Browsing by Author "Chen, Jason"

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    Charting the Unexplored RNA-binding Protein Atlas of the Human Genome
    (Office of the Vice Chancellor for Research, 2012-04-13) Zhao, Huiying; Yang, Yuedong; Janga, Sarath Chandra; Chen, Jason; Zhu, Heng; Kao, Cheng; Zhou, Yaoqi
    Detecting protein-RNA interactions is challenging–both experimentally and computationally– because RNAs are large in number, diverse in cellular location and function, and flexible in structure. As a result, many RNA-binding proteins (RBPs) remain to be identified and characterized. Recently, we developed a bioinformatics tool called SPOT-Seq that integrates template-based structure prediction with RNA-binding affinity prediction to predict RBPs. Application of SPOT-Seq to human genome leads to doubling of RBPs from 2115 to 4296. Half of novel (>2000) RBPs are poorly or not annotated. The other half possesses Gene Ontology leaf IDs that are associated with known RBPs. In particular, we identified 36 novel RBPs in cancer, cardiovascular, diabetes and neurodegenerative pathways and 26 novel RBPs associated with disease-causing SNPs. Half of these disease-associating, predicted novel RBPs are annotated to interact with known RBPs. Accuracy of predicted novel RBPs is further validated by same confirmation rate of novel and annotated RBPs in human proteome microarrays experiments. The large number of predicted novel RBPs and their abundance in disease pathways and disease-causing SNPs are useful for hypothesis generation. These predicted novel human RBPs (>2000) with confidence level and their predicted complex structures with RNA can be downloaded from http://sparks.informatics.iupui.edu (yqzhou@iupui.edu)
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    Profiting from a contrarian application of technical trading rules in the US stock market
    (2009) Balsara, Nauzer; Chen, Jason; Zheng, Lin
    Using the variance ratio test, we cannot reject the random walk null hypothesis for three major U.S. stock market indexes between 1990 and 2005. Consistent with this result, we find that the naïve forecasting model based on the random walk assumption generates more accurate forecasts as compared to the ARIMA forecasting model. We find that the regular application of three commonly used technical trading rules (the moving average crossover rule, the channel breakout rule, and the Bollinger band breakout rule) under-perform the buy-and-hold strategy between 1990 and 2005. However, we observe significant positive returns on trades generated by the contrarian version of these three technical trading rules, even after considering a 0.5% transaction costs on all trades. Moreover, we find that while the contrarian version of these rules results in a significantly higher probability of success as compared to the regular version, it results in a significantly lower payoff ratio than that generated by the regular version.