By Julio Saez-Rodriguez, Miguel P. Rocha, Florentino Fdez-Riverola, Juan F. De Paz Santana
Biological and biomedical study are more and more pushed by way of experimental strategies that problem our skill to examine, technique and extract significant wisdom from the underlying info. The striking functions of subsequent new release sequencing applied sciences, including novel and ever evolving detailed sorts of omics facts applied sciences, have positioned an more and more complicated set of demanding situations for the starting to be fields of Bioinformatics and Computational Biology. The research of the datasets produced and their integration demand new algorithms and methods from fields equivalent to Databases, data, information Mining, computing device studying, Optimization, desktop technological know-how and synthetic Intelligence. in actual fact, Biology is increasingly more a technology of knowledge requiring instruments from the computational sciences. within the previous couple of years, we've seen the surge of a brand new iteration of interdisciplinary scientists that experience a powerful historical past within the organic and computational sciences. during this context, the interplay of researchers from assorted clinical fields is, greater than ever, of superior significance boosting the examine efforts within the box and contributing to the schooling of a brand new iteration of Bioinformatics scientists. PACBB‘14 contributes to this attempt selling this fruitful interplay. PACBB'14 technical application incorporated 34 papers spanning many various sub-fields in Bioinformatics and Computational Biology. as a result, the convention promotes the interplay of scientists from assorted study teams and with a different heritage akin to laptop scientists, mathematicians or biologists.
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Extra resources for 8th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2014)
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Section 4 concludes the paper. 2 Methodology This section discusses the strategy of our method. 1 Features Used in the Proposed RF-Based Classiﬁer In an attempt to improve miRNA prediction, the proposed algorithm uses a set of features that have never been collectively used in previous works. A set of discriminative features is utilized in our classiﬁer. Scatter diagrams of some of the selected features are shown in Fig. 1. These scatter diagrams show how good the chosen features are. M. ElGokhy, T.
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