Results 71 to 80 of about 3,867 (213)
BicAT: a biclustering analysis toolbox [PDF]
Bioinformatics, 2006 Abstract Summary: Besides classical clustering methods such as hierarchical clustering, in recent years biclustering has become a popular approach to analyze biological data sets, e.g. gene expression data. The Biclustering Analysis Toolbox (BicAT) is a software platform for clustering-based data analysis that integrates various ...
Barkow, Simon +4 more
openaire +5 more sources
Imaging‐genomic spatial‐modality attentive fusion for studying neuropsychiatric disorders
Human Brain Mapping, Volume 45, Issue 17, December 1, 2024.Attentive fusion of neuroimaging and genomics data classify schizophrenia (SZ) with high precision. The attention scores provide the most contributing imaging‐genetics features for characterizing the disorder. The proposed fusion module is self‐explaining; interprets how each biological sources complement the other and leverage their combination to ...
Md Abdur Rahaman +9 more
wiley +1 more source
Biclustering via sparse clustering
Biometrics, 2019 AbstractIn identifying subgroups of a heterogeneous disease or condition, it is often desirable to identify both the observations and the features which differ between subgroups. For instance, it may be that there is a subgroup of individuals with a certain disease who differ from the rest of the population based on the expression profile for only a ...
Helgeson, Erika S. +4 more
openaire +4 more sources
Onset of a conceptual outline map to get a hold on the jungle of cluster analysis
WIREs Data Mining and Knowledge Discovery, Volume 14, Issue 5, September/October 2024.Abstract The domain of cluster analysis is a meeting point for a very rich multidisciplinary encounter, with cluster‐analytic methods being studied and developed in discrete mathematics, numerical analysis, statistics, data analysis, data science, and computer science (including machine learning, data mining, and knowledge discovery), to name but a few.
Iven Van Mechelen +2 more
wiley +1 more source
Fast Biclustering by Dual Parameterization
CoRR, 2015 We study two clustering problems, Starforest Editing, the problem of adding and deleting edges to obtain a disjoint union of stars, and the generalization Bicluster Editing. We show that, in addition to being NP-hard, none of the problems can be solved in subexponential time unless the exponential time hypothesis fails.
Pål Grønås Drange +3 more
openaire +6 more sources
Algorithms for Molecular Biology, 2009 Background The ability to monitor the change in expression patterns over time, and to observe the emergence of coherent temporal responses using gene expression time series, obtained from microarray experiments, is critical to advance our understanding ...
Madeira Sara C, Oliveira Arlindo L
doaj +1 more source
Known by Who We Follow: A Biclustering Application to Community Detection
IEEE Access, 2020 The detection of communities in social networks is a task with multiple applications both in research and in sectors such as marketing and politics among others.
Juan M. Cotelo +4 more
doaj +1 more source
Molecular Oncology, Volume 18, Issue 8, Page 2042-2059, August 2024.DNA methylation and machine learning (LASSO leave‐one‐out) was used to identify and validate a signature to identify patients responding to chemotherapy and bevacizumab (pCR: pathological complete response). Further, by integrating DNA methylation and gene expression, we identified alterations in DNA methylation of enhancer CpGs in GRHL2‐binding ...
Thomas Fleischer +9 more
wiley +1 more source
Detecting Cancer Survival Related Gene Markers Based on Rectified Factor Network
Frontiers in Bioengineering and Biotechnology, 2020 Detecting gene sets that serve as biomarkers for differentiating patient survival groups may help diagnose diseases robustly and develop multi-gene targeted therapies.
Lingtao Su +5 more
doaj +1 more source
Propagation-Based Biclustering Algorithm for Extracting Inclusion-Maximal Motifs [PDF]
, 2016 Biclustering, which is simultaneous clustering of columns and rows in data matrix, became an issue when classical clustering algorithms proved not to be good enough to detect similar expressions of genes under subset of conditions.
Boryczko, Krzysztof, Orzechowski, Patryk
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