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ANALYSIS OF ALGORITHMS
ANALYSIS OF ALGORITHMS

Document
Document

Lecture 27
Lecture 27

... Certain amino acids are more likely to be accepted than others. Distribution of amino acids in proteins is not uniform (9.5% are Leu on average and only 1.2% are Trp). Can also be affected by shifts in the sequence resulting from insertion or deletion of one or more residues within a chain. Example ...
Comp. Genomics
Comp. Genomics

... omitted by the model ψi • Can be computed efficiently by Felsenstein’s “pruning algorithm” (recitation 6) • Joint probability of a path in the HMM and and alignment X • Viterbi, forward-backward etc. – as usual ...
GENESIS: genome evolution scenarios
GENESIS: genome evolution scenarios

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Supplementary experimental procedures

Milestone7
Milestone7

Learning Algorithms for Solving MDPs References: Barto, Bradtke
Learning Algorithms for Solving MDPs References: Barto, Bradtke

... References: Barto, Bradtke and Singh (1995) “Learning to Act Using Real-Time Dynamic Programming” in Machine Learning (also on WWW) 1. Q-Learning Given an MDP problem, define the ...
Travelling Salesman Problem
Travelling Salesman Problem

doc
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... partition table. In order to fulfill this purpose, we explore a self-learning tool for studying interrupts (Witts), demonstrating that model checking and RAID can connect to accomplish this objective. The understanding of courseware is a natural quandary. The notion that researchers agree with DNS i ...
Bioinformatics Individual Projects
Bioinformatics Individual Projects

... g. Use the wildtype protein sequence and BLAST to obtain 4 more homologous protein sequences for your multiple sequence alignment. Copy those 4 FASTA formatted sequences to your Word sequence file too h. Use ClustalW to align all 6 sequences (wildtype, mutant, plus 4 homologous sequences) i. Save th ...
Author: Cross Multiply and Numbers Between Group Members: 1
Author: Cross Multiply and Numbers Between Group Members: 1

... Author: ...
Implementing Parallel processing of DBSCAN with Map reduce
Implementing Parallel processing of DBSCAN with Map reduce

...  Density-based spatial clustering of applications with noise ...
- BioMed Central
- BioMed Central

Recostructing the Evolutionary History of Complex Human Gene
Recostructing the Evolutionary History of Complex Human Gene

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... 27. When aligning two sequences that are about 20% identical, which of the following scoring matrices would be most appropriate? (A) PAM 3 (B) PAM 9 (C) PAM 24 (D) PAM 210 28. 2 pts Some of the following can be done with BLAST and some of them should NOT be done. Sort them into the correct bin below ...
Slides - Indico
Slides - Indico

Algebra 2 Name: 1.1 – More Practice Your Skills – Arithmetic
Algebra 2 Name: 1.1 – More Practice Your Skills – Arithmetic

... 4. Indicate whether each situation could be represented by an arithmetic sequence. If the situation can be ...
PPTX - Tandy Warnow
PPTX - Tandy Warnow

... methods; it also showed that SATé trees and alignments were even more accurate than maximum likelihood trees on leading alignments. Thus, parsimony-style co-estimation (as in POY and ...
Clustered alignments of gene-expression time series data
Clustered alignments of gene-expression time series data

... • To solve “all genes are assumed to be aligned in lockstep with one another” – Calculated clustered alignments – Find clusters of gene such that genes within a cluster share a common alignment – Each cluster is aligned independently of the others – Similar to k-means • Alternates between assigning ...
Genome & Protein “ Sequence Analysis Programs”
Genome & Protein “ Sequence Analysis Programs”

... Computer programs ...
Practical theory (15-20 min) A phylogeny is the representation of the
Practical theory (15-20 min) A phylogeny is the representation of the

... 6. Using “seq4.fasta” and “seq5.fasta”, find their orthologs in UniProt in Mus musculus, Gallus gallus, Xenopus laevis and Ornithorhynchus anatinus (platypus). Put all of the sequences in one file and built a phylogenetic tree using Trex. Use the radial representation of the tree. What do you observ ...
Doc - UCF CS
Doc - UCF CS

Penalized Score Test for High Dimensional Logistic Regression
Penalized Score Test for High Dimensional Logistic Regression

15-451 Homework 1 Jan 20, 2008
15-451 Homework 1 Jan 20, 2008

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Smith–Waterman algorithm

The Smith–Waterman algorithm performs local sequence alignment; that is, for determining similar regions between two strings or nucleotide or protein sequences. Instead of looking at the total sequence, the Smith–Waterman algorithm compares segments of all possible lengths and optimizes the similarity measure.The algorithm was first proposed by Temple F. Smith and Michael S. Waterman in 1981. Like the Needleman–Wunsch algorithm, of which it is a variation, Smith–Waterman is a dynamic programming algorithm. As such, it has the desirable property that it is guaranteed to find the optimal local alignment with respect to the scoring system being used (which includes the substitution matrix and the gap-scoring scheme). The main difference to the Needleman–Wunsch algorithm is that negative scoring matrix cells are set to zero, which renders the (thus positively scoring) local alignments visible. Backtracking starts at the highest scoring matrix cell and proceeds until a cell with score zero is encountered, yielding the highest scoring local alignment. One does not actually implement the algorithm as described because improved alternatives are now available that have better scaling (Gotoh, 1982) and are more accurate (Altschul and Erickson, 1986).
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