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Algorithms for Molecular Biology, 2010 Background Biclustering is an important analysis procedure to understand the biological mechanisms from microarray gene expression data. Several algorithms have been proposed to identify biclusters, but very little effort was made to compare the ...
Karuturi R Krishna Murthy, Chia Burton
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Deep learning methods for protein function prediction
PROTEOMICS, Volume 25, Issue 1-2, January 2025.Abstract Predicting protein function from protein sequence, structure, interaction, and other relevant information is important for generating hypotheses for biological experiments and studying biological systems, and therefore has been a major challenge in protein bioinformatics.
Frimpong Boadu +2 more
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Biclustering Algorithms Based on Metaheuristics: A Review
, 2023 International audienceBiclustering is an unsupervised machine learning technique that simultaneously clusters rows and columns in a data matrix. Biclustering has emerged as an important approach and plays an essential role in various applications such as
José-García, Adán +3 more
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Robust knowledge-guided biclustering for multi-omics data [PDF]
, 2023 Biclustering is a useful method for simultaneously grouping samples and features and has been applied across various biomedical data types. However, most existing biclustering methods lack the ability to integratively analyze multi-modal data such as ...
Zhang, Qiyiwen +2 more
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Biclustering Gene Expression Data with Subspace Evolution
AlgorithmsBiclustering is crucial for gene expression data analysis, but evolutionary algorithm-based methods often suffer from high computational costs. To address this, we propose a novel subspace evolution-based biclustering method that significantly reduces ...
Jianjun Sun +4 more
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Journal of Integrative Bioinformatics, 2011 The constant drive towards a more personalized medicine led to an increasing interest in temporal gene expression analyzes. It is now broadly accepted that considering a temporal perspective represents a great advantage to better understand disease ...
Carreiro André V. +3 more
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Computational Methods for Data Integration and Imputation of Missing Values in Omics Datasets
PROTEOMICS, Volume 25, Issue 1-2, January 2025.ABSTRACT Molecular profiling of different omic‐modalities (e.g., DNA methylomics, transcriptomics, proteomics) in biological systems represents the basis for research and clinical decision‐making. Measurement‐specific biases, so‐called batch effects, often hinder the integration of independently acquired datasets, and missing values further hamper the ...
Yannis Schumann +2 more
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IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2022 Under-five poverty is a condition where the needs of toodlers are not met, resulting in undernourished children and unable to reach their full potential in the social sphere.
Rahmadathul Wisdawati +3 more
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Alzheimer's &Dementia, Volume 20, Issue S1, December 2024.Abstract Background Alzheimer’s disease (AD) exhibits considerable phenotypic heterogeneity, suggesting the potential existence of subtypes. AD is under substantial genetic influence, thus identifying systematic variation in genetic risk may provide insights into disease origins. We previously identified a genetic heterogeneity across two levels.
Jeremy A. Elman +2 more
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