cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26180/5619511
title: Consensus clustering of gene expression microarray data using genetic algorithms

description: This work presents a new consensus clustering method for gene expression microarray data based on a genetic algorithm. Using two datasets - DA and DB - as input, the genetic algorithm examines putative partitions for the samples in DA, selecting biomarkers that support such partitions. The biomarkers are then used to build a classifier which is used in DB to determine its samples classes. The genetic algorithm is guided by an objective function that takes into account the accuracy of classification in both datasets, the number of biomarkers that support the partition, and the distribution of the samples across the classes for each dataset. To illustrate the method, two whole-genome breast cancer instances from dfferent sources were used. In this application, the results indicate that the method could be used to find unknown subtypes of diseases supported by biomarkers presenting similar gene expression profiles across platforms. Moreover, even though this initial study was restricted to two datasets and two classes, the method can be easily extended to consider both more datasets and classes. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; 
Chetty, Madhu ; 
Ahmad, Shandar ; 
Ngom, Alioune ; 
Teng, Shyh Wei ; 
Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; 
Coverage: 
Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
DOI: 10.26180/5619511.v1
title: Consensus clustering of gene expression microarray data using genetic algorithms

description: This work presents a new consensus clustering method for gene expression microarray data based on a genetic algorithm. Using two datasets - DA and DB - as input, the genetic algorithm examines putative partitions for the samples in DA, selecting biomarkers that support such partitions. The biomarkers are then used to build a classifier which is used in DB to determine its samples classes. The genetic algorithm is guided by an objective function that takes into account the accuracy of classification in both datasets, the number of biomarkers that support the partition, and the distribution of the samples across the classes for each dataset. To illustrate the method, two whole-genome breast cancer instances from dfferent sources were used. In this application, the results indicate that the method could be used to find unknown subtypes of diseases supported by biomarkers presenting similar gene expression profiles across platforms. Moreover, even though this initial study was restricted to two datasets and two classes, the method can be easily extended to consider both more datasets and classes. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; 
Chetty, Madhu ; 
Ahmad, Shandar ; 
Ngom, Alioune ; 
Teng, Shyh Wei ; 
Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; 
Coverage: 
Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
DOI: 10.26180/5619508
title: Classifying microarray cancer datasets using nearest subspace classification

description: In this paper we implement and test the recently described nearest subspace classifier on a range of microarray cancer datasets. Its classification accuracy is tested against nearest neighbor and nearest centroid algorithms, and is shown to give a significant improvement. This classification system uses class-dependent PCA to construct a subspace for each class. Test vectors are assigned the class label of the nearest subspace, which is defined as the minimum reconstruction error across all subspaces. Furthermore, we demonstrate this distance measure is equivalent to the null-space component of the vector being analyzed. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; 
Chetty, Madhu ; 
Ahmad, Shandar ; 
Ngom, Alioune ; 
Teng, Shyh Wei ; 
Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; 
Coverage: 
Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
DOI: 10.26180/5619508.v1
title: Classifying microarray cancer datasets using nearest subspace classification

description: In this paper we implement and test the recently described nearest subspace classifier on a range of microarray cancer datasets. Its classification accuracy is tested against nearest neighbor and nearest centroid algorithms, and is shown to give a significant improvement. This classification system uses class-dependent PCA to construct a subspace for each class. Test vectors are assigned the class label of the nearest subspace, which is defined as the minimum reconstruction error across all subspaces. Furthermore, we demonstrate this distance measure is equivalent to the null-space component of the vector being analyzed. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; 
Chetty, Madhu ; 
Ahmad, Shandar ; 
Ngom, Alioune ; 
Teng, Shyh Wei ; 
Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; 
Coverage: 
Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
DOI: 10.26180/5619505
title: Extraction of human DNA replication timing patterns from discrete microarray data

description: Effective reproduction is essential for the survival and proliferation of any organism, from the birth of new offspring to the repro- duction of individual cells. Each portion of a cell’s DNA must be copied exactly once during the replication phase of its cell cycle to ensure viability. In humans, this is achieved by a complex pattern of replication origins and terminations along the chromosomes until the final product is realized. DNA Tiling Microarrays are utilized to assay discrete pools of DNA replicated during different parts of the replication phase. We present a generalized framework for analyzing this discrete timing data to recover a relatively continuous profile of the DNA replication timing. This approach can be used to assay DNA replication timing over a variety of human cell lines or extended to other organisms. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; 
Chetty, Madhu ; 
Ahmad, Shandar ; 
Ngom, Alioune ; 
Teng, Shyh Wei ; 
Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; 
Coverage: 
Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
DOI: 10.26180/5619505.v1
title: Extraction of human DNA replication timing patterns from discrete microarray data

