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Finding optimal gene networks using biological constraints

Sascha Ott, Satoru Miyano
2003 Genome Informatics Series  
Since the problem of estimating gene networks is NP-hard and exhibits a search space of super-exponential size, researchers are using heuristic algorithms for this task.  ...  In order to overcome this problem, we present a general approach to reduce the search space to a biologically meaningful subspace and to find optimal solutions within the subspace in linear time.  ...  Acknowledgments The authors would like to thank Seiya Imoto, who pointed us to the application of Algorithm 3 to cell cycle data.  ... 
pmid:15706527 fatcat:r3opx7fd7beorow2rdikezwmhe

PartitionFinder: Combined Selection of Partitioning Schemes and Substitution Models for Phylogenetic Analyses

R. Lanfear, B. Calcott, S. Y. W. Ho, S. Guindon
2012 Molecular biology and evolution  
We demonstrate that these methods significantly outperform previous approaches, including both the ad hoc selection of partitioning schemes (e.g., partitioning by gene or codon position) and a recently  ...  proposed hierarchical clustering method.  ...  clustering approach on a ten gene data set from ray-finned fishes (table 3; Li et al. 2008) .  ... 
doi:10.1093/molbev/mss020 pmid:22319168 fatcat:jvj7qdxnunc5jpxmptxe235yle

Text Mining Biomedical Literature for Discovering Gene-to-Gene Relationships: A Comparative Study of Algorithms

Ying Liu, S.B. Navathe, J. Civera, V. Dasigi, A. Ram, B.J. Ciliax, R. Dingledine
2005 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
BEA-PARTITION is simple to implement and provides a powerful approach to clustering genes or to any clustering problem where starting matrices are available from experimental observations.  ...  The sharing of functional keywords among genes is used as a basis for clustering in a new approach called BEA-PARTITION in this paper.  ...  The authors would like to thank Brian Revennaugh and Alex Pivoshenk for research support.  ... 
doi:10.1109/tcbb.2005.14 pmid:17044165 fatcat:fn547ts72feezcihirysvxtsxu

Novel Hybrid PSO-SA Model for Biclustering of Expression Data

K. Thangavel, J. Bagyamani, R. Rathipriya
2012 Procedia Engineering  
Conventional clustering approaches like K-Means, heuristic biclustering approaches like Greedy Algorithms, meta-heuristic biclustering approaches like Genetic Algorithm, Simulated Annealing or Particle  ...  Uncovering genetic pathways is equivalent to finding clusters of genes with expression levels that evolve coherently under subsets of conditions.  ...  Due to the complexity of heuristic approach and due to the large size of gene expression data, meta-heuristic algorithms like PSO, SA and GA have been applied for biclustering of gene expression data.  ... 
doi:10.1016/j.proeng.2012.01.962 fatcat:ca32wh3s2rarpebnltcy6wysqy

Simultaneous Clustering of Multiple Gene Expression and Physical Interaction Datasets

Manikandan Narayanan, Adrian Vetta, Eric E. Schadt, Jun Zhu, Jörg Stelling
2010 PLoS Computational Biology  
Our algorithm provides an efficient solution to a well-defined problem of jointly clustering networks, using techniques that permit certain theoretical guarantees on the quality of the detected clustering  ...  These guarantees coupled with an effective scaling heuristic and the flexibility to handle multiple heterogeneous networks make our method JointCluster an advance over earlier approaches.  ...  Ron Shamir and Igor Ulitsky for providing the Matisse and Co-clustering software, John Tsang for pointing to some enrichment-related references, and Bin Zhang for help with certain yeast datasets.  ... 
doi:10.1371/journal.pcbi.1000742 pmid:20419151 pmcid:PMC2855327 fatcat:2e6eimejmvhwvomzuwx7qijznu

An Improved Pearson's Correlation Proximity-Based Hierarchical Clustering for Mining Biological Association between Genes

P. M. Booma, S. Prabhakaran, R. Dhanalakshmi
2014 The Scientific World Journal  
Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association  ...  To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure  ...  Acknowledgment The authors would like to acknowledge the valuable comments of the anonymous reviewers.  ... 
doi:10.1155/2014/357873 pmid:25136661 pmcid:PMC4083291 fatcat:5oym3gmo7jeedkgmy3dcrk5zi4

From co-expression to co-regulation: how many microarray experiments do we need?

Ka Yee Yeung, Mario Medvedovic, Roger E Bumgarner
2004 Genome Biology  
We applied various clustering algorithms to microarray datasets with different sizes, and we evaluated the clustering results by determining the fraction of gene pairs from the same clusters that share  ...  Moreover, the model-based clustering algorithm MCLUST consistently outperforms more traditional methods in accurately assigning co-regulated genes to the same clusters on standardized data.  ...  Our overall approach Figure 1 Our overall approach. We applied different clustering algorithms to cluster the genes in yeast microarray datasets with different sizes to identify co-expressed genes.  ... 
doi:10.1186/gb-2004-5-7-r48 pmid:15239833 pmcid:PMC463312 fatcat:bthqq7lsz5apdasiaeouv32rmy

Software Module Clustering using a Fast Multi-objective Hyper-heuristic Evolutionary Algorithm

