Intersected coexpressed subcube miner: An effective triclustering algorithm

Ahmed, Hasin Afzal; Mahanta, Priyakshi; Bhattacharyya, Dhruba K.; Kalita, Jugal; Ghosh, Ashish

doi:10.1109/wict.2011.6141358

Cited by 13 publications

(5 citation statements)

References 10 publications

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“…A set of principles for each dimension is illustrated and detailed throughout this work for each biclustering step (mining, mapping and closing) and biclustering goal (defined according to a specific type, structure and quality of biclustering solutions). 3 Illustrating, xMotif [94] relies on greedy search and uses a size merit function and a noise threshold to guarantee the discovery of large and interesting biclusters, and SAMBA-based approaches [119] map binarized matrices into a weighted bipartite graph to find subgraphs that maximize a weight merit function. 4 For a matrix A ¼ ðX; YÞ and I D X; J D Y, the support metric is defined as…”

Section: Mining Approaches To Compose Biclustersmentioning

confidence: 99%

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A structured view on pattern mining-based biclustering

Henriques

Antunes

Madeira

2015

Pattern Recognition

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Section: Mining Approaches To Compose Biclustersmentioning

confidence: 99%

“…The additional dimensions can be used to capture additional informative views (such as time points or replicates) [3], to model contributions from overlapping areas of biclusters under a plaid model assumption [60], or to find biclusters' consensus over cubes with different pre-processing and closing criteria.…”

Section: Mining Approaches To Compose Biclustersmentioning

confidence: 99%

A structured view on pattern mining-based biclustering

Henriques

Antunes

Madeira

2015

Pattern Recognition

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“…gTRICLUSTER [89] uses Spearmen correlation coefficient to measure correlation among objects across time points while mining triclusters. ICSM [63] is a triclustering method that operates on possible pairs of time planes and detects some initial modules which are further extended to triclusters.…”

Section: Existing Clustering Methodsmentioning

confidence: 99%

“…Another variant of clustering, called triclustering operates on such datasets to generate triclusters. A tricluster is a group of objects that are not only similar over a subset of features, but are also similar across a subset of time points [63]. Triclustering promotes grouping of objects, features and time points simultaneously.…”

Section: Unsupervised Learningmentioning

confidence: 99%

Big Data Analytics in Bioinformatics: A Machine Learning Perspective

Kashyap¹,

Ahmed²,

Hoque³

et al. 2015

Preprint

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Bioinformatics research is characterized by voluminous and incremental datasets and complex data analytics methods. The machine learning methods used in bioinformatics are iterative and parallel. These methods can be scaled to handle big data using the distributed and parallel computing technologies.Usually big data tools perform computation in batch-mode and are not optimized for iterative processing and high data dependency among operations. In the recent years, parallel, incremental, and multi-view machine learning algorithms have been proposed. Similarly, graph-based architectures and in-memory big data tools have been developed to minimize I/O cost and optimize iterative processing.However, there lack standard big data architectures and tools for many important bioinformatics problems, such as fast construction of co-expression and regulatory networks and salient module identification, detection of complexes over growing protein-protein interaction data, fast analysis of massive DNA, RNA, and protein sequence data, and fast querying on incremental and heterogeneous disease networks. This paper addresses the issues and challenges posed by several big data problems in bioinformatics, and gives an overview of the state of the art and the future research opportunities.

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“…Hence three dimensional data anal sis approach evolved leading to generation of another variant of clustering called triclustering. A tricluster thus generated consist of objects which are similar regarding subset of feature and also subset of time points [52]. It leads to a simultaneous grouping of objects with feature points and time PLDA RF single SVM N., & Raza.…”

Section: Unsupervised Learning Process Does Not Use Class Labels For mentioning

confidence: 99%

Machine Learning-based state-of-the-art methods for the classification of RNA-Seq data

Jabeen

Ahmad

Raza

2017

Preprint

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Abstract. RNA-Seq measures expression levels of several transcripts simultaneously. The identified reads can be gene, exon, or other region of interest. Various computational tools have been developed for studying pathogen or virus from RNA-Seq data by classifying them according to the attributes in several predefined classes, but still computational tools and approaches to analyze complex datasets are still lacking. The development of classification models is highly recommended for disease diagnosis and classification, disease monitoring at molecular level as well as researching for potential disease biomarkers. In this chapter, we are going to discuss various machine learning approaches for RNA-Seq data classification and their implementation. Advancements in bioinformatics, along with developments in machine learning based classification, would provide powerful toolboxes for classifying transcriptome information available through RNA-Seq data.

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Intersected coexpressed subcube miner: An effective triclustering algorithm

Cited by 13 publications

References 10 publications

A structured view on pattern mining-based biclustering

A structured view on pattern mining-based biclustering

Big Data Analytics in Bioinformatics: A Machine Learning Perspective

Machine Learning-based state-of-the-art methods for the classification of RNA-Seq data

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