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Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397

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  • Article
    Citation - Scopus: 25
    Recursive Cluster Elimination Based Rank Function (SVM-RCE-R) Implemented in KNIME
    (F1000 Research Ltd, 2021-01-05) Yousef, Malik; Bakir-Güngör, Burcu; Jabeer, Amhar; Göy, Gökhan; Qureshi, Rehman A.; C Showe, Louise; C. Showe, Louise
    In our earlier study, we proposed a novel feature selection approach, Recursive Cluster Elimination with Support Vector Machines (SVM-RCE) and implemented this approach in Matlab. Interest in this approach has grown over time and several researchers have incorporated SVM-RCE into their studies, resulting in a substantial number of scientific publications. This increased interest encouraged us to reconsider how feature selection, particularly in biological datasets, can benefit from considering the relationships of those genes in the selection process, this led to our development of SVM-RCE-R. SVM-RCE-R, further enhances the capabilities of SVM-RCE by the addition of a novel user specified ranking function. This ranking function enables the user to stipulate the weights of the accuracy, sensitivity, specificity, f-measure, area under the curve and the precision in the ranking function This flexibility allows the user to select for greater sensitivity or greater specificity as needed for a specific project. The usefulness of SVM-RCE-R is further supported by development of the maTE tool which uses a similar approach to identify MicroRNA (miRNA) targets. We have also now implemented the SVM-RCE-R algorithm in Knime in order to make it easier to applyThe use of SVM-RCE-R in Knime is simple and intuitive and allows researchers to immediately begin their analysis without having to consult an information technology specialist. The input for the Knime implemented tool is an EXCEL file (or text or CSV) with a simple structure and the output is also an EXCEL file. The Knime version also incorporates new features not available in SVM-RCE. The results show that the inclusion of the ranking function has a significant impact on the performance of SVM-RCE-R. Some of the clusters that achieve high scores for a specified ranking can also have high scores in other metrics. © 2021 Elsevier B.V., All rights reserved.
  • Book Part
    Citation - Scopus: 4
    Computational Detection of Pre-MicroRNAs
    (Humana Press Inc., 2021-08-26) Saçar Demirci, Müşerref Duygu
    MicroRNA (miRNA) studies have been one of the most popular research areas in recent years. Although thousands of miRNAs have been detected in several species, the majority remains unidentified. Thus, finding novel miRNAs is a vital element for investigating miRNA mediated posttranscriptional gene regulation machineries. Furthermore, experimental methods have challenging inadequacies in their capability to detect rare miRNAs, and are also limited to the state of the organism under examination (e.g., tissue type, developmental stage, stress-disease conditions). These issues have initiated the creation of high-level computational methodologies endeavoring to distinguish potential miRNAs in silico. On the other hand, most of these tools suffer from high numbers of false positives and/or false negatives and as a result they do not provide enough confidence for validating all their predictions experimentally. In this chapter, computational difficulties in detection of pre-miRNAs are discussed and a machine learning based approach that has been designed to address these issues is reviewed. © 2021 Elsevier B.V., All rights reserved.