Summary: Chemical-genomic approaches that map interactions between small molecules and genetic perturbations offer a promising strategy for functional annotation of uncharacterized bioactive compounds. We recently developed a new high-throughput platform for mapping chemical-genetic (CG) interactions in yeast that can be scaled to screen large compound collections, and we applied this system to generate CG interaction profiles for more than 13 000 compounds. When integrated with the existing global yeast genetic interaction network, CG interaction profiles can enable mode-of-action prediction for previously uncharacterized compounds as well as discover unexpected secondary effects for known drugs. To facilitate future analysis of these valuable data, we developed a public database and web interface named MOSAIC. The website provides a convenient interface for querying compounds, bioprocesses (Gene Ontology terms) and genes for CG information including direct CG interactions, bioprocesses and gene-level target predictions. MOSAIC also provides access to chemical structure information of screened molecules, chemical-genomic profiles and the ability to search for compounds sharing structural and functional similarity. This resource will be of interest to chemical biologists for discovering new small molecule probes with specific modes-of-action as well as computational biologists interested in analysing CG interaction networks. Availability and implementation: MOSAIC is available at http://mosaic.cs.umn.edu. Contact: [email protected], [email protected], [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Chemical-genetic interactions-observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes-contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. In a recent publication, we applied CG-TARGET to a screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. We present here a formal description and rigorous benchmarking of the CG-TARGET method, showing that, compared to alternative enrichment-based approaches, it achieves similar or better accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. Additional investigation of the compatibility of chemical-genetic and genetic interaction profiles revealed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We also present experimental validations of CG-TARGET-predicted tubulin polymerization and cell cycle progression inhibitors. Our approach successfully demonstrates the use of genetic interaction networks in the high-throughput functional annotation of compounds to biological processes.

Instruction set simulators (ISSs) are indispensable tools for developing new architectures and embedded software. Due to the increasing variety of architectures and time-to-market pressure, it is important to efficiently develop fast ISSs based on dynamic binary translation. However, the implementation of such ISSs needs more effort than interpretive ISSs. In this paper, we propose a novel framework that generates ISSs based on dynamic binary translation from descriptions of interpretive ISSs. Our results on SH, MIPS64, and ARM show that the generated ISSs are 1.4 to 13.4 times faster than their original interpretive ISSs.

Recently, a portable digital device equipped with voice guidance has been widely used with increasing the demand for the usability-conscious dialogue system. One of the problems with the existing dialogue system is its immature application to non-task dialogue. Non-task-oriented dialogue requires some schemes that enable smooth and flexible conversations with a user. For example, it would be possible to go beyond the closed relationship between the system and the user by considering the user's relationship with others in real life. In this paper, we focused on the dialogue made by the two characters in a drama scenario, and tried to express their relationship with a scale of “intimacy degree.” There will be such various elements related to the intimacy degree as the frequency of response to the utterance and the attitude of a speaker during the dialogue. We focused on the emotional state of the speaker during the utterance and tried to realize intimacy estimation with higher accuracy. As the evaluation result, we achieved higher accuracy in intimacy estimation than the existing method based on speech role.

Researches on emotion estimation from text mostly use machine learning method. Because machine learning requires a large amount of example corpora, how to acquire high quality training data has been discussed as one of its major problems. The existing language resources include emotion corpora; however, they are not available if the language is different. Constructing bilingual corpus manually is also financially difficult. We propose a method to convert a training data into different language using an existing Japanese-English parallel emotion corpus. With a bilingual dictionary, the translation candidates are extracted against every word of each sentence included in the corpus. Then the extracted translation candidates are narrowed down into a set of words that highly contribute to emotion estimation and we used the set of words as training data. As the result of the evaluation experiment using the training data created by our proposed method, the accuracy of emotion estimation increased up to 66.7% in Naive Bayes. 1 INTRODUCTION Recently, there have been many researches on emotion estimation from text in the field of sentiment analysis or opinion mining (Ren, 2009), (Ren and Quan, 2015), (Ren and Wu, 2013), (Quan and Ren, 2010), (Quan and Ren, 2014), (Ren and Matsumoto, 2015) and many of them adopted machine learning methods that used words as a feature. When the type of the target sentence for emotion estimation and the type of the sentence prepared as training data are different, as in the case of terminology in the problem of domain adaptation for document classification, the appearance tendency of the emotion words differs. This causes a problem in fluctuation of accuracy. On the other hand, when a word is used as a feature for emotion estimation, the sentence structure does not have to be considered. As a result, it is easy to apply the method to other languages. Only if we prepare a large number of corpora with annotation of emotion tags on each sentence, emotion would be easily estimated by using the machine learning method. In the machine learning method, because manual definition of a rule is not necessary, we can reduce costs to apply the method to other languages. However, just like the problem in the domain, depending on the

Young people commonly use slang in the texts for weblogs or Social Networking Sites. How to treat such slang words properly is one of the problems in the field of text mining. In this paper, we examined several methods to extract Japanese slang called “Wakamono Kotoba,” which is particularly used by young people, by focusing on its script type and stroke count. In the evaluation experiment, a high precision was obtained when we adopted script type for extraction.

