Computational Intelligence Methods for Bioinformatics and Biostatistics
portes grátis
Computational Intelligence Methods for Bioinformatics and Biostatistics
19th International Meeting, CIBB 2024, Benevento, Italy, September 4-6, 2024, Revised Selected Papers
Cerulo, Luigi; Pagnotta, Stefano M.; Cheng, Lu; Occhipinti, Annalisa; Napolitano, Francesco; Bardozzo, Francesco
Springer International Publishing AG
05/2025
321
Mole
Inglês
9783031897030
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Bioinformatics.- Clustering-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.- Medical Informatics.- Private, 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.- Natural language processing (NLP) and large language models (LLM) for unstructured data in health informatics.- Driver 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.- Modeling and simulation methods for computational biology and systems medicine.- Gene 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.- Machine learning for structured data in clinical informatics and medical biology.- Forward 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.- Computational Intelligence in Personalized Medicine.- Group 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.- Computational Structural Bioinformatics.- ESMCrystal : Enhancing Protein Crystallization Prediction through Protein Embeddings.- TARNAS, a TrAnslator for RNA Secondary structure formats.- Short papers.- Novel Approaches for Spatially Resolving Gene Responses and Injection Site Localization in Transcriptomic Data.- Deep Learning Approaches for Forensics DNA Profiling: a Replication Study.
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bioinformatics;computational biology;biostatistics;artificial intelligence;medical informatics;computational intelligence;Biological data analysis;genomics;transcriptomics;proteomics;gene regulatory networks;systems biology;computational drug discovery;health informatics
Bioinformatics.- Clustering-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.- Medical Informatics.- Private, 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.- Natural language processing (NLP) and large language models (LLM) for unstructured data in health informatics.- Driver 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.- Modeling and simulation methods for computational biology and systems medicine.- Gene 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.- Machine learning for structured data in clinical informatics and medical biology.- Forward 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.- Computational Intelligence in Personalized Medicine.- Group 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.- Computational Structural Bioinformatics.- ESMCrystal : Enhancing Protein Crystallization Prediction through Protein Embeddings.- TARNAS, a TrAnslator for RNA Secondary structure formats.- Short papers.- Novel Approaches for Spatially Resolving Gene Responses and Injection Site Localization in Transcriptomic Data.- Deep Learning Approaches for Forensics DNA Profiling: a Replication Study.
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