Results 71 to 80 of about 3,388 (214)
BICLUSTERING APPLICATION IN INDONESIAN ECONOMIC AND PANDEMIC VULNERABILITY
Barekeng, 2022 Biclustering is an analytical tool to group data from two dimensions simultaneously. The analysis was first introduced by Hartigan (1972) and applied by Cheng and Church (2000) to the gene expression matrix.
Wiwik Andriyani Lestari Ningsih +2 more
doaj +1 more source
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
wiley +1 more source
A collaborative filtering recommendation algorithm based on biclustering
, 2015 Collaborative filtering has been widely used in many fields such as movie recommendation and e-commerce. However, there are still some problems such as data sparsity which restrict its further development. To address the data sparsity problem we proposed
Wang JS(汪家升) +5 more
core +1 more source
A biclustering algorithm based on a Bicluster Enumeration Tree: application to DNA microarray data
BioData Mining, 2009 Background In a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of rows coherent with groups of columns.
Ayadi Wassim +2 more
doaj +1 more source
DPCT: A Dynamic Method for Detecting Protein Complexes From TAP-Aware Weighted PPI Network
Frontiers in Genetics, 2020 Detecting protein complexes from the Protein-Protein interaction network (PPI) is the essence of discovering the rules of the cellular world. There is a large amount of PPI data available, generated from high throughput experimental data.
Ali SabziNezhad, Saeed Jalili
doaj +1 more source
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
wiley +1 more source
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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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
doaj +1 more source
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
doaj +1 more source