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Nonnegative Matrix Factorization with Sparseness Constraints
Nonnegative Matrix Factorization with Sparseness Constraints

Genetic algorithm, particle swarm optimization and hybrid scheme
Genetic algorithm, particle swarm optimization and hybrid scheme

... emerged as one of the most powerful computational method for solving complex real-world problems. Commonly GA contains three different stages in the process of global solution searching [23]:  Stage 1: generating an initial population.  Stage 2: evaluating a fitness function.  Stage 3: producing ...
Lecture 9 - MyCourses
Lecture 9 - MyCourses

Solving 3D incompressible Navier-Stokes equations on hybrid CPU
Solving 3D incompressible Navier-Stokes equations on hybrid CPU

Constant-Time Local Computation Algorithms
Constant-Time Local Computation Algorithms

... and the dotted red arc represents the distance 2 range. Edges marked by ? are considered by v in round 2. ...
document
document

Introduction to Computer Science
Introduction to Computer Science

... We have to think which informations are essential, which can help us and which are completely useless. We have to think how we will represent choosen informations. The last point lead us to notion of data type (data structure). ...
Brand, Veronica - Degenerate Primer Design using Computational Tools
Brand, Veronica - Degenerate Primer Design using Computational Tools

here
here

... We develop an decentralised control algorithm for mobile agents [1, 3, 4, 5] whose motion is subject to constraints. These motion constraints can be used to model the physical layout of the environment (such as the floor map of a building), as well as the intrinsic movement constraints of the agent ...
A Novel Algorithm of Gene Expression Programming Based on
A Novel Algorithm of Gene Expression Programming Based on

... Gene Expression Programming(GEP), invented by Cândida Ferreira [1], is a novel genetic algorithm in which the individuals are encoded as symbolic strings of fixed length (genotype) and then expressed as expression trees (phenotype)with different sizes and shapes. It combines the characteristics of G ...
Graph-based consensus clustering for class discovery from gene
Graph-based consensus clustering for class discovery from gene

... where is an diagonal matrix with as diagonal, is the correlation matrix. • The label vector is composed from the second smaller eigenvector . ...
Document
Document

The Simulated Greedy Algorithm for Several Submodular Matroid Secretary Problems Princeton University
The Simulated Greedy Algorithm for Several Submodular Matroid Secretary Problems Princeton University

Unsupervised Face Alignment by Robust Nonrigid Mapping
Unsupervised Face Alignment by Robust Nonrigid Mapping

PPT - Glasnost
PPT - Glasnost

... Beyond Scalars ...
Genome
Genome

... Examine the other matches on the previous slide in the genome browser. Keep in mind 2 questions: (Answers provided at the end of the document) ...
A set reduction and pattern matching problem motivated by Allele
A set reduction and pattern matching problem motivated by Allele

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Solving Redundancy Allocation Problem with Repairable
Solving Redundancy Allocation Problem with Repairable

... general RAP. Ida et al. [12] and Yokota et ...
as Adobe PDF - Edinburgh Research Explorer
as Adobe PDF - Edinburgh Research Explorer

Mapping strategies for sequence reads (with focus on RNA-seq)
Mapping strategies for sequence reads (with focus on RNA-seq)

... ted by its splice site scoring algorithm; its authors mapping 71 million RNA-Seq reads to A.thaliana 0 CPU hours, which is ∼180 000 reads per CPU hour. cle, we describe TopHat, a software package that ce sites ab initio by large-scale mapping of RNA-Seq maps reads to splice sites in a mammalian geno ...
Genetic Algorithm to find optimal GLCM features
Genetic Algorithm to find optimal GLCM features

Clusterpath: An Algorithm for Clustering using Convex
Clusterpath: An Algorithm for Clustering using Convex

... when independently applied to each column of a matrix of data. Thus the `1 clusterpath for a matrix of data is strictly agglomerative, and results in an algorithm of complexity O(pn log n). This is an interesting alternative to hierarchical clustering, which normally requires O(pn2 ) space and time ...
Documentation - Broad Institute
Documentation - Broad Institute

... consensus assembly. The correction process is aggressive, and as such requires an assembly that is highly representative of the population represented by the read data. It is highly recommended that the user use a de novo assembly generated from the read set being corrected. The reference should als ...
Karp Algorithm
Karp Algorithm

... 3. A partitioning algorithm. In this section we present a partitioning algorithm (called Algorithm 1) for the construction of a spanning walk through n given points (cities) in a rectangular region of the plane. The algorithm uses a subroutine TOUR capable of the exact solution of ;-city traveling-s ...
< 1 2 3 4 5 6 7 8 ... 21 >

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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