Quality Measures for Gene Expression Biclusters

Pontes, Beatriz; Girldez, Ral; Aguilar–Ruiz, Jesús S.

doi:10.1371/journal.pone.0115497

Cited by 42 publications

(24 citation statements)

References 37 publications

(43 reference statements)

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“…Aguilar-Ruiz et al [28,29] have demonstrated that the mean squared residuals can only find additive models and no other models can be found. The mean square residue ( ) Hscore is a better way to measure whether the result is close to the addition model [30], and it is also the usual measure of measurement at the present stage. The size of a bicluster S (bicluster contains the number of elements) is also a criterion for judging the quality of bicluster.…”

Section: 2 Comparison Of Results Between This Algorithm and Grasp-mentioning

confidence: 99%

Optimization of Biclustering Algorithm Based on Greedy Randomized Adaptive Search Procedure

Yang¹,

Zhao²,

Shu³

et al. 2018

JMI

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The biclustering algorithm based on greedy randomized adaptive search procedure (GRASP) has two main steps, firstly, bicluster seeds are generated by Kmeans clustering; secondly, the greedy randomized adaptive search procedure is used to expand these bicluster seeds so that to get some better biclusters. However, K-means clustering assumes that each gene belongs to only one cluster, which is not biologically valid, because some genes may not belong to only one category, and K-means clustering requires multiple runs to determine the number of "clusters," all of which will affect the identification of bicluster seeds. This paper optimizes GRASP-based bIclustering algorithm. In this paper, the single column vector clustering is used to generate biclusters seed, and then the bicluster seeds are added more rows and columns to generate the final biclusters. Experiment shows that this algorithm can generate a large number of biclusters with coexpression level, and the algorithm also finds more biclusters.

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Section: 2 Comparison Of Results Between This Algorithm and Grasp-mentioning

confidence: 99%

Optimization of Biclustering Algorithm Based on Greedy Randomized Adaptive Search Procedure

Yang¹,

Zhao²,

Shu³

et al. 2018

JMI

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“…To determine whether a bicluster presents a coherent tendency, i.e., whether a group of buildings show a similar electric energy consumption behaviour during the same period of time, we used a measure called Transposed Virtual Error (VE t ) [30]. The lower is the value of VE t , the better the bicluster is considered.…”

Section: Biclusteringmentioning

confidence: 99%

Biclustering of Smart Building Electric Energy Consumption Data

Divina¹,

Vela²,

Torres³

2019

Applied Sciences

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Nowadays, smart buildings can collect data regarding the electric energy consumption, which can then be analyzed to gain insights or to predict or identify abnormal energy consumption. Numerous models are applied to face this problem but they are based on a global point of view and cannot detect local patterns of abnormal consumption. This work lies in the former option, as we propose a way to analyze energy consumption data from smart buildings. In particular, we use energy consumption data collected by various buildings over a five-year period. These data were analyzed to gain insight into the functioning of the considered buildings, with the aim of detecting anomalous situations, which could indicate that some energy usage policy should be changed or that there is a fault in the sensor network. In particular, we propose an approach based on biclustering, which allows obtaining subgroups of buildings that show a similar behaviour over a specific period of time. To the best of our knowledge, this is the first application of biclustering to energy consumption data analysis. Results confirm that the proposed approach can help policy makers in detecting irregular situations, which can provide hints on how to improve the efficiency of buildings.

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“…The performance of the proposed work was compared based on four validation indices such as mean square residue [9], coverage of the triclusters [10], average row variance [11], and average correlation value [12]. These validation measures are used to evaluate biclustering, so they were remodeled for triclustering by extension with the time point.…”

Section: Performance Comparisonmentioning

confidence: 99%

Rough fuzzy cuckoo search for triclustering microarray gene expression data

Palaniswamy¹,

Premalatha²

2019

Turk J Elec Eng & Comp Sci

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Analyzing time series microarray gene expression data is a computational challenge due to its threedimensional characteristics. Triclustering techniques are applied to three-dimensional data for mining similarly expressed genes under a subset of conditions and time points. In this work, a novel rough fuzzy cuckoo search algorithm is proposed for triclustering genes across samples and time points simultaneously. By applying the upper and lower approximation of rough set theory and the objective function of fuzzy k-means, rough fuzzy k-means was incorporated into a cuckoo search to handle the uncertainty of the data. The proposed method was applied to three real-life time series gene expression datasets. This work was evaluated using four validation indices and correlation analysis was performed to indicate the cluster quality. The proposed work was also compared with the existing triclustering algorithms and it outperformed the other methods.

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Quality Measures for Gene Expression Biclusters

Cited by 42 publications

References 37 publications

Optimization of Biclustering Algorithm Based on Greedy Randomized Adaptive Search Procedure

Optimization of Biclustering Algorithm Based on Greedy Randomized Adaptive Search Procedure

Biclustering of Smart Building Electric Energy Consumption Data

Rough fuzzy cuckoo search for triclustering microarray gene expression data

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