'Score 1' diaporamas de présentation

Lecture 2. Optimal Sequence Alignment

Lecture 2. Optimal Sequence Alignment. The Chinese University of Hong Kong CSCI3220 Algorithms for Bioinformatics. Lecture outline. Problems related to sequences Core: Sequence alignment Sequence alignment Problem components Difficulty Methods (focused on optimal methods in this lecture)

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Lecture 2 Pairwise Sequence Alignment

Lecture 2 Pairwise Sequence Alignment. WHAT?. WHAT?. Given any two sequences (DNA or protein) Seq 1: CATATTGCAGTGGTCCCGCGTCAGGCT S eq 2: TAAATTGCGTGGTCGCACTGCACGCT we are interested to know to what extent they are similar?. CATATTGCAGTGGTCCCGCGTCAGGCT TAAATTGCGT-GGTCGCACTGCACGCT. WHY?.

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Results of a market research survey

Results of a survey relating to Siemens Enterprise Communications at the CeBIT 2007 exhibition in Hanover. Results of a market research survey. Produced on behalf of Siemens Enterprise Communications, Munich. Munich, 20/4/2007 Project 4739. Academic staff Patrick Rudloff (Economics degree)

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Survey Results

Survey Results. Practice. The Neuroticism Measure = 23.32 S = 6.24 n = 54 How many people likely have a neuroticism score between 29 and 34?. Practice. (29-23.32) /6.24 = .91 B = .3186 ( 34-23.32)/6.26 = 1.71 B =.4564 .4564-.3186 = .1378 .1378*54 = 7.44 or 7 people.

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Multiple Sequence Composition Alignment

Multiple Sequence Composition Alignment. Name: Yip Chi Kin Date: 21-12-2006. Studied Papers. [B03] Composition Alignment. [S98] Divide-and-conquer Alignment. [M99] DIALIGN Algorithm. [SMS03] DCA + Segment-based. Main Aspects. ?Dynamic Programming

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Undergrad Status Report: August 1, 2003 John Blatz & Alex Kulesza

Undergrad Status Report: August 1, 2003 John Blatz & Alex Kulesza. Presentation Overview. Project Summary Semantic Similarity as a Confidence Feature Dealing with 100GB of Data Human MT Evaluations. Project Summary. Goal: estimate probabilities of correctness for MT outputs

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Allergen Control in Foodservice

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High Performance Direct Pairwise Comparison of Genomic Sequences

High Performance Direct Pairwise Comparison of Genomic Sequences. Christopher Mueller, Mehmet Dalkilic, Andrew Lumsdaine HiCOMB April 4, 2005 Denver, Colorado. Introduction. Goals Generate data for large format visualization Exploit parallel features present in commodity hardware

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CRIM 483

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Stage

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Nutch Search Engine Tool

Nutch Search Engine Tool. Nutch overview. A full-fledged web search engine Functionalities of Nutch Internet and Intranet crawling Parsing different document formats (PDF, HTML, XML, JS, DOC,PPT etc.) ? Web interface for querying the index Management of Recrawls. Nutch Architecture.

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Randomhex Module

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Sequence comparison: Significance of similarity scores

Sequence comparison: Significance of similarity scores. http://faculty.washington.edu/jht/GS559_2012/. Genome 559: Introduction to Statistical and Computational Genomics Prof. James H. Thomas. Review. How to compute and use a score matrix.

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11 th meeting SCORE Project Nottingham University 09/11/2009

11 th meeting SCORE Project Nottingham University 09/11/2009. Technology acceptance Field information Practical Action, Kenya. Use of Solar lighting systems (50Wp).

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Multidimensional Arrays

Multidimensional Arrays. Name the collection. Number the elements. HW 0. HW 1. HW 2. HW 3. HW 4. student 0. 10. 7. 8. 9. 5. student 1. 5. 6. 8. 3. 9. row. student 2. 7. 4. 6. 9. 10. column. Definition : An ordered collection of values of

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Score 2

Supplementary Fig. S1. RACK1. Score 0. Score 1. Score 2. RhoA. (-). (+). (++).

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