Browsing by Subject "Support vector machines"

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Blind image and video quality assessment using natural scene and motion models
(2013-05) Saad, Michele Antoine; Bovik, Alan C. (Alan Conrad), 1958-
We tackle the problems of no-reference/blind image and video quality evaluation. The approach we take is that of modeling the statistical characteristics of natural images and videos, and utilizing deviations from those natural statistics as indicators of perceived quality. We propose a probabilistic model of natural scenes and a probabilistic model of natural videos to drive our image and video quality assessment (I/VQA) algorithms respectively. The VQA problem is considerably different from the IQA problem since it imposes a number of challenges on top of the challenges faced in the IQA problem; namely the challenges arising from the temporal dimension in video that plays an important role in influencing human perception of quality. We compare our IQA approach to the state of the art in blind, reduced reference and full-reference methods, and we show that it is top performing. We compare our VQA approach to the state of the art in reduced and full-reference methods (no blind VQA methods that perform reliably well exist), and show that our algorithm performs as well as the top performing full and reduced reference algorithms in predicting human judgments of quality.
Data mining techniques for classifying RNA folding structures
(2016-08) Kim, Vince; Garg, Vijay K. (Vijay Kumar), 1963-; Gutell, Robin R
RNA is a crucial biological molecule that is critical for protein synthesis. Significant research has been done on folding algorithms for RNA, in particular the 16S rRNA of bacteria and archaea. Rather than modifying current works on these folding algorithms, this report ventures into the pioneering works for data mining the same 16S rRNA. Initial works were based on a single complex helix across seven organisms. However, classification analysis proved to be inaccurate due to severe multicollinearity in the data set. A secondary data mining analysis was done on the entire RNA sequence of the same seven organisms, and was successfully used to sequentially categorically predict the characteristic of a given nucleotide in the RNA sequence.
Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses
(Public Library of Science, 2013-05-01) Singh-Blom, U. Martin; Natarajan, Nagarajan; Tewari, Ambuj; Woods, John O.; Dhillon, Inderjit S.; Marcotte, Edward M.
Correctly identifying associations of genes with diseases has long been a goal in biology. With the emergence of large-scale gene-phenotype association datasets in biology, we can leverage statistical and machine learning methods to help us achieve this goal. In this paper, we present two methods for predicting gene-disease associations based on functional gene associations and gene-phenotype associations in model organisms. The first method, the Katz measure, is motivated from its success in social network link prediction, and is very closely related to some of the recent methods proposed for gene-disease association inference. The second method, called CATAPULT (Combining dATa Across species using Positive-Unlabeled Learning Techniques), is a supervised machine learning method that uses a biased support vector machine where the features are derived from walks in a heterogeneous gene-trait network. We study the performance of the proposed methods and related state-of-the-art methods using two different evaluation strategies, on two distinct data sets, namely OMIM phenotypes and drug-target interactions. Finally, by measuring the performance of the methods using two different evaluation strategies, we show that even though both methods perform very well, the Katz measure is better at identifying associations between traits and poorly studied genes, whereas CATAPULT is better suited to correctly identifying gene-trait associations overall. The authors want to thank Jon Laurent and Kris McGary for some of the data used, and Li and Patra for making their code available. Most of Ambuj Tewari's contribution to this work happened while he was a postdoctoral fellow at the University of Texas at Austin.