'Computational biology' presentation slideshows

Computational biology - PowerPoint PPT Presentation


6.096 – Algorithms for Computational Biology Lecture 12

6.096 – Algorithms for Computational Biology Lecture 12

6.096 – Algorithms for Computational Biology Lecture 12. Biological Networks Microarrays – Expression Clustering – Bayesian nets – Small-world nets. 6.096 – Algorithms for Computational Biology – Lecture 9. Biological networks. Lecture 1 - Introduction Lecture 2 - Hashing / BLAST

By JasminFlorian
(313 views)

Module A: Fundamental Algorithms in Sequence Analysis

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

By elina
(189 views)

Markov Logic and Deep Networks

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

By paul
(389 views)

Advanced Mathematical Methods

Advanced Mathematical Methods

Advanced Mathematical Methods. COMP3006 Introduction to the course. Introduction. 2 sections Maths-Dr. Karen Page & Statistics –Dr. Simon Prince Maths until reading week. Course contact details. All communication concerning this course will be done via the email list.

By nile
(224 views)

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

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

By tamesis
(247 views)

Research in Slovenia

Research in Slovenia

Research in Slovenia. Ljubljana, 12.3.2008 mag. Marta Šabec Paradiž Service for intenational co-operation and European affairs Ministry of Higher Education, Science and Technology. (Sources: MHEST, SURS, EC, Eurostat ). Bojan Jenko. SLOVENIA Basic D ata.

By avak
(137 views)

BIOINFORMATICS OF AVIAN INFLUENZA VIRUS

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?.

By dayo
(203 views)

生物資訊 bioinformatics

生物資訊 bioinformatics

生物資訊 bioinformatics. 林育慶. Bioinformatics. 資訊 information. 資訊 (information) 包括各種形式,如新聞、文獻、影片、報告、數字、統計等,是人類活動重要的記錄。 數量龐大且混雜無序的資訊一定要透過適當的處理與分析,才能成為知識,為各行各業所用。 . What is Bioinformatics?. Computer Science. Mathematics and Statistics. 分子生物學 生物化學 遺傳學 物理化學 結構生物學 演化生物學 核磁共振學 基因體學

By avery
(183 views)

Advanced Mathematical Methods

Advanced Mathematical Methods

Advanced Mathematical Methods. COMP3006 Introduction to the course. Introduction. 2 sections Maths-Dr. Karen Page & Statistics –Dr. Simon Prince Maths until reading week. Course contact details. All communication concerning this course will be done via the email list.

By ataret
(120 views)

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

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

By carter
(158 views)

Introduction to the R language

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.

By oded
(95 views)

A Whirlwind Tour of Bioinformatics

A Whirlwind Tour of Bioinformatics

A Whirlwind Tour of Bioinformatics. Kun-Mao Chao ( 趙坤茂 ) 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.

By nizana
(106 views)

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

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

Comparison of Phenylalanine v. Tyrosine rotamers using 3D modeling software. Sam Portillo BNFO 300. PDB Statistics 1. Table 1 1. PDB Statistics 1. Ahmed et al (2015) uses “hint!” ( h ydropathic int actions ). Fig. 1 4,5. Ahmed et al (2015) used hint! to analyze 30,000 tyrosines.

By torie
(101 views)

Advanced Computing Resources for IU Researchers

Advanced Computing Resources for IU Researchers

Advanced Computing Resources for IU Researchers. Anurag Shankar University Information Technology Services Indiana University. March 2, 2012. Outline. IU’s Research Cyberinfrastructure - People, hardware, software, services, training & support Research LifeCycle Support

By aitana
(227 views)

Research Alliance in Math and Science

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.

By josiah
(113 views)

Choosing a Research Topic

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?

By duy
(128 views)

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

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.

By hedda
(126 views)

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

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

Pharmaceutical Informatics and Computer-Aided Drug Discovery Sangtae Kim Executive Director, Morgridge Institute for Research CDS&E Distinguished Seminar Series at Rutgers – October, 10, 2011. Twin institutes under one roof on the UW-Madison campus. Vision Inspired by Wisconsin Idea.

By gen
(122 views)

Chapter 6 Dynamic Programming

Chapter 6 Dynamic Programming

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

By ura
(491 views)

Representation and Reasoning with Graphical Models

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.

By larue
(170 views)

View Computational biology PowerPoint (PPT) presentations online in SlideServe. SlideServe has a very huge collection of Computational biology PowerPoint presentations. You can view or download Computational biology presentations for your school assignment or business presentation. Browse for the presentations on every topic that you want.