Identifying Co-expressed miRNAs using Multiobjective Optimization
22 Dec 2014-pp 245-250
TL;DR: The proposed method integrates the ability of point symmetry based distance and existing Multi-objective optimization based clustering technique-AMOSA to identify co-regulated or co-expressed miRNA clusters to help extraction of relevant information from expression data of miRNA.
Abstract: The micro RNAs or miRNAs are short non-coding RNAs, which are capable in regulating gene expression in post-transcriptional level. A huge volume of data is generated by expression profiling of miRNAs. From various studies it has been proved that a large proportion of miRNAs tend to form clusters on chromosome. So, in this article we are proposing a multi-objective optimization based clustering algorithm for extraction of relevant information from expression data of miRNA. The proposed method integrates the ability of point symmetry based distance and existing Multi-objective optimization based clustering technique-AMOSA to identify co-regulated or co-expressed miRNA clusters. The superiority of our proposed approach by comparing it with other state-of-the-art clustering methods, is demonstrated on two publicly available miRNA expression data sets using Davies-Bouldin index - an external cluster validity index.
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TL;DR: An automatic clustering technique using the search capability of multiobjective optimization which can automatically determine the relevant distance measure and the corresponding partitioning from a given data set is developed.
Abstract: Distance plays an important role in the clustering process for allocating data points to different clusters. Several distance or proximity measures have been developed and reported in the literature to determine dissimilarities between two given points. The choice of distance measure depends on a particular domain as well as different data sets of the same domain. It is important to automatically determine the appropriate distance measure which acts best for a particular data set. In this study we have developed an automatic clustering technique using the search capability of multiobjective optimization which can automatically determine the relevant distance measure and the corresponding partitioning from a given data set. Our proposed automated framework is generic in nature i.e., any number of different distance measures can be incorporated into it. In our work we have used four existing widely used distance measures, i.e., Euclidean, line symmetry, point symmetry and city block distance to be explored for each data set. In order to measure the richness of an obtained partitioning using a particular distance, four cluster validity indices, the Silhouette index, the DB index, the adjusted rand index and classification accuracy are used. A new encoding strategy which can encode the set of cluster centers and the particular distance function is used to represent the problem. The appropriate distance function and the corresponding partitioning are determined using the search capability of a multiobjective optimization based technique. The efficiency of the proposed technique is shown on clustering three microRNA and three microarray gene expression data sets having varying complexities. The results show the usefulness of the proposed automated approach.
8 citations
TL;DR: The problem of proper selection of weight values for different time points and then determining the proper partitioning from the given miRNA data set utilizing the similarity computed using the new set of weightvalues is formulated as an optimization problem where several cluster validity indices are optimized as the goodness measures.
Abstract: MicroRNAs (miRNAs) are a type of RNAs, which are responsible for monitoring the gene expression values. Recent research asserts that miRNAs form some clustering on chromosomes. The miRNAs belonging to a particular cluster are highly similar in terms of their activity and they are termed as “coregulated” miRNAs. The current paper presents an approach that simultaneously performs two tasks: i) clustering of miRNAs into different categories based on some similarity measures ii) identification of proper weight values for different time points with respect to which expression values are available. In general, a large number of expression values are available for a given miRNA data set. All these values may not be suitable to be used equally to measure the similarity between two miRNAs. In the current study, the problem of proper selection of weight values for different time points and then determining the proper partitioning from the given miRNA data set utilizing the similarity computed using the new set of weight values is formulated as an optimization problem where several cluster validity indices are optimized as the goodness measures. To that end, a multiobjective differential evolution based optimization technique is utilized. The supremacy of the proposed technique is tested on three miRNA data sets in comparison to some recent approaches in terms of some popular performance measures like Silhouette index and DB-index. The observations are further supported by statistical and biological significance tests. Supplementary information is available at https://www.iitp.ac.in/~sriparna/journals.html .
7 citations
Cites background from "Identifying Co-expressed miRNAs usi..."
...To overcome these limitations, in [14] authors have proposed an unsupervised based approach for performing automatic classification of miRNA data sets....
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TL;DR: The proposed mathematical optimization-based approaches to integrative analysis of NCI-60 human tumor cell lines characterized by gene expression and drug activity profiles outperform some state-of-the-art and traditional distance-based integrative and non-integrative clustering techniques.
