A binary biclustering algorithm based on the adjacency difference matrix for gene expression data analysis

Chu, He-Ming; Liu, Jin-Xing; Zhang, Ke; Zheng, Chun-Hou; Wang, Juan; Kong, Xiang-Zhen

doi:10.1186/s12859-022-04842-4

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…The SDI evolutionary tree highlighted the internal distinctions of SARS-CoV-2, whereas the SDII evolutionary tree compared SARS-CoV-2 to other HCoVs and SARSr-CoV-2 lineages to explore their genetic evolution rules. The phylogenetic results of SDs demonstrated that the internal nodes in trees and the clustering algorithm for adjacency matrix ( Chu et al., 2022 ) had little effect on the classification of certain species, which intuitively displayed excessively long branches in the tree due to poor sequencing quality of a few sequences. Internal nodes of the tree ( Fig.…”

Section: Discussionmentioning

confidence: 99%

RNA barcode segments for SARS-CoV-2 identification from HCoVs and SARSr-CoV-2 lineages

You,

Jiang,

Ding

et al. 2024

Virologica Sinica

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Section: Discussionmentioning

confidence: 99%

RNA barcode segments for SARS-CoV-2 identification from HCoVs and SARSr-CoV-2 lineages

You,

Jiang,

Ding

et al. 2024

Virologica Sinica

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“…Certain overlapping biclustering models are able to capture member clusters contained in two or more biclusters in the data matrix. Such methods have been applied to many biological data for the classification and identification of biological entities [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Biclustering Models Under Collinearity in Simulated Biological Experiments

Nnamani,

Ahmad

2023

MATEMATIKA

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Biclustering models allow simultaneous detection of group observations that are related to variables in a data matrix. Such methods have been applied in biological data for classification. Collinearity is a common feature in biological data as there exist interactions between genes and proteins in their respective pathways. Such relationships could seriously reduce the efficiency of biclustering models. In this study, synthetic data are generated to investigate the effect of collinearity on the performance of biclustering models. Specifically, the data are generated and induced with varying degrees of collinearity using Cholesky decomposition, and are implanted with biclusters to produce different sets of synthetic data. The effectiveness of three models namely Biclustering by Cheng and Church (BCCC), Spectral Bicluster (BCSpectral) and Plaid Model in correctly detecting three types of biclusters in the generated data matrix were compared. The results show that all the models investigated are sensitive to changes in the level of collinearity. At low collinearity, all biclustering models were able to detect the implanted biclusters in the data correctly. As the level of collinearity in the data rise, the proportion of detected biclusters captured by the models reduces. In particular, BCC outperformed the other two models for moderate to high collinearity with a Jaccard coefficient of 0.499 to 0.875 and 0.746 to 0.936 for one and two implanted biclusters respectively.

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“…However, Wang et al (2016) argue that biclustering algorithms are non-specific so there is no rule how to choose the right one for certain criteria or datasets. The selection of a biclustering algorithm should be based on several considerations, namely ease of implementation, absence of disturbance by noisy datasets and speed in finding the most suitable structure in the data matrix (Castanho et al, 2022;Chu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Province clustering based on the percentage of communicable disease using the BCBimax biclustering algorithm

Aidi,

Wulandari,

Oktarina

et al. 2023

Geospat Health

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Indonesia needs to lower its high infectious disease rate. This requires reliable data and following their temporal changes across provinces. We investigated the benefits of surveying the epidemiological situation with the imax biclustering algorithm using secondary data from a recent national scale survey of main infectious diseases from the National Basic Health Research (Riskesdas) covering 34 provinces in Indonesia. Hierarchical and k-means clustering can only handle one data source, but BCBimax biclustering can cluster rows and columns in a data matrix. Several experiments determined the best row and column threshold values, which is crucial for a useful result. The percentages of Indonesia’s seven most common infectious diseases (ARI, pneumonia, diarrhoea, tuberculosis (TB), hepatitis, malaria, and filariasis) were ordered by province to form groups without considering proximity because clusters are usually far apart. ARI, pneumonia, and diarrhoea were divided into toddler and adult infections, making 10 target diseases instead of seven. The set of biclusters formed based on the presence and level of these diseases included 7 diseases with moderate to high disease levels, 5 diseases (formed by 2 clusters), 3 diseases, 2 diseases, and a final order that only included adult diarrhoea. In 6 of 8 clusters, diarrhea was the most prevalent infectious disease in Indonesia, making its eradication a priority. Direct person-to-person infections like ARI, pneumonia, TB, and diarrhoea were found in 4-6 of 8 clusters. These diseases are more common and spread faster than vector-borne diseases like malaria and filariasis, making them more important.

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A binary biclustering algorithm based on the adjacency difference matrix for gene expression data analysis

Cited by 7 publications

References 26 publications

RNA barcode segments for SARS-CoV-2 identification from HCoVs and SARSr-CoV-2 lineages

RNA barcode segments for SARS-CoV-2 identification from HCoVs and SARSr-CoV-2 lineages

Biclustering Models Under Collinearity in Simulated Biological Experiments

Province clustering based on the percentage of communicable disease using the BCBimax biclustering algorithm

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