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

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Single Model Quality Estimation of?Protein Structures via?Non-negative Tensor Factorizatione its efficacy on the estimation of model accuracy (EMA) problem. The method outperforms state-of-the-art methods, including single-model methods that leverage deep neural networks and domain-specific insight.

exceptional

Graph Representation Learning for?Protein Conformation Samplingthat graph representation learning outperforms models based on image convolution. This work also equips graph-based deep latent variable models with the ability to learn from experimentally-available tertiary structures of proteins of varying lengths. The resulting models are shown to outperform sta

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MELEPS: Multiple Expert Linear Epitope Prediction Systemhe proposed system could achieve 69.40% and 60.54% respectively, while the average F1-Scores of the other six systems could only achieve 55.21% and 41.94%. The proposed multi-expert recommendation system outperforms individual linear epitope prediction systems.

fiction

Encoder-Decoder Architectures for?Clinically Relevant Coronary Artery Segmentationdes vessels that are not relevant for diagnosis. We adopt a better-suited clinical criterion and segment vessels according to their clinical relevance. Additionally, we simultaneously perform catheter segmentation, which may be useful for diagnosis due to the scale factor provided by the catheter’s

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說明

Addressing Classification on?Highly Imbalanced Clinical Datasetswe propose an original framework to assess several balancing techniques, combining them with 3 out-of-the-box classifiers. We applied the combination of techniques to the AVOCADO clinical study, which consists of a database of patient information including cardiovascular death or survival. Our resul

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mcPBWT: Space-Efficient Multi-column PBWT Scanning Algorithm for?Composite Haplotype Matchingolumns of PBWT respectively. . finds two matching pairs’ combinations representative of crossover event or phasing error while . finds three matching pairs’ combinations representative of gene-conversion tract.

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Clustering SARS-CoV-2 Variants from?Raw High-Throughput Sequencing Reads Dataerical representation, it can be passed to already highly optimized clustering methods such as .-means. We show that the clusterings we obtain with the proposed embeddings are more suited to this setting than the Pangolin tool, based on several internal clustering evaluation metrics. Moreover, we sh