Titlebook: Bioinformatics Research and Applications; 16th International S Zhipeng Cai,Ion Mandoiu,Xuan Guo Conference proceedings 2020 Springer Nature

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Isoform-Disease Association Prediction by Data Fusion,ion term to dispatch gene-disease associations to individual isoforms, and reversely aggregate these dispatched associations to affiliated genes. Next, it fuses different genomics and transcriptomics data to replenish gene-disease associations and to induce a linear classifier for predicting isoform

赏心悦目

EpIntMC: Detecting Epistatic Interactions Using Multiple Clusterings,a and reduce the chance of filtering out potential candidates overlooked by a single clustering. In the search stage, EpIntMC applies Entropy score to screen SNPs in each cluster, and uses Jaccard similarity to merge the most similar clusters into candidate sets. After that, EpIntMC uses exhaustive

MARS

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Ess-NEXG: Predict Essential Proteins by Constructing a Weighted Protein Interaction Network Based ontial proteins. In Ess-NEXG, we construct a reliable weighted network by using these data. Then we use the node2vec technique to capture the topological features of proteins in the constructed weighted PPI network. Last, the extracted features of proteins are put into a machine learning classifier t

Bone-Scan

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SVLR: Genome Structure Variant Detection Using Long Read Sequencing Data,e classic structural variants that can be detected by state-of-the-art methods (e.g., SVIM and Sniffles), our experiments demonstrate recall improvements of up-to . without harming the precisions (i.e., above .). We also point out three directions to further improve structural variant detection in t

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Prediction of Drug-Target Interaction via Laplacian Regularized Schatten-p Norm Minimization,ew drug/target cases by combining the loss function with a Laplacian regularization term. Finally, we numerically solve the LRSpNM model by an efficient alternating direction method of multipliers (ADMM) algorithm. Performance evaluations on benchmark datasets show that LRSpNM achieves better and mo

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