Association Analysis Techniques and Applications in Bioinformatics - Chen, Qingfeng
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Présentation Association Analysis Techniques And Applications In Bioinformatics Format Relié
- Livre Manga
Résumé :
Chapter1:Computer science for Molecular biology.- Chapter2:Introduction to association analysis.- Chapter3:Introduction to computational linguistics and biology structure.- Chapter4:Matrix decomposition for dimensionality deduction.- Chapter5:Discovering conserved RNA secondary structures with structure similarity.- Chapter6:Gene ontology for non-coding RNAs classification.- Chapter7:Learning frequent sub-structure by graph mining.- Chapter8:Editing distance and its application to biology graph analytics.- Chapter9:Sequence assembly and applications.- Chapter10:Classifying protein structures by measuring structural similarity.- Chapter11:Identification of metabolic pathways with embedding network.- Chapter12:Emerging Knowledge integration-based approach with multi-sources data for bioinformatics.- Chapter13:Conclusion and Future Work....
Biographie:
Qingfeng Chen received his BSc and MSc degrees in Mathematics from Guangxi Normal University, China, in 1995 and 1998, respectively, and his PhD in Computer Science from the University of Technology Sydney in September 2004. He is currently a Professor at Guangxi University, China and an honorary research fellow at La Trobe University, Australia. He has published many papers in top venues for machine learning and artificial intelligence, serves as an associate editor for top journals including Complexity & Intelligent systems, and has co-chaired several international conferences. ...
Sommaire:
Chapter1:Computer science for Molecular biology.- Chapter2:Introduction to association analysis.- Chapter3:Introduction to computational linguistics and biology structure.- Chapter4:Matrix decomposition for dimensionality deduction.- Chapter5:Discovering conserved RNA secondary structures with structure similarity.- Chapter6:Gene ontology for non-coding RNAs classification.- Chapter7:Learning frequent sub-structure by graph mining.- Chapter8:Editing distance and its application to biology graph analytics.- Chapter9:Sequence assembly and applications.- Chapter10:Classifying protein structures by measuring structural similarity.- Chapter11:Identification of metabolic pathways with embedding network.- Chapter12:Emerging Knowledge integration-based approach with multi-sources data for bioinformatics.- Chapter13:Conclusion and Future Work.