A. CharanKumari, K. Srinivas
2013 International Journal of Applied Information Systems  
The Multiobjective Hyper-heuristic Evolutionary Algorithm (MHypEA) is a fast and effective metaheuristic search technique for suggesting software module clusters while maximizing cohesion and minimizing  ...  The selection mechanism to select a low-level heuristic is based on reinforcement learning with adaptive weights.  ...  The authors are also thankful to Spiros Mancoridis and Mark Harman for providing the MDGs referenced in the paper.  ... 
doi:10.5120/ijais13-450925 fatcat:vf4oysvxqffr7ps75bp4jhifoe

Biclustering Algorithms [chapter]

Amos Tanay, Roded Sharan, Ron Shamir
2005 Chapman & Hall/CRC Computer & Information Science Series  
We review some of the algorithmic approaches to biclustering and discuss their properties.  ...  Analysis of large scale geonomics data, notably gene expression, has initially focused on clustering methods.  ...  Algorithmic approaches The algorithmic approaches for detecting biclusters given the data are greatly affected by the type of score/constraint model in use.  ... 
doi:10.1201/9781420036275.ch26 fatcat:llsjo5ve4vg7dpjfprg6aselgy

Optimisation algorithms for microarray biclustering

Dimitri Perrin, Christophe Duhamel
2013 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)  
For the largest dataset, solutions are obtained in only five minutes using a standard cluster and the parallel approach with eight islands.  ...  Greedy heuristic We propose a new search heuristic.  ... 
doi:10.1109/embc.2013.6609569 pmid:24109756 dblp:conf/embc/PerrinD13 fatcat:5a5qon4y4rg63oimkd7qnmlt3e

Multiple sequence alignment with user-defined constraints at GOBICS

B. Morgenstern, N. Werner, S. J. Prohaska, R. Steinkamp, I. Schneider, A. R. Subramanian, P. F. Stadler, J. Weyer-Menkhoff
2004 Bioinformatics  
We apply our approach to genomic sequences adjacent to the Hox genes.  ...  As a by-product, we obtain not only useful insights for the further development of alignment algorithms, but also an improved approach to phylogenetic footprinting.  ...  Acknowledgements The work was supported by DFG grant MO 1048/1-1 to BM, IS and JWM and by DFG Bioinformatics Initiative BIZ-6/1-2 to SJP and PFS.  ... 
doi:10.1093/bioinformatics/bti142 pmid:15546937 fatcat:mxppqp7y2ff2rkyiqb2chhzpfm

Computational methods for Gene Orthology inference

D. M. Kristensen, Y. I. Wolf, A. R. Mushegian, E. V. Koonin
2011 Briefings in Bioinformatics  
Identification of orthologous gene sets typically involves phylogenetic tree analysis, heuristic algorithms based on sequence conservation, synteny analysis, or some combination of these approaches.  ...  Other, heuristic methods identify probable orthologs as the closest homologous pairs or groups of genes in a set of organisms.  ...  Ortholuge uses a phylogenetic approach to refine clusters made by a heuristic algorithm, noting cases where relative gene divergence is atypical between two compared species and an outgroup species and  ... 
doi:10.1093/bib/bbr030 pmid:21690100 pmcid:PMC3178053 fatcat:hbxflsbzc5gnrf7svljpuek7su

BiCross : A Biclustering Technique for Gene Expression Data using One Layer Fixed Weighted Bipartite Graph Crossing Minimization

Suvendu Kanungo, Gadadhar Sahoo, Manoj Madhava Gore
2011 International Journal of Computer Applications  
This technique, in contrast to the conventional clustering techniques, helps in identifying a subset of the genes and a subset of the experimental conditions that together exhibit co-related pattern.  ...  The experimental results demonstrate that, our method is scalable to practical gene expression data and has superiority over other similar algorithms in terms of accuracy and computational efficiency.  ...  Many conventional clustering algorithms [29] have been developed to mine clusters in the whole data space.  ... 
doi:10.5120/3553-4880 fatcat:x773wq7lkncqhnlol6npl35fle

A systematic comparison of genome-scale clustering algorithms

Jeremy J Jay, John D Eblen, Yun Zhang, Mikael Benson, Andy D Perkins, Arnold M Saxton, Brynn H Voy, Elissa J Chesler, Michael A Langston
2012 BMC Bioinformatics  
A wealth of clustering algorithms has been applied to gene co-expression experiments.  ...  These algorithms cover a broad range of approaches, from conventional techniques such as k-means and hierarchical clustering, to graphical approaches such as k-clique communities, weighted gene co-expression  ...  WGCNA, CAST, and CLICK are all heuristic approaches, computing approximations to various graph-based metrics.  ... 
doi:10.1186/1471-2105-13-s10-s7 pmid:22759431 pmcid:PMC3382433 fatcat:2fdeattqyjad7pd3b37ainkqze

A Systematic Comparison of Genome Scale Clustering Algorithms [chapter]

Jeremy J. Jay, John D. Eblen, Yun Zhang, Mikael Benson, Andy D. Perkins, Arnold M. Saxton, Brynn H. Voy, Elissa J. Chesler, Michael A. Langston
2011 Lecture Notes in Computer Science  
A wealth of clustering algorithms has been applied to gene co-expression experiments.  ...  These algorithms cover a broad range of approaches, from conventional techniques such as k-means and hierarchical clustering, to graphical approaches such as k-clique communities, weighted gene co-expression  ...  WGCNA, CAST, and CLICK are all heuristic approaches, computing approximations to various graph-based metrics.  ... 
doi:10.1007/978-3-642-21260-4_39 fatcat:kjtudyxtejb7fnoz4xh3t2tfke
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