description: Effective reproduction is essential for the survival and proliferation of any organism, from the birth of new offspring to the repro- duction of individual cells. Each portion of a cell’s DNA must be copied exactly once during the replication phase of its cell cycle to ensure viability. In humans, this is achieved by a complex pattern of replication origins and terminations along the chromosomes until the final product is realized. DNA Tiling Microarrays are utilized to assay discrete pools of DNA replicated during different parts of the replication phase. We present a generalized framework for analyzing this discrete timing data to recover a relatively continuous profile of the DNA replication timing. This approach can be used to assay DNA replication timing over a variety of human cell lines or extended to other organisms. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; 
Chetty, Madhu ; 
Ahmad, Shandar ; 
Ngom, Alioune ; 
Teng, Shyh Wei ; 
Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; 
Coverage: 
Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
DOI: 10.26180/5619502
title: Spectrum analysis based method for dynamics and collective analysis of protein-protein interaction networks

description: The importance of understanding biological interaction networks has fueled the development of numerous interaction data generation techniques, databases and prediction tools. Generation of high-confident interaction networks formulates the first step towards the study for protein–protein interactions (PPI). A number of experimental methods, based on distinct, physical principles have been developed to identify PPI such as the yeast two-hybrid method (Y2H). In this work, we focus on one example of biological networks, namely the yeast protein interaction network (YPIN). In YPIN, we design and implement a computational model that captures the discrete and stochastic nature of protein interactions. In this model, we apply spectrum analysis method to the variance of the protein nodes which play an important role in the PPI networks, which can show the topology structure of dynamic and collective performances of PPI networks. We take YPIN, such as 48 "quasi-cliques" and 6 "quasi-bipartites" separated from 11855 yeast PPI networks with 2617 proteins, as an example and apply spectrum analysis to show the topology structure of dynamic and collective analysis of PPI networks and the performances. The obtained results may be valuable for deciphering unknown protein functions, determining protein complexes, and inventing drugs. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; 
Chetty, Madhu ; 
Ahmad, Shandar ; 
Ngom, Alioune ; 
Teng, Shyh Wei ; 
Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; 
Coverage: 
Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
DOI: 10.26180/5619502.v1
title: Spectrum analysis based method for dynamics and collective analysis of protein-protein interaction networks

description: The importance of understanding biological interaction networks has fueled the development of numerous interaction data generation techniques, databases and prediction tools. Generation of high-confident interaction networks formulates the first step towards the study for protein–protein interactions (PPI). A number of experimental methods, based on distinct, physical principles have been developed to identify PPI such as the yeast two-hybrid method (Y2H). In this work, we focus on one example of biological networks, namely the yeast protein interaction network (YPIN). In YPIN, we design and implement a computational model that captures the discrete and stochastic nature of protein interactions. In this model, we apply spectrum analysis method to the variance of the protein nodes which play an important role in the PPI networks, which can show the topology structure of dynamic and collective performances of PPI networks. We take YPIN, such as 48 "quasi-cliques" and 6 "quasi-bipartites" separated from 11855 yeast PPI networks with 2617 proteins, as an example and apply spectrum analysis to show the topology structure of dynamic and collective analysis of PPI networks and the performances. The obtained results may be valuable for deciphering unknown protein functions, determining protein complexes, and inventing drugs. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; 
Chetty, Madhu ; 
Ahmad, Shandar ; 
Ngom, Alioune ; 
Teng, Shyh Wei ; 
Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; 
Coverage: 
Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
DOI: 10.26180/5619499
title: Algorithms for detecting protein complexes in PPI networks: an evaluation study

description: Since protein complexes play important biological roles in cells, many computational methods have been proposed to detect protein complexes from protein-protein interaction (PPI) data. In this paper, we first review four reputed protein-complex detection algorithms (MCODE[2], MCL[21], CPA[1] and DECAFF[14]) and then present a comprehensive evaluation among them on two popular yeast PPI data3. We also discuss their relative strengthes and disadvantages to guide interested researchers. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; 
Chetty, Madhu ; 
Ahmad, Shandar ; 
Ngom, Alioune ; 
Teng, Shyh Wei ; 
Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; 
Coverage: 
Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
DOI: 10.26180/5619499.v1
title: Algorithms for detecting protein complexes in PPI networks: an evaluation study

description: Since protein complexes play important biological roles in cells, many computational methods have been proposed to detect protein complexes from protein-protein interaction (PPI) data. In this paper, we first review four reputed protein-complex detection algorithms (MCODE[2], MCL[21], CPA[1] and DECAFF[14]) and then present a comprehensive evaluation among them on two popular yeast PPI data3. We also discuss their relative strengthes and disadvantages to guide interested researchers. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; 
Chetty, Madhu ; 
Ahmad, Shandar ; 
Ngom, Alioune ; 
Teng, Shyh Wei ; 
Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; 
Coverage: 
Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
DOI: 10.26180/5619496
title: Feature ranking and feature redundancy reduction for prognostic microarray study of tumor clinical outcomes