Abstract: Recent advances in high-throughput technologies have given rise to collecting large amounts of multidimensional heterogeneous data that provide diverse information on the same biological samples Integrative analysis of such multisource datasets may reveal new biological insights into complex biological mechanisms and therefore remains an important research field in systems biology Most of the modern integrative clustering approaches rely on independent analysis of each dataset and consensus clustering, probabilistic or statistical modeling, while flexible distance-based integrative clustering techniques are sparsely covered We propose two distance-based integrative clustering frameworks based on bi-level and bi-objective extensions of the p-median problem A hybrid branch-and-cut method is developed to find global optimal solutions to the bi-level p-median model As to the bi-objective problem, an $\varepsilon$ -constraint algorithm is proposed to generate an approximation to the Pareto optimal set Every solution found by any of the frameworks corresponds to an integrative clustering We present an application of our approaches to integrative analysis of NCI-60 human tumor cell lines characterized by gene expression and drug activity profiles We demonstrate that the proposed mathematical optimization-based approaches outperform some state-of-the-art and traditional distance-based integrative and non-integrative clustering techniques
6 citations
Cites methods from "Identifying Co-expressed miRNAs usi..."
...Acharya and Saha [47] applied the simulated annealing-based multi-...
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...Acharya and Saha [47] applied the simulated annealing-based multiobjective optimization algorithm [48] combined with the Point symmetry distance to identify co-expressed miRNAs....
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01 Oct 2017
TL;DR: Surprisingly, inherent stochasticity also increases the robustness of the progenitor state and lessens the impact of unequal, random distribution of molecules at cell division on the temporal spread of differentiation at the population level.
Abstract: Recent studies suggest that cells make stochastic choices with respect to differentiation or division. The effect of molecule concentration on cell division rate is analysed in this work. However, the molecular mechanism underlying such stochasticity is unknown. Here, we computationally model the effects of molecule concentration (acts as noise) on the Hes1/miR-9 oscillator. Consequences of low molecular numbers of interacting species are determined experimentally by the researchers. We report that increased stochasticity spreads the timing of differentiation in a population, such that initially equivalent cells differentiate over a period of time. Surprisingly, inherent stochasticity also increases the robustness of the progenitor state and lessens the impact of unequal, random distribution of molecules at cell division on the temporal spread of differentiation at the population level. This advantageous use of biological noise contrasts with the view that noise needs to be counteracted.
Cites background from "Identifying Co-expressed miRNAs usi..."
...BACKGROUND The micro RNAs or are short non-coding RNAs, which are capable in regulating gene expression in posttranscriptional level [16]....
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References
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TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Abstract: There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.
41,772 citations
TL;DR: A new graphical display is proposed for partitioning techniques, where each cluster is represented by a so-called silhouette, which is based on the comparison of its tightness and separation, and provides an evaluation of clustering validity.
14,144 citations
"Identifying Co-expressed miRNAs usi..." refers methods in this paper
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Book•
01 Jan 2001
TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
Abstract: From the Publisher:
Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run. · Comprehensive coverage of this growing area of research · Carefully introduces each algorithm with examples and in-depth discussion · Includes many applications to real-world problems, including engineering design and scheduling · Includes discussion of advanced topics and future research · Features exercises and solutions, enabling use as a course text or for self-study · Accessible to those with limited knowledge of classical multi-objective optimization and evolutionary algorithms The integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing. This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
12,134 citations
TL;DR: A new, bead-based flow cytometric miRNA expression profiling method is used to present a systematic expression analysis of 217 mammalian miRNAs from 334 samples, including multiple human cancers, and finds the miRNA profiles are surprisingly informative, reflecting the developmental lineage and differentiation state of the tumours.
Abstract: Recent work has revealed the existence of a class of small non-coding RNA species, known as microRNAs (miRNAs), which have critical functions across various biological processes. Here we use a new, bead-based flow cytometric miRNA expression profiling method to present a systematic expression analysis of 217 mammalian miRNAs from 334 samples, including multiple human cancers. The miRNA profiles are surprisingly informative, reflecting the developmental lineage and differentiation state of the tumours. We observe a general downregulation of miRNAs in tumours compared with normal tissues. Furthermore, we were able to successfully classify poorly differentiated tumours using miRNA expression profiles, whereas messenger RNA profiles were highly inaccurate when applied to the same samples. These findings highlight the potential of miRNA profiling in cancer diagnosis.
9,470 citations
01 Jan 1988
9,439 citations
"Identifying Co-expressed miRNAs usi..." refers methods in this paper
...Any clustering([4], [5]) method is aimed to partitions n number of data-points into K number of clusters....
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