Omics phenotyping has become increasingly recognizedin our path to Precision Medicine. A major computationalchallenge of our investigator community is to identify the necessarydata analytical tools to process multi-omics data. To aidnavigation of the analytical tools fragmented across the web, wehave created a novel computational resource platform, AZTEC,that empowers users to simultaneously search a diverse arrayof digital resources including databases, standalone software,web services, publications, and large libraries composed ofmany interrelated functions. AZTEC fosters an environment ofsustainable resource support and discovery, enabling researchersto overcome the challenges of information science. An online videoof the demonstration can be viewed at https://www.youtube.com/watch?v=FIxvlof6Kbg.

Topic modeling has increasingly attracted interests from researchers. Common methods of topic modeling usually produce a collection of unlabeled topics where each topic is depicted by a distribution of words. Associating semantic meaning with these word distributions is not always straightforward. Traditionally, this task is left to human interpretation. Manually labeling the topics is unfortunately not always easy, as topics generated by unsupervised learning methods do not necessarily align well with our prior knowledge in the subject domains. Currently, two approaches to solve this issue exist. The first is a post-processing procedure that assigns each topic with a label from the prior knowledge base that is semantically closest to the word distribution of the topic. The second is a supervised topic modeling approach that restricts the topics to a predefined set whose word distributions are provided beforehand. Neither approach is ideal, as the former may produce labels that do not accurately describe the word distributions, and the latter lacks the ability to detect unknown topics that are crucial to enrich our knowledge base. Our goal in this paper is to introduce a semisupervised Latent Dirichlet allocation (LDA) model, Source-LDA, which incorporates prior knowledge to guide the topic modeling process to improve both the quality of the resulting topics and of the topic labeling. We accomplish this by integrating existing labeled knowledge sources representing known potential topics into a probabilistic topic model. These knowledge sources are translated into a distribution and used to set the hyperparameters of the Dirichlet generated distribution over words. This approach ensures that the topic inference process is consistent with existing knowledge, and simultaneously, allows for discovery of new topics. The results show improved topic generation and increased accuracy in topic labeling when compared to those obtained using various labeling approaches based off LDA.

MOTIVATION: An important challenge in the use of large-scale gene expression data for biological classification occurs when the expression dataset being analyzed involves multiple classes. Key issues that need to be addressed under such circumstances are the efficient selection of good predictive gene groups from datasets that are inherently 'noisy', and the development of new methodologies that can enhance the successful classification of these complex datasets. METHODS: We have applied genetic algorithms (GAs) to the problem of multi-class prediction. A GA-based gene selection scheme is described that automatically determines the members of a predictive gene group, as well as the optimal group size, that maximizes classification success using a maximum likelihood (MLHD) classification method. RESULTS: The GA/MLHD-based approach achieves higher classification accuracies than other published predictive methods on the same multi-class test dataset. It also permits substantial feature reduction in classifier genesets without compromising predictive accuracy. We propose that GA-based algorithms may represent a powerful new tool in the analysis and exploration of complex multi-class gene expression data. AVAILABILITY: Supplementary information, data sets and source codes are available at http://www.omniarray.com/bioinformatics/GA.

Many techniques have been proposed for modeling source rates generated by variable bit rate video coders. However, little work has been done to verify these models, especially in the area of measuring the quality of their predictions for network performance and the grade of service experienced by a customer. In this paper, we summarize the previous work in this area, highlight some of the difficulties in effectively verifying these models, and suggest methods by which source rate models for VBR video might be more effectively verified in the future. In addition to studying the problem of model verification, a new model is proposed. Using this model, a video sequence is characterized by four parameters. As in previous cases, the number of bits generated by the coder for a sequence of video frames is modelled. The new model differs from previous models in that this sequence is not Markovian.>