description: Different from significant gene expression analysis which looks for all genes that are differentially regulated, feature selection in prognostic gene expression analysis aims at finding a subset of informative marker genes that are discriminative for prediction. Unfortunately feature selection in the literature of microarray study is predominated by the simple heuristic univariate gene filter paradigm that selects differentially expressed genes according to their statistical significance. Since the univariate approach does not take into account the correlated or interactive structure among the genes, classifiers built on genes so selected can be less accurate. More advanced approaches based on multivariate models have to be considered. Here, we introduce a feature ranking method through forward orthogonal search to assist prognostic gene selection. Application to published gene-lists selected by univariate models shows that the feature space can be largely reduced while achieving improved testing performances. Our results indicate that "significant" features selected using the gene-wised approaches can contain irrelevant genes that only serve to complicate model building. Multivariate feature ranking can help to reduce feature redundancy and to select highly informative prognostic marker genes. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; 
Chetty, Madhu ; 
Ahmad, Shandar ; 
Ngom, Alioune ; 
Teng, Shyh Wei ; 
Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; 
Coverage: 
Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
DOI: 10.26180/5619496.v1
title: Feature ranking and feature redundancy reduction for prognostic microarray study of tumor clinical outcomes

description: Different from significant gene expression analysis which looks for all genes that are differentially regulated, feature selection in prognostic gene expression analysis aims at finding a subset of informative marker genes that are discriminative for prediction. Unfortunately feature selection in the literature of microarray study is predominated by the simple heuristic univariate gene filter paradigm that selects differentially expressed genes according to their statistical significance. Since the univariate approach does not take into account the correlated or interactive structure among the genes, classifiers built on genes so selected can be less accurate. More advanced approaches based on multivariate models have to be considered. Here, we introduce a feature ranking method through forward orthogonal search to assist prognostic gene selection. Application to published gene-lists selected by univariate models shows that the feature space can be largely reduced while achieving improved testing performances. Our results indicate that "significant" features selected using the gene-wised approaches can contain irrelevant genes that only serve to complicate model building. Multivariate feature ranking can help to reduce feature redundancy and to select highly informative prognostic marker genes. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; 
Chetty, Madhu ; 
Ahmad, Shandar ; 
Ngom, Alioune ; 
Teng, Shyh Wei ; 
Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; 
Coverage: 
Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
DOI: 10.26180/5619493
title: Multiple sequence alignment for functional correlation among low similarity sequences

description: Multiple sequence alignment is a broadly used methodology in biological applications. It is expected to locate consensus sequence stretches with evolutionary and functional conservation. However, when sequence similarity among the queries becomes low, it works poorly. The aim of this study is to incorporate important biological knowledge and assumption to improve the quality of a general alignment on low similarity sequences such as carbohydrate binding module (CBM) families. Since the recognition of characteristic patterns in CBMs does not apply to a general model, a more accurate scoring function employing secondary-structure-based and key-residue-weighted algorithms for alignment was designed to approach this goal. Our results indicated that the new method was practically applicable to identify the key residues in terms of three-dimensional structures, while conventional tools could fail. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; 
Chetty, Madhu ; 
Ahmad, Shandar ; 
Ngom, Alioune ; 
Teng, Shyh Wei ; 
Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; 
Coverage: 
Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
DOI: 10.26180/5619493.v1
title: Multiple sequence alignment for functional correlation among low similarity sequences

description: Multiple sequence alignment is a broadly used methodology in biological applications. It is expected to locate consensus sequence stretches with evolutionary and functional conservation. However, when sequence similarity among the queries becomes low, it works poorly. The aim of this study is to incorporate important biological knowledge and assumption to improve the quality of a general alignment on low similarity sequences such as carbohydrate binding module (CBM) families. Since the recognition of characteristic patterns in CBMs does not apply to a general model, a more accurate scoring function employing secondary-structure-based and key-residue-weighted algorithms for alignment was designed to approach this goal. Our results indicated that the new method was practically applicable to identify the key residues in terms of three-dimensional structures, while conventional tools could fail. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; 
Chetty, Madhu ; 
Ahmad, Shandar ; 
Ngom, Alioune ; 
Teng, Shyh Wei ; 
Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; 
Coverage: 
Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
DOI: 10.26180/5619490
title: Fuzzy forms of the rand , adjusted rand and jaccard indices for fuzzy partitions of gene expression and other data

description: Clustering is one of the most basic processes that are performed in simplifying data and expressing knowledge in a scientific endeavor. Clustering algorithms have been proposed for the analysis of gene expression data with little guidance available to help choose among them however. Since the output of clustering is a partition of the input data, the quality of the partition must be determined. This paper presents fuzzy extensions to some commonly used clustering measures including the rand index (RI), adjusted rand index(ARI) and the jaccard index(JI) that are already defined for crisp clustering. Fuzzy clustering, and therefore fuzzy cluster indices, is beneficial since it provides more realistic cluster memberships for the objects that are clustered rather than 0 or 1 values. If a crisp partition is still desired the fuzzy partition can be turned in to a crisp partition in an obvious manner. The usefulness of the fuzzy clustering in that case is that it processes noise better. These new indices proposed in this paper, called FRI, FARI, and FJI for fuzzy clustering, give the same values as the original indices do in the special case of crisp clustering. Through use in fuzzy clustering of artificial data and real data, including gene expression data, the effectiveness of the indices is demonstrated. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; 
Chetty, Madhu ; 
Ahmad, Shandar ; 
Ngom, Alioune ; 
Teng, Shyh Wei ; 
Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; 
Coverage: 
Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.