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Goa, India

Kothandan R.,VISTA Laboratory | Biswas S.,VISTA Laboratory
Computational Biology and Chemistry | Year: 2015

Since Ambros' discovery of small non-protein coding RNAs in the early 1990s, the past two decades have seen an upsurge in the number of reports of predicted microRNAs (miR), which have been implicated in various functions. The correlation of miRs with cancer has spurred the usage of this class of non-coding RNAs in various cancer therapies, although most of them are at trial stages. However, the experimental identification of a miR to be associated with cancer is still an elaborate, time-consuming process. To aid this process of miR association, we undertook an in-silico study involving the identification of global signatures in experimentally validated microRNAs associated with cancer. Subsequently, a support vector machine based two-step binary classifier system has been trained and modeled from the features extracted from the above study. A total of 60 distinguishing features were selected and ranked to form the feature set for classification - 26 of these extracted from the miR sequence itself, and the remainder from the thermodynamics of folding and the hybridized miRNA-mRNA structure. The two step classifier model - miRSEQ and miRINT had reasonably good performance measures with fairly high values of Matthew's correlation coefficient (MCC) values ranging from 0.72 to 0.82 (availability: https://sites.google.com/site/sumitslab/tools). © 2015 Elsevier Ltd. All rights reserved. Source

Kothandan R.,VISTA Laboratory | Biswas S.,VISTA Laboratory
Current Bioinformatics | Year: 2016

The discovery of microRNAs (miRs) in the 1990's spawned a genre of research which has thrown light on the involvement of these small non-coding RNAs in several developmental pathways and diseases, one of which happens to be cancer. While algorithms which predict the binding of miRNAs to their targets are abundant, the same is not true for the association of miRNAs to targets which can be implicated in cancer. Machine learning approaches, which have been implemented in target prediction need to be extrapolated with proper feature selection to reach an acceptable level of accuracy in the prediction of associations of miRNAs to cancer. In this study we present a comparison of three different learning algorithms viz., the kernel-based Support Vector Machines (SVM), Decision Tree-based Random Forest (RF) and C4.5 to predict miRNAs associated with cancer. 60 informative features were extracted from a dataset of experimentally validated miRNA based on sequence, thermodynamics of miRNA-mRNA binding and their hybridization. Initially, features were ranked based on F-score and a two-stage Recursive Feature Elimination (RFE) process was employed to select the optimal subset of features for individual classifier. Class imbalance in the training set was overcome by employing cost-sensitive approach. The performance of each individual learning algorithm was evaluated in terms of precision, recall, F-measure and AUC. Subsequently, the learning algorithm with better performance measure would be utilized for constructing a two-step binary classifier viz., miRSEQ and miRINT, which will identify a miRNA to be associated with the cancer pathway. Based on our comparative analysis, it was evident that the decision tree based RF model performed well in terms of better precision and AUC (for miRSEQ), but was moderate (for miRINT). © 2016 Bentham Science Publishers. Source

Chouhan O.P.,VISTA Laboratory | Bandekar D.,VISTA Laboratory | Hazra M.,Kalyani University | Baghudana A.,VISTA Laboratory | And 2 more authors.
AMB Express | Year: 2016

Vibrio cholerae, the cause of seven noted pandemics, leads a dual lifecycle—one in the human host in its virulent form, and the other as a sessile, non-virulent bacterium in aquatic bodies in surface biofilms. Surface biofilms have been attributed to be associated with a ubiquitous protein domain present in all branches of bacteria, known as the GGD(/E)EF domain. While the diguanlyate cyclase activities of these proteins are universally established, the role of these proteins as diguanlyate-specific phosphodiesterases in conjunction with a EAL domain has also been reported. The VC0395_0300 protein from V. cholerae which shows biofilm forming abilities also acts as a phosphodiesterase. Interestingly, this GGD(/E)EF protein contains a EAL site in the reverse orientation. We attempted to mutate the GGEEF signature along the sequence by site-directed mutagenesis. The resultant mutants (Sebox5–7) did not show much difference in phosphodiesterase activity in comparison with the wild type protein (Sebox3), indicating the independence of the phosphodiesterase activity of the protein from the GGD(/E)EF domain. However, the ability of the mutants to form surface biofilm was significantly lesser in the case of mutations in the three central positions of the signature domain. © 2015, Chouhan et al. Source

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