Learning a Hybrid Architecture for Sequence Regression and Annotation

When learning a hidden Markov model (HMM), sequen- tial observations can often be complemented by real-valued summary response variables generated from the path of hid- den states. Such settings arise in numerous domains, includ- ing many applications in biology, like motif discovery and genome annotation. In this paper, we present a flexible frame- work for jointly modeling both latent sequence features and the functional mapping that relates the summary response variables to the hidden state sequence. The algorithm is com- patible with a rich set of mapping functions. Results show that the availability of additional continuous response vari- ables can simultaneously improve the annotation of the se- quential observations and yield good prediction performance in both synthetic data and real-world datasets.Comment: AAAI 201

Empirical analysis of current status data for additive hazards model with auxiliary covariates

summary:In practice, it often occurs that some covariates of interest are not measured because of various reasons, but there may exist some auxiliary information available. In this case, an issue of interest is how to make use of the available auxiliary information for statistical analysis. This paper discusses statistical inference problems in the context of current status data arising from an additive hazards model with auxiliary covariates. An empirical log-likelihood ratio statistic for the regression parameter vector is defined and its limiting distribution is shown to be a standard chi-squared distribution. A profile empirical log-likelihood ratio statistic for a sub-vector of the parameters and its asymptotic distribution are also studied. To assess the finite sample performance of the proposed methods, simulation studies are implemented and simulation results show that the methods work well

Political dynamics in land commodification: Commodifying rural land development rights in Chengdu, China

Ministry of Education, Singapore under its Academic Research Funding Tier

Construction of a nasopharyngeal carcinoma 2D/MS repository with Open Source XML Database – Xindice

BACKGROUND: Many proteomics initiatives require integration of all information with uniformcriteria from collection of samples and data display to publication of experimental results. The integration and exchanging of these data of different formats and structure imposes a great challenge to us. The XML technology presents a promise in handling this task due to its simplicity and flexibility. Nasopharyngeal carcinoma (NPC) is one of the most common cancers in southern China and Southeast Asia, which has marked geographic and racial differences in incidence. Although there are some cancer proteome databases now, there is still no NPC proteome database. RESULTS: The raw NPC proteome experiment data were captured into one XML document with Human Proteome Markup Language (HUP-ML) editor and imported into native XML database Xindice. The 2D/MS repository of NPC proteome was constructed with Apache, PHP and Xindice to provide access to the database via Internet. On our website, two methods, keyword query and click query, were provided at the same time to access the entries of the NPC proteome database. CONCLUSION: Our 2D/MS repository can be used to share the raw NPC proteomics data that are generated from gel-based proteomics experiments. The database, as well as the PHP source codes for constructing users' own proteome repository, can be accessed at

Regulatory network of GSK3-like kinases and their role in plant stress response

Glycogen synthase kinase 3 (GSK3) family members are evolutionally conserved Ser/Thr protein kinases in mammals and plants. In plants, the GSK3s function as signaling hubs to integrate the perception and transduction of diverse signals required for plant development. Despite their role in the regulation of plant growth and development, emerging research has shed light on their multilayer function in plant stress responses. Here we review recent advances in the regulatory network of GSK3s and the involvement of GSK3s in plant adaptation to various abiotic and biotic stresses. We also discuss the molecular mechanisms underlying how plants cope with environmental stresses through GSK3s-hormones crosstalk, a pivotal biochemical pathway in plant stress responses. We believe that our overview of the versatile physiological functions of GSK3s and underlined molecular mechanism of GSK3s in plant stress response will not only opens further research on this important topic but also provide opportunities for developing stress-resilient crops through the use of genetic engineering technology

An Empirical Study of Classifier Combination on Cross-Project Defect Prediction

Abstract—To help developers better allocate testing and de-bugging efforts, many software defect prediction techniques have been proposed in the literature. These techniques can be used to predict classes that are more likely to be buggy based on past history of buggy classes. These techniques work well as long as a sufficient amount of data is available to train a prediction model. However, there is rarely enough training data for new software projects. To deal with this problem, cross-project defect prediction, which transfers a prediction model trained using data from one project to another, has been proposed and is regarded as a new challenge for defect prediction. So far, only a few cross-project defect prediction techniques have been proposed. To advance the state-of-the-art, in this work, we investigate 7 composite algorithms, which integrate multiple machine learning classifiers, to improve cross-project defect prediction. To evaluate the performance of the composite algorithms, we perform exper-iments on 10 open source software systems from the PROMISE repository which contain a total of 5,305 instances labeled as defective or clean. We compare the composite algorithms with CODEPLogistic, which is the latest cross-project defect prediction algorithm proposed by Panichella et al. [1], in terms of two standard evaluation metrics: cost effectiveness and F-measure. Our experiment results show that several algorithms outperform CODEPLogistic: Max performs the best in terms of F-measure and its average F-measure outperforms that of CODEPLogistic by 36.88%. BaggingJ48 performs the best in terms of cost effectiveness and its average cost effectiveness outperforms that of CODEPLogistic by 15.34%