In recent decades, the devastating earthquakes, which can damage a lot of houses and buildings, have frequently happened. To slow down or prevent the disastrous damages of the earthquake, the various technologies have been required. Of these technologies, the ground motion simulator of the earthquake can be used to alert people to the dangers of the earthquake. This paper introduces a potable earthquake simulator, called a Jishin-The-Vuton 3D, which realizes the up-and-down motion with the holonomic omni-directional motions in order to simulate three-dimensional ground motion of the earthquake. Focusing on the design concept, structure, and feature of this new simulator, the authors discuss its implementation and verify its feasibility with preliminary experimental results.

With conventional accelerometer examination, e.g. ISO standard, mainly adopts linear motion based on an assumption that each actual sensor direction corresponds to the package direction. However, actual sensor directions do not agree with the package directions in fact. In addition, conventional methods have major problems as follows. 1) About sensor output, it is almost impossible to separate the effects of sensor inclination and sensitivity. 2) Measuring actual sensor direction is very difficult because the offset voltage changes continuously. 3) With linear motion, the reference of the input acceleration includes large calculation error because 2nd order differential is required. 4) Even if allowing sensor inclination error, 3 times measurements are necessary to know all sensitivities of a 3-axis accelerometer. This study proposes a novel examination method with an exciter mechanism using parallel linkage. The exciter rotates a target accelerometer in a uniform circular motion keeping the accelerometer posture constant against the external coordinate system. With balanced structure, the exciter inputs stable sine wave acceleration for each sensor. The advantages of this method are as follows. 1) It is possible to separate the effects of sensor inclination and sensitivity from the phase and the amplitude of the sine wave output. 2) It is possible to measure the actual sensor direction because the offset change can be removed by referring stable cyclic input acceleration. 3) Calculated input acceleration is reliable because it requires only 1st order differential. 4) Only 2 times measurements are enough to examine 3 actual sensor directions and 3 sensitivities for 3-axis accelerometer. With commercially available MEMS 3-axis accelerometer, the sensor directions and sensitivities are measured individually by a prototype exciter. The results showed good agreement in two different experimental conditions. Between the two conditions, the maximum difference of the sensor directions and the sensitivities are less than 0.8% and 1.6% respectively.

In this paper, we report our system that disambiguates person names in Web search results. The system uses named entities, compound key words, and URLs as features for document similarity calculation, which typically show high precision but low recall clustering results. We propose to use a two-stage clustering algorithm by bootstrapping to improve the low recall values, in which clustering results of the first stage are used to extract features used in the second stage clustering. Experimental results revealed that our algorithm yields better score than the best systems at the latest WePS workshop.

BACKGROUND: Post-translational modifications (PTMs) have a key role in regulating cell functions. Consequently, identification of PTM sites has a significant impact on understanding protein function and revealing cellular signal transductions. Especially, phosphorylation is a ubiquitous process with a large portion of proteins undergoing this modification. Experimental methods to identify phosphorylation sites are labor-intensive and of high-cost. With the exponentially growing protein sequence data, development of computational approaches to predict phosphorylation sites is highly desirable. RESULTS: Here, we present a simple and effective method to recognize phosphorylation sites by combining sequence patterns and evolutionary information and by applying a novel noise-reducing algorithm. We suggested that considering long-range region surrounding a phosphorylation site is important for recognizing phosphorylation peptides. Also, from compared results to AutoMotif in 36 different kinase families, new method outperforms AutoMotif. The mean accuracy, precision, and recall of our method are 0.93, 0.67, and 0.40, respectively, whereas those of AutoMotif with a polynomial kernel are 0.91, 0.47, and 0.17, respectively. Also our method shows better or comparable performance in four main kinase groups, CDK, CK2, PKA, and PKC compared to six existing predictors. CONCLUSION: Our method is remarkable in that it is powerful and intuitive approach without need of a sophisticated training algorithm. Moreover, our method is generally applicable to other types of PTMs.

We developed a model based on nonparametric Bayesian modeling for automatic discovery of semantic relationships between words taken from a corpus. It is aimed at discovering semantic knowledge about words in particular domains, which has become increasingly important with the growing use of text mining, information retrieval, and speech recognition. The subject-predicate structure is taken as a syntactic structure with the noun as the subject and the verb as the predicate. This structure is regarded as a graph structure. The generation of this graph can be modeled using the hierarchical Dirichlet process and the Pitman-Yor process. The probabilistic generative model we developed for this graph structure consists of subject-predicate structures extracted from a corpus. Evaluation of this model by measuring the performance of graph clustering based on WordNet similarities demonstrated that it outperforms other baseline models.

We propose a novel approach for extracting semantic structures from Web documents. Our task is to extract trees that describe the hierarchical relations in documents. We developed an algorithm for this task by using the stochastic context free grammar (SCFG) framework. Experiments showed that our approach effectively worked showing performance improvement through the parameter estimation.

This paper presents the estimation of depth based on the cylindrical re-projections of images captured by a stereo setup consisting of two sets of an ODV (omnidirectional vision system). In general, images obtained by such a device have a relatively low spatial resolution and have some aberration in comparison to standard camera images and thus, considerable range estimation errors are caused. We propose a stereo setup of an ODV, which features two mirrors with a contrived curvature to minimize blurring influence. We also propose an edge-based intensity interpolation method, a bi-directional sub-pixel matching procedure, and a discontinuity compensation algorithm to reduce the disparity error.