Visualizing Biclustering Results on Gene Expression Data: A Survey

Aouabed, Haithem; Santamaría, Rodrigo; Elloumi, Mourad

doi:10.1145/3473258.3473284

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Considering a biclustering solution, the most popular options for biclustering visualization are heatmaps [ 69 , 163–165 ]. Visualizing a biclustering solution would resemble structures from Figure 14 in ideal conditions.…”

Section: Evaluation Measuresmentioning

confidence: 99%

Biclustering data analysis: a comprehensive survey

Castanho,

Aidos,

Madeira

2024

Briefings in Bioinformatics

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Biclustering, the simultaneous clustering of rows and columns of a data matrix, has proved its effectiveness in bioinformatics due to its capacity to produce local instead of global models, evolving from a key technique used in gene expression data analysis into one of the most used approaches for pattern discovery and identification of biological modules, used in both descriptive and predictive learning tasks. This survey presents a comprehensive overview of biclustering. It proposes an updated taxonomy for its fundamental components (bicluster, biclustering solution, biclustering algorithms, and evaluation measures) and applications. We unify scattered concepts in the literature with new definitions to accommodate the diversity of data types (such as tabular, network, and time series data) and the specificities of biological and biomedical data domains. We further propose a pipeline for biclustering data analysis and discuss practical aspects of incorporating biclustering in real-world applications. We highlight prominent application domains, particularly in bioinformatics, and identify typical biclusters to illustrate the analysis output. Moreover, we discuss important aspects to consider when choosing, applying, and evaluating a biclustering algorithm. We also relate biclustering with other data mining tasks (clustering, pattern mining, classification, triclustering, N-way clustering, and graph mining). Thus, it provides theoretical and practical guidance on biclustering data analysis, demonstrating its potential to uncover actionable insights from complex datasets.

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Section: Evaluation Measuresmentioning

confidence: 99%

Biclustering data analysis: a comprehensive survey

Castanho,

Aidos,

Madeira

2024

Briefings in Bioinformatics

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“…Biclustering is an unsupervised learning technique used to group data into clusters using multiple dimensions (rows and columns), unlike traditional clustering methods which only cluster rows. It is typically used for clustering genetic data when both genes and associated conditions are needed to obtain meaningful clusters [28], in which it is important for data points to be able to be clustered into more than one group. This technique has been used by Divina et al [29] who used biclustering to analyse the energy consuption of homes, with the aim of clustering both by location and time to find buildings which use similar amounts of energy during particular time periods.…”

Section: Biclusteringmentioning

confidence: 99%

Inter operator variability of machine learning researchers predicting all-cause mortality in patients admitted to intensive care unit

Jones,

Cleland,

et al. 2021

European Heart Journal

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Background The number of publications using machine learning (ML) to predict cardiovascular outcomes and identify clusters of patients at greater risk has risen dramatically in recent years. However, research papers which use ML often fail to provide sufficient information about their algorithms to enable results to be replicated by others in the same or different datasets. Aim To test the reproducibility of results from ML algorithms given three different levels of information commonly found in publications: model type alone, a description of the model, and complete algorithm. Methods MIMIC-III is a healthcare dataset comprising detailed information from over 60,000 intensive care unit (ICU) admissions from the Beth Israel Deaconess Medical Centre between 2001 and 2012. Access is available to everyone pending approval and completion of a short training course. Using this dataset, three models for predicting all-cause in-hospital mortality were created, two from a PhD student working in ML, and one from an existing research paper which used the same dataset and provided complete model information. A second researcher (a PhD student in ML and cardiology) was given the same dataset and was tasked with reproducing their results. Initially, this second researcher was told what type of model was created in each case, followed by a brief description of the algorithms. Finally, the complete algorithms from each participant were provided. In all three scenarios, recreated models were compared to original models using Area Under the Receiver Operating Characteristic Curve (AUC). Results After excluding those younger than 18 years and events with missing or invalid entries, 21,139 ICU admissions remained from 18,094 patients between 2001 and 2012, including 2,797 in-hospital deaths. Three models were produced: two Recurrent Neural Networks (RNNs) which differed significantly in internal weights and variables, and a Boosted Tree Classifier (BTC). The AUC of the first reproduced RNN matched that of the original RNN (Figure 1), however the second RNN and the BTC could not be reproduced given model type alone. As more information was provided about these algorithms, the results from the reproduced models matched the original results more closely. Conclusions In order to create clinically useful ML tools with results that are reproducible and consistent, it is vital that researchers share enough detail about their models. Model type alone is not enough to guarantee reproducibility. Although some models can be recreated with limited information, this is not always the case, and the best results are found when the complete algorithm is shared. These findings have huge relevance when trying to apply ML in clinical practice. Funding Acknowledgement Type of funding sources: None.

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