Multi-assignment clustering: Machine learning from a biological perspective
نویسندگان
چکیده
A common approach for analyzing large-scale molecular data is to cluster objects sharing similar characteristics. This assumes that genes with highly expression profiles are likely participating in a process. Biological systems extremely complex and challenging understand, proteins having multiple functions sometimes need be activated or expressed time-dependent manner. Thus, the strategies applied clustering of these molecules into groups key importance translation biologically interpretable findings. Here we implemented multi-assignment (MAsC) allows assigned clusters, rather than single ones as commonly used techniques. When high-throughput transcriptomics data, MAsC increased power downstream pathway analysis allowed identification pathways high biological relevance experimental setting studied. Multi-assignment also reduced noise partition by excluding low correlation all resulting clusters. Together, findings suggest our methodology facilitates knowledge. The method made available an R package on GitLab (https://gitlab.com/wolftower/masc).
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1 Ingrid Russell, University of Hartford, Department of Computer Science, West Hartford, CT 06117, [email protected], 860-768-4191. 2 Zdravko Markov, Central Connecticut State University, Department of Computer Science, New Britain, CT 06050, [email protected], 860-832-2723 3 Neli Zlatareva, Central Connecticut State University, Department of Computer Science, New Britain, CT 06050, zlatarev...
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ژورنال
عنوان ژورنال: Journal of Biotechnology
سال: 2021
ISSN: ['1873-4863', '0168-1656']
DOI: https://doi.org/10.1016/j.jbiotec.2020.12.002