Download 8th International Conference on Practical Applications of by Julio Saez-Rodriguez, Miguel P. Rocha, Florentino PDF

By Julio Saez-Rodriguez, Miguel P. Rocha, Florentino Fdez-Riverola, Juan F. De Paz Santana

ISBN-10: 3319075802

ISBN-13: 9783319075808

Biological and biomedical study are more and more pushed through experimental recommendations that problem our skill to examine, strategy and extract significant wisdom from the underlying info. The notable features of subsequent new release sequencing applied sciences, including novel and ever evolving distinctive varieties of omics information applied sciences, have positioned an more and more advanced set of demanding situations for the becoming fields of Bioinformatics and Computational Biology. The research of the datasets produced and their integration demand new algorithms and techniques from fields reminiscent of Databases, facts, info Mining, computing device studying, Optimization, machine technological know-how and synthetic Intelligence. in actual fact, Biology is an increasing number of a technology of knowledge requiring instruments from the computational sciences. within the previous few years, we've seen the surge of a brand new iteration of interdisciplinary scientists that experience a robust historical past within the organic and computational sciences. during this context, the interplay of researchers from various medical fields is, greater than ever, of most excellent value 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 integrated 34 papers spanning many various sub-fields in Bioinformatics and Computational Biology. consequently, the convention promotes the interplay of scientists from various examine teams and with a different historical past comparable 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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Segmentation is the process that divides an image into regions or objects whose pixels have similar attributes. Each segmented region typically has a physical significance within the image. It is one of the most important processes in an automated vision system because it makes it possible to extract the objects from the image for subsequent description and recognition. Segmentation techniques can be divided into three main groups: techniques based on the detection of edges or borders [16], thresholding techniques [18], and techniques based on clustering of pixels [11].

Although it has not been widely used in miRNA prediction, it has several advantages that make it suitable for miRNA prediction. RF has excellent predictive performance even when Improving miRNA Classification Using an Exhaustive Set of Features 35 Table 1. Selected set of features indicates that the feature has been utilized. indicates that the feature has not been utilized. etc the local contiguous sequence-structure triplets The minimal free energy of the folding Minimal free energy corrected for GC content Minimal free energy adjusted for Hairpin length Minimal free energy adjusted for Hairpin length corrected for GC content Minimal free energy adjusted for stem length Minimal free energy adjusted for stem length corrected for GC content P value of MFE of randomized sequences Z value of MFE of randomized sequences The partition function The normalized shannon entropy of the hairpin sequence The normalized shannon entropy of the hairpin structure The bulge ratio is ratio of asymmetrical bulges versus the stem alignment length Adjusted base pairing propensity measures the total number of base pairs presented in the secondary structure divided by the hairpin length the matech ratio equal the number of matches divided by the length of the stem alignment string considering bulges the max match count is the highest number of matches in 24 positions in the stem alignment string most data samples are noisy.

In the present work we use WEKA as the platform of our RF classifier. The default values of the parameters are considered. 3 Results and Discussions This section discusses the data sets that have been used for the training and testing of our classifier and also the obtained prediction results. 1 Data Sets The training dataset consists of 300 known human pre-miRNAs; retrieved from miRBase19 [24]; and 700 pseudo hairpins; extracted from human RefSeq genes [16]. A testing data set consisting of 500 known human pre-miRNAs and 1000 pseudo hairpins - different than those used in training - retrieved from miRBase19 [24] and human RefSeq genes [16]; respectively; have been used for testing the performance of the already trained classifier.

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