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

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Improving Metagenomic Classification Using Discriminative k-mers from Sequencing Data,library with novel discriminative k-mers from the input sequencing reads. We evaluated the performance in different conditions against several other tools and the results showed an improved F-measure, especially when close reference genomes are not available..Availability:

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Cancer Breakpoint Hotspots Versus Individual Breakpoints Prediction by Machine Learning Models, criterion of hotspot selection for all cancer types but there are three to four distinct groups of cancer with similar properties. Overall the presented results point to the necessity to choose different hotspots selection criteria for different types of cancer.

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Conference proceedings 2020ssia, in December 2020.. The 23 full papers and 18 short papers presented in this book were carefully reviewed and selected from 131 submissions. They were organized in topical sections named: genome analysis; systems biology; computational proteomics; machine and deep learning; and data analysis an

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Warenbuchungen (ohne Umsatzsteuer), (2) devise an exact algorithm for decisiveness, (3) develop problem reduction rules, and use them to obtain efficient algorithms for inputs with few loci, and (4) devise an integer linear programming formulation of the decisiveness problem, which allows us to analyze data sets that arise in practice.

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Checking Phylogenetic Decisiveness in Theory and in Practice, (2) devise an exact algorithm for decisiveness, (3) develop problem reduction rules, and use them to obtain efficient algorithms for inputs with few loci, and (4) devise an integer linear programming formulation of the decisiveness problem, which allows us to analyze data sets that arise in practice.

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0302-9743 Moscow, Russia, in December 2020.. The 23 full papers and 18 short papers presented in this book were carefully reviewed and selected from 131 submissions. They were organized in topical sections named: genome analysis; systems biology; computational proteomics; machine and deep learning; and data a

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https://doi.org/10.1007/978-3-8351-9029-0mutations collected in public databases such as PhyloTree. Experimental results on real and simulated datasets show that our mutation analysis methods have accuracy comparable to that of state-of-the-art methods based on haplogroup frequency estimation for both single-individual samples and two-individual mixtures, with a much lower running time.

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