'Computational biology' diaporamas de présentation

Chapter 6 Dynamic Programming

Chapter 6 Dynamic Programming. Algorithmic Paradigms. Greed. Build up a solution incrementally , only optimizing some local criterion.

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1.09k views • 71 slides

Module A: Fundamental Algorithms in Sequence Analysis

Module A: Fundamental Algorithms in Sequence Analysis. Section 1: Sequence Alignments Srinivas Aluru. Biology easily has 500 years of exciting problems to work on -Donald E. Knuth. Biological Data. DNA: Self-replicating

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A Whirlwind Tour of Bioinformatics

A Whirlwind Tour of Bioinformatics. Kun-Mao Chao ( ??? ) National Taiwan University http://www.csie.ntu.edu.tw/~kmchao/. The Best? The Cheapest?. The Best. Entrance. The Cheapest. Bio-X? X-Informatics?. Bio-X. Bioinformatics. X-Informatics. Source: NIH, Bioinformatics Journal, NPS.

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BIOINFORMATICS OF AVIAN INFLUENZA VIRUS

BIOINFORMATICS OF AVIAN INFLUENZA VIRUS. GROUP 4 Yu Hai Dong Tay Hwee Goon Ling Wen Wan Felicia Loe Loh Shin Shion Clarice Chen Bai Hui Fen Low Soon Wah. INTRODUCTION. Where can one read up more about the Bird Flu?.

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Advanced Computing Resources for IU Researchers

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???? bioinformatics. ???. Bioinformatics. ?? information. ?? (information) ?????????????????????????????????????? ????????????????????????????????????????? . What is Bioinformatics?. Computer Science. Mathematics and Statistics. ????? ???? ??? ???? ????? ????? ????? ????

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ICML 2001 Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

ICML 2001 Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. John Lafferty, Andrew McCallum, Fernando Pereira Presentation by Rongkun Shen Nov. 20, 2003. Sequence Segmenting and Labeling. Goal: mark up sequences with content tags

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ICML 2001 Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

ICML 2001 Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. John Lafferty, Andrew McCallum, Fernando Pereira Presentation by Rongkun Shen Nov. 20, 2003. Sequence Segmenting and Labeling. Goal: mark up sequences with content tags

450 views • 28 slides

Research in Slovenia

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Representation and Reasoning with Graphical Models

Representation and Reasoning with Graphical Models. Rina Dechter Information and Computer Science, UC-Irvine, and Radcliffe Institue of Advanced Study, Cambridge. Outline. Introduction to reasoning in AI Graphical models Constraint networks Probabilistic networks Graph-based reasoning.

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Oklahoma and Kansas BRICNET: a B ioinformatics R esearch I nspired C yber NET work

Oklahoma and Kansas BRICNET: a B ioinformatics R esearch I nspired C yber NET work. Presenter: Rakesh Kaundal Oklahoma State University Oklahoma EPSCoR Track II Oklahoma State Regents for Higher Education Office, Oklahoma City Wednesday November 16, 2011.

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Pharmaceutical Informatics and Computer-Aided Drug Discovery Sangtae Kim Executive Director, Morgridge Institute for Res

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Comparison of Phenylalanine v. Tyrosine rotamers using 3D modeling software

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Advanced Mathematical Methods

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Advanced Mathematical Methods

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Research Alliance in Math and Science

Research Alliance in Math and Science. Debbie McCoy Computing and Computational Sciences. Research Alliance in Math and Science (RAMS) Program.

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Choosing a Research Topic

Choosing a Research Topic. Patrice Koehl Computer Science, UC Davis. Research. Finding a research topic Finding an advisor Doing Research. Research. Finding a research topic Finding an advisor Doing Research. Research Topic. Research Topic. Who are you?

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Introduction to the R language

Introduction to the R language. Mathew Plucinski University of Cambridge / UC Berkeley. What is it. A free version of S+ Statistical programming Used extensively in computational biology, to a lesser extent in applied statistics, financial mathematics Interpretive language. Outline.

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Markov Logic and Deep Networks

Markov Logic and Deep Networks. Pedro Domingos Dept. of Computer Science & Eng. University of Washington. Weight of formula i. No. of true instances of formula i in x. Markov Logic Networks. Basic idea: Use first-order logic to compactly specify large non-i.i.d. models

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