Titlebook: Big Data Analytics in Genomics; Ka-Chun Wong Book 2016 Springer International Publishing Switzerland (Outside the USA) 2016 Big Data.Genom

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書目名稱Big Data Analytics in Genomics影響因子(影響力)




書目名稱Big Data Analytics in Genomics影響因子(影響力)學科排名




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書目名稱Big Data Analytics in Genomics網(wǎng)絡公開度學科排名




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書目名稱Big Data Analytics in Genomics被引頻次學科排名




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書目名稱Big Data Analytics in Genomics年度引用學科排名




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書目名稱Big Data Analytics in Genomics讀者反饋學科排名

gospel

Causal Inference and Structure Learning of Genotype–Phenotype Networks Using Genetic Variation–phenotype relations. We discuss four recent algorithms for genotype–phenotype network structure learning, namely (1) QTL-directed dependency graph, (2) QTL+Phenotype supervised orientation, (3) QTL-driven phenotype network, and (4) sparsity-aware maximum likelihood (SML).

團結

State-of-the-Art in Smith–Waterman Protein Database Search on HPC Platforms implementation. Additionally, as energy efficiency is becoming more important every day, we also survey performance/power consumption works. Finally, we give our view on the future of Smith–Waterman protein searches considering next generations of hardware architectures and its upcoming technologie

大吃大喝

apiary

inquisitive

Perspectives of Machine Learning Techniques in Big Data Mining of Cancerional process of various genes identified by different genomics efforts. This might be useful to understand the modern trends and strategies of the fast evolving cancer genomics research. In the recent years, parallel, incremental, and multi-view machine learning algorithms have been proposed. This

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令人心醉

A Bioinformatics Approach for Understanding Genotype–Phenotype Correlation in Breast Cancererns, which can assign known phenotypes to BC TN patients, focusing more on paired or more complicated nucleotide/gene mutational patterns, by using three machine learning methods: limitless arity multiple procedure (LAMP), decision trees, and hierarchical disjoint clustering. Association rules obta

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