21,886 research outputs found
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
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
Stable Feature Selection for Biomarker Discovery
Feature selection techniques have been used as the workhorse in biomarker
discovery applications for a long time. Surprisingly, the stability of feature
selection with respect to sampling variations has long been under-considered.
It is only until recently that this issue has received more and more attention.
In this article, we review existing stable feature selection methods for
biomarker discovery using a generic hierarchal framework. We have two
objectives: (1) providing an overview on this new yet fast growing topic for a
convenient reference; (2) categorizing existing methods under an expandable
framework for future research and development
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
Transcription Factor-DNA Binding Via Machine Learning Ensembles
We present ensemble methods in a machine learning (ML) framework combining
predictions from five known motif/binding site exploration algorithms. For a
given TF the ensemble starts with position weight matrices (PWM's) for the
motif, collected from the component algorithms. Using dimension reduction, we
identify significant PWM-based subspaces for analysis. Within each subspace a
machine classifier is built for identifying the TF's gene (promoter) targets
(Problem 1). These PWM-based subspaces form an ML-based sequence analysis tool.
Problem 2 (finding binding motifs) is solved by agglomerating k-mer (string)
feature PWM-based subspaces that stand out in identifying gene targets. We
approach Problem 3 (binding sites) with a novel machine learning approach that
uses promoter string features and ML importance scores in a classification
algorithm locating binding sites across the genome. For target gene
identification this method improves performance (measured by the F1 score) by
about 10 percentage points over the (a) motif scanning method and (b) the
coexpression-based association method. Top motif outperformed 5 component
algorithms as well as two other common algorithms (BEST and DEME). For
identifying individual binding sites on a benchmark cross species database
(Tompa et al., 2005) we match the best performer without much human
intervention. It also improved the performance on mammalian TFs.
The ensemble can integrate orthogonal information from different weak
learners (potentially using entirely different types of features) into a
machine learner that can perform consistently better for more TFs. The TF gene
target identification component (problem 1 above) is useful in constructing a
transcriptional regulatory network from known TF-target associations. The
ensemble is easily extendable to include more tools as well as future PWM-based
information.Comment: 33 page
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