31,571 research outputs found
A Survey on Soft Subspace Clustering
Subspace clustering (SC) is a promising clustering technology to identify
clusters based on their associations with subspaces in high dimensional spaces.
SC can be classified into hard subspace clustering (HSC) and soft subspace
clustering (SSC). While HSC algorithms have been extensively studied and well
accepted by the scientific community, SSC algorithms are relatively new but
gaining more attention in recent years due to better adaptability. In the
paper, a comprehensive survey on existing SSC algorithms and the recent
development are presented. The SSC algorithms are classified systematically
into three main categories, namely, conventional SSC (CSSC), independent SSC
(ISSC) and extended SSC (XSSC). The characteristics of these algorithms are
highlighted and the potential future development of SSC is also discussed.Comment: This paper has been published in Information Sciences Journal in 201
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
Microbial community pattern detection in human body habitats via ensemble clustering framework
The human habitat is a host where microbial species evolve, function, and
continue to evolve. Elucidating how microbial communities respond to human
habitats is a fundamental and critical task, as establishing baselines of human
microbiome is essential in understanding its role in human disease and health.
However, current studies usually overlook a complex and interconnected
landscape of human microbiome and limit the ability in particular body habitats
with learning models of specific criterion. Therefore, these methods could not
capture the real-world underlying microbial patterns effectively. To obtain a
comprehensive view, we propose a novel ensemble clustering framework to mine
the structure of microbial community pattern on large-scale metagenomic data.
Particularly, we first build a microbial similarity network via integrating
1920 metagenomic samples from three body habitats of healthy adults. Then a
novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is
proposed and applied onto the network to detect clustering pattern. Extensive
experiments are conducted to evaluate the effectiveness of our model on
deriving microbial community with respect to body habitat and host gender. From
clustering results, we observed that body habitat exhibits a strong bound but
non-unique microbial structural patterns. Meanwhile, human microbiome reveals
different degree of structural variations over body habitat and host gender. In
summary, our ensemble clustering framework could efficiently explore integrated
clustering results to accurately identify microbial communities, and provide a
comprehensive view for a set of microbial communities. Such trends depict an
integrated biography of microbial communities, which offer a new insight
towards uncovering pathogenic model of human microbiome.Comment: BMC Systems Biology 201
Paradigm of tunable clustering using binarization of consensus partition matrices (Bi-CoPaM) for gene discovery
Copyright @ 2013 Abu-Jamous et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Clustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. Also, these algorithms cannot generate tight clusters that focus on their cores or wide clusters that overlap and contain all possibly relevant genes. In this paper, a new clustering paradigm is proposed. In this paradigm, all three eventualities of a gene being exclusively assigned to a single cluster, being assigned to multiple clusters, and being not assigned to any cluster are possible. These possibilities are realised through the primary novelty of the introduction of tunable binarization techniques. Results from multiple clustering experiments are aggregated to generate one fuzzy consensus partition matrix (CoPaM), which is then binarized to obtain the final binary partitions. This is referred to as Binarization of Consensus Partition Matrices (Bi-CoPaM). The method has been tested with a set of synthetic datasets and a set of five real yeast cell-cycle datasets. The results demonstrate its validity in generating relevant tight, wide, and complementary clusters that can meet requirements of different gene discovery studies.National Institute for Health Researc
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