City Block Distance for Identification of Co-expressed MicroRNAs
19 Dec 2013-pp 387-396
TL;DR: The proposed method judiciously integrates the merits of robust rough-fuzzy c-means algorithm and normalized range-normalized city block distance to discover co-expressed miRNA clusters and helps to handle minute differences between two miRNA expression profiles.
Abstract: The microRNAs or miRNAs are short, endogenous RNAs having ability to regulate gene expression at the post-transcriptional level. Various studies have revealed that a large proportion of miRNAs are co-expressed. Expression profiling of miRNAs generates a huge volume of data. Complicated networks of miRNA-mRNA interaction increase the challenges of comprehending and interpreting the resulting mass of data. In this regard, this paper presents the application of city block distance in order to extract meaningful information from miRNA expression data. The proposed method judiciously integrates the merits of robust rough-fuzzy c-means algorithm and normalized range-normalized city block distance to discover co-expressed miRNA clusters. The city block distance is used to calculate the membership functions of fuzzy sets, and thereby helps to handle minute differences between two miRNA expression profiles. The effectiveness of the proposed approach, along with a comparison with other related methods, is demonstrated on several miRNA expression data sets using different cluster validity indices and gene ontology.
Citations
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22 Dec 2014
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.
4 citations
Cites background or methods or result from "City Block Distance for Identificat..."
...In this section, we have reported the results of [16] for six upper mentioned algorithms with their best DB index values (either for Euclidean distance or NRNCBD) for two miRNA microarray data sets, GSE16473 and GSE29495 and compared their results with our proposed clustering algorithm’s outcome....
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...In [16] authors have incorporated range-normalized city block distance(NRNCBD) instead of Euclidean distance in robust rough Fuzzy c-means(rRFCM) [30] clustering algorithm....
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...In [16] authors have shown the superioty of NRNCBD over Euclidean and Pearson distance version of different clustering algorithms like fuzzy c-means (FCM)[26], hard c-means (HCM)[7], rough-fuzzy c-means (RFCM)[27] and Robust rough-fuzzy c-means(rRFCM) [17]....
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...Recently in [16] a clustering algorithm, combining the concepts of robust rough-fuzzy cmeans algorithm [17] and Normalized range-normalized cityblock distance(NRNCBD) is proposed to discover co-regulated miRNAs from datasets of miRNA expression data....
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...As we can see in the table that for different clustering algorithms its best performance with respect DB index from [16] are reported....
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12 Mar 2018
TL;DR: This paper has proposed a point symmetry-based clustering algorithm which has been used to identify clusters of tissue samples from some real life cancer datasets and is also multi-objective-optimisation (MOO) based, i.e., optimises more than one objectives simultaneously.
Abstract: Clustering or unsupervised classification techniques can be used to solve different types of classification problems of different domains. Symmetry is an important property for any real life object. Therefore, symmetry-based distance measurements play some important roles in identifying some patterns or clusters of real life datasets. In this paper, inspired by the symmetric property, we have proposed a point symmetry-based clustering algorithm which has been used to identify clusters of tissue samples from some real life cancer datasets. Our proposed algorithm is also multi-objective-optimisation (MOO) based, i.e., optimises more than one objectives simultaneously. We have also shown the superiority of our proposed algorithm with respect to some state-of-the-art clustering algorithms.
3 citations
Cites background or methods from "City Block Distance for Identificat..."
..., 2010; Paul and Maji, 2013) for measuring similarity between two given data points, Euclidean distance or city block distance (Paul and Maji, 2013) is in general used....
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...In most of the existing literatures (Acharya et al., 2014; Mukhopadhyay et al., 2010; Paul and Maji, 2013) for measuring similarity between two given data points, Euclidean distance or city block distance (Paul and Maji, 2013) is in general used....
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...For example, microRNA datasets used in Paul and Maji (2013) or real life gene expression datasets used in Saha et al. (2013) are some unlabeled datasets....
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TL;DR: In this article , three kinds of brain tumors (a meningioma, a glioma and a pituitary tumor) were detected using machine learning algorithms, such as Support Vector Machines (SVM), K-Nearest Neighbor (KNN), and GRNN.
Abstract: The brain is the organ that controls the activities of all parts of the body. The tumor is familiar as an irregular outgrowth of tissue. Brain tumors are an abnormal lump of tissue in which cells grow up and redouble uncontrollably. It is categorized into different types based on their nature, origin, growth rate, and stage of progress. Detection of the tumor by traditional methods is time-consuming and does not widen to diagnose a large amount of data and is less accurate. So, the automatic diagnosis of the tumors in the brain by magnetic resonance imaging (MRI) plays a very important role in computer-aided diagnosis. This paper concentrates on the diagnosis of three kinds of brain tumors (a meningioma, a glioma, and a pituitary tumor). Machine learning algorithms: KNN, SVM, and GRNN are suggested to increase accuracy and reduce diagnostic time by using a publicly available dataset, features that are extracted of images, data pre-processing methods, and the principal component analysis (PCA). This paper aims to minimize the training time of the suggested algorithms. The dimensionality reducing technique is applied to the dataset and diagnosis using machine learning algorithms, such as Support Vector Machines (SVM), K-Nearest Neighbor (KNN), and Generalized Regression Neural Networks (GRNN). The accuracies of the algorithms used in diagnosing tumors are 97%, 96.24%, and 94.7% for KNN, SVM, and GRNN, respectively. The KNN is therefore regarded as the algorithm of choice.
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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.
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7,826 citations