Functional grouping of similar genes using eigenanalysis on minimum spanning tree based neighborhood graph

Jothi, R.; Mohanty, Sraban Kumar; Ojha, Aparajita

doi:10.1016/j.compbiomed.2016.02.007

“…. , u t of the vertex v; (4) for i � 1 to t do (5) Calculate the GIC i j of the vertex u i for the vertex v by equation 5; (6) Calculate the GIC u i v of the vertex v for the vertex u i by equation 5; (7) if…”

Section: Complexity Analysismentioning

confidence: 99%

“…Clustering is one of the major unsupervised learning techniques and has been applied in many fields such as pattern recognition [1], image processing [2,3], community detection [4,5], bioinformatics [6,7], information retrieval [8,9], and so on. e main task of clustering is to classify a dataset into some nonoverlapping clusters based on a suitable similarity metric so that the elements in the same cluster are similar, while any elements from different clusters are dissimilar.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hierarchical Clustering Approach Based on Universal Gravitation

Zhang

¹

,

She

²

2020

Mathematical Problems in Engineering

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The target of the clustering analysis is to group a set of data points into several clusters based on the similarity or distance. The similarity or distance is usually a scalar used in numerous traditional clustering algorithms. Nevertheless, a vector, such as data gravitational force, contains more information than a scalar and can be applied in clustering analysis to promote clustering performance. Therefore, this paper proposes a three-stage hierarchical clustering approach called GHC, which takes advantage of the vector characteristic of data gravitational force inspired by the law of universal gravitation. In the first stage, a sparse gravitational graph is constructed based on the top k data gravitations between each data point and its neighbors in the local region. Then the sparse graph is partitioned into many subgraphs by the gravitational influence coefficient. In the last stage, the satisfactory clustering result is obtained by merging these subgraphs iteratively by using a new linkage criterion. To demonstrate the performance of GHC algorithm, the experiments on synthetic and real-world data sets are conducted, and the results show that the GHC algorithm achieves better performance than the other existing clustering algorithms.

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“…Multiple clustering methods were proposed to analyze microarray data (McDowell et al (2018), Chandra and Tripathi (2019)). These methods group samples with similar expression profiles into clusters as a way to reveal hidden patterns of samples (Jothi et al (2016)).…”

Section: Introductionmentioning

confidence: 99%

Peer Review #2 of "A hybrid multi-objective whale optimization algorithm for analyzing microarray data based on Apache Spark (v0.1)"

2021

0

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A microarray is a revolutionary tool that generates vast volumes of data that describe the expression profiles of genes under investigation that can be qualified as Big Data. Hadoop and Spark are efficient frameworks, developed to store and analyze Big Data. Analyzing microarray data helps researchers to identify correlated genes. Clustering has been successfully applied to analyze microarray data by grouping genes with similar expression profiles into clusters. The complex nature of microarray data obligated clustering methods to employ multiple evaluation functions to ensure obtaining solutions with high quality. This transformed the clustering problem into a Multi-Objective Problem (MOP). A new and efficient hybrid Multi-Objective Whale Optimization Algorithm with Tabu Search (MOWOATS) was proposed to solve MOPs. In this paper, MOWOATS is proposed to analyze massive microarray datasets. Three evaluation functions have been developed to ensure an effective assessment of solutions. MOWOATS has been adapted to run in parallel using Spark over Hadoop computing clusters. The quality of the generated solutions was evaluated based on different indices, such as Silhouette and Davies-Bouldin indices. The obtained clusters were very similar to the original classes. Regarding the scalability, the running time was inversely proportional to the number of computing nodes

show abstract

“…Multiple clustering methods were proposed to analyze microarray data (McDowell et al (2018), Chandra and Tripathi (2019)). These methods group samples with similar expression profiles into clusters as a way to reveal hidden patterns of samples (Jothi et al (2016)).…”

Section: Introductionmentioning

confidence: 99%

Peer Review #2 of "A hybrid multi-objective whale optimization algorithm for analyzing microarray data based on Apache Spark (v0.2)"

2021

0

View full text Add to dashboard Cite

A microarray is a revolutionary tool that generates vast volumes of data that describe the expression profiles of genes under investigation that can be qualified as Big Data. Hadoop and Spark are efficient frameworks, developed to store and analyze Big Data. Analyzing microarray data helps researchers to identify correlated genes. Clustering has been successfully applied to analyze microarray data by grouping genes with similar expression profiles into clusters. The complex nature of microarray data obligated clustering methods to employ multiple evaluation functions to ensure obtaining solutions with high quality. This transformed the clustering problem into a Multi-Objective Problem (MOP). A new and efficient hybrid Multi-Objective Whale Optimization Algorithm with Tabu Search (MOWOATS) was proposed to solve MOPs. In this paper, MOWOATS is proposed to analyze massive microarray datasets. Three evaluation functions have been developed to ensure an effective assessment of solutions. MOWOATS has been adapted to run in parallel using Spark over Hadoop computing clusters. The quality of the generated solutions was evaluated based on different indices, such as Silhouette and Davies-Bouldin indices. The obtained clusters were very similar to the original classes. Regarding the scalability, the running time was inversely proportional to the number of computing nodes

show abstract

Functional grouping of similar genes using eigenanalysis on minimum spanning tree based neighborhood graph

Cited by 25 publications

References 28 publications

A Novel Hierarchical Clustering Approach Based on Universal Gravitation

A Novel Hierarchical Clustering Approach Based on Universal Gravitation

Peer Review #2 of "A hybrid multi-objective whale optimization algorithm for analyzing microarray data based on Apache Spark (v0.1)"

Peer Review #2 of "A hybrid multi-objective whale optimization algorithm for analyzing microarray data based on Apache Spark (v0.2)"

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