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Titlebook: Computational Advances in Bio and Medical Sciences; 11th International C Mukul S. Bansal,Ion Măndoiu,Alexander Zelikovsky Conference procee

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Single Model Quality Estimation of Protein Structures via Non-negative Tensor Factorizationering tertiary structures of a protein has been leveraged to build representations of the structure-energy landscape, highlight stable and semi-stable structural states, support models of structural dynamics, and connect them to biological function. Over the years, our laboratory has introduced meth
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Excerno: Filtering Mutations Caused by the Clinical Archival Process in Sequencing Dataancer patients. Clinical tests use archival pathology slides, which are preserved by Formalin-Fixation Paraffin Embedding (FFPE). The FFPE process introduces spurious C > T mutations hindering accurate cancer diagnosis..FFPE mutational artifacts occur in a well-defined pattern called a mutational si
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MELEPS: Multiple Expert Linear Epitope Prediction System fishery farming environments are vulnerable to bacteria or viruses and would cause serious losses. Predicting epitope binding segments from pathogenic bacteria is the first step for vaccine and drug development, and bioinformatics technologies could provide effective approaches to facilitate effect
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Encoder-Decoder Architectures for Clinically Relevant Coronary Artery Segmentationof global deaths every year. However, the images acquired in these procedures have low resolution and poor contrast, making lesion detection and assessment challenging. Accurate coronary artery segmentation not only helps mitigate these problems, but also allows the extraction of relevant anatomical
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Feature Selection for Identification of Risk Factors Associated with Infant Mortalityncrease the interpretability of data and explanation of the studied phenomenon. In this paper, we developed a Machine Learning approach to identify the main risk factors that impact the local population studied with regard to infant mortality, aiming to help professionals who deal directly with the
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Addressing Classification on Highly Imbalanced Clinical Datasetsegression, clustering and dimensionality reduction techniques have been widely used in clinical studies to assist health professionals in screening, risk estimation, diagnostics and prognostics. Prospective studies often involve a long follow-up period and a large sample, therefore many investigatio
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