Computational intelligence methods f...
CIBB (Meeting) (2021 :)

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  • Computational intelligence methods for bioinformatics and biostatistics = 17th International Meeting, CIBB 2021, virtual event, November 15-17, 2021 : revised selected papers /
  • 紀錄類型: 書目-電子資源 : Monograph/item
    正題名/作者: Computational intelligence methods for bioinformatics and biostatistics/ edited by Davide Chicco ... [et al.].
    其他題名: 17th International Meeting, CIBB 2021, virtual event, November 15-17, 2021 : revised selected papers /
    其他題名: CIBB 2021
    其他作者: Chicco, Davide.
    團體作者: CIBB (Meeting)
    出版者: Cham :Springer International Publishing : : 2022.,
    面頁冊數: xv, 253 p. :ill. (chiefly color), digital ;24 cm.
    內容註: Chemical Neural Networks and Synthetic Cell Biotechnology: Preludes to Chemical AI -- Development of Bayesian network for multiple sclerosis risk factor interaction analysis -- Real-Time Automatic Plankton Detection, Tracking and Classification on Raw Hologram -- The first in-silico model of leg movement activity during sleep -- Transfer learning and magnetic resonance imaging techniques for deep neural network-based diagnosis of early cognitive decline and dementia -- Improving bacterial sRNA identification by combining genomic context and sequence-derived features -- High-dimensional multi-trait GWAS by reverse prediction of genotypes using machine learning methods -- A Non-Negative Matrix Tri-Factorization based Method for Predicting Antitumor Drug Sensitivity -- A Rule-based Approach for Generating Synthetic Biological Pathways -- Machine Learning Classifiers based on Dimensionality Reduction Techniques for the Early Diagnosis of Alzheimer's Disease using Magnetic Resonance Imaging and Positron Emission Tomography Brain Data -- Text Mining Enhancements for Image Recognition of Gene Names and Gene Relations -- Sentence Classification to Detect Tables for Helping Extraction of Regulatory Interactions in Bacteria -- RF-Isolation: a Novel Representation of Structural Connectivity Networks for Multiple Sclerosis Classification -- Summarizing Global SARS-CoV-2 Geographical Spread by Phylogenetic Multitype Branching Models -- Explainable AI Models for COVID-19 Diagnosis using CT-Scan Images and Clinical Data -- The need of standardised metadata to encode causal relationships: Towards safer data-driven machine learning biological solutions -- Deep Recurrent Neural Networks for the Generation of Synthetic Coronavirus Spike Protein Sequences -- Recent Dimensionality Reduction Techniques for High-Dimensional COVID-19 Data -- Soft brain ageing indicators based on light-weight LeNet-like neural networks and localized 2D brain age biomarkers.
    Contained By: Springer Nature eBook
    標題: Computational intelligence - Congresses. -
    電子資源: https://doi.org/10.1007/978-3-031-20837-9
    ISBN: 9783031208379
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