) for Unstructured Data in Health Informatics; Modeling and Simulation Methods for Computational Biology and Systems Medicine; Machine Learning for Structured Data in Clinical Informatics and Medical Biology; Computational Intelligence in Personalized Medicine; and Computational Structural Bioinformatics.\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eBioinformatics.- \u003c\/strong\u003eClustering-based Negative Sampling Approaches for Protein-Protein Interaction Prediction.- Proteins transcription factor prediction using Graph Neural Networks.- Identification of Differential Alternative Splicing Events: Assessing Tools Performance with Different Sequencing Parameters.- Methods and tools to facilitate RE:IN modeling and analysis of GRNs.- Gene set-focused analysis of RNA-seq data with MIEP (Make-It-Easy-Pipeline).- Cross sequencing integration of compositional microbiome data in cancer.- \u003cstrong\u003eMedical Informatics.- \u003c\/strong\u003ePrivate, Efficient and Scalable Kernel Learning for Medical Image Analysis.- Toward a Unified Graph-Based Representation of Medical Data for Precision Oncology Medicine.- FP-Elegans M1: feature pyramid reservoir connectome transformers and multi-backbone feature extractors for MEDMNIST2D-V2.- \u003cstrong\u003eNatural language processing (NLP) and large language models (LLM) for unstructured data in health informatics.- \u003c\/strong\u003eDriver Gene Detection via Causal Inference on Single Cell Embeddings.- Assessing and Comparing Free Large Language Models Responses to a Clinical Case: Accuracy, Safety, and Reliability.- Three-stage Data Science methodology to explore genetic heterogeneity of diseases.- Functional data analysis and clustering of haematological parameters in SARS-CoV-2 patients.- \u003cstrong\u003eModeling and simulation methods for computational biology and systems medicine.- \u003c\/strong\u003eGene set optimization for single cell transcriptomics.- MicroRNAs as biomarkers for Ulcerative Colitis.- PHeP: TrustAlert Open-Source Platform for Enhancing Predictive Healthcare with Deep Learning.- Cutting Slices of Complexity in Cancer Therapy Design: An Agent-Based Model of Dabrafenib in Melanoma.- \u003cstrong\u003eMachine learning for structured data in clinical informatics and medical biology.- \u003c\/strong\u003eForward and backward feature selection guided by prior biological knowledge for enhanced interpretability.- The impact of mis-labeled artefacts on deep learning models for EEG analysis: a case study.- Benchmark study on supervised Relevance-Redundancy assessment for feature selection in genomic data.- \u003cstrong\u003eComputational Intelligence in Personalized Medicine.- \u003c\/strong\u003eGroup discovery in a clinical database of patients with psychosis who have undergone Metacognitive Training.- Hierarchical Clustering with an Ensemble of Principle Component Trees for Interpretable Patient Stratification.- \u003cstrong\u003eComputational Structural Bioinformatics.- \u003c\/strong\u003eESMCrystal : Enhancing Protein Crystallization Prediction through Protein Embeddings.- TARNAS, a TrAnslator for RNA Secondary structure formats.- \u003cstrong\u003eShort papers.- \u003c\/strong\u003eNovel Approaches for Spatially Resolving Gene Responses and Injection Site Localization in Transcriptomic Data.- Deep Learning Approaches for Forensics DNA Profiling: a Replication Study.\u003c\/p\u003e","brand":"SPRINGER, BERLIN; SPRINGER NATURE SWITZERLAND; SPRING","offers":[{"title":"Default Title","offer_id":48865607942379,"sku":"00000_00000_00000_00000","price":137.32,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0758\/4484\/5803\/files\/9783031897030-1.jpg?v=1781717900","url":"https:\/\/kinokuniya.com.sg\/products\/9783031897030","provider":"Books Kinokuniya Singapore","version":"1.0","type":"link"}