Von-Wun Soo

A personalized restaurant recommender agent for mobile e-service

A recommender agent in mobile environments should be context-aware to assist users while the users are moving. Many different kinds of contexts can be used by a recommender agent, such as weather, route conditions, time and location, etc. We demonstrate a prototype design of a software agent that recommends travel-related information according to contexts of the user in mobile environment. The recommendation procedure includes the dialogue between the user and the agent for modifying constraints given to the agent. We illustrate with a scenario of recommending a restaurant to a tourist in Taipei city by interacting with a personalized agent in a mobile device.

A cooperative multi-agent platform for invention based on patent document analysis and ontology

Abstract We propose a cooperative multi-agent platform to support the invention process based on the patent document analysis. It helps industrial knowledge managers to retrieve and analyze existing patent documents and extract structure information from patents with the aid of ontology and natural language processing techniques. It allows the invention process to be carried out through the cooperation and coordination among software agents delegated by the various domain experts in the complex industrial R&D environment. Furthermore, it integrates the patent document analysis with the inventive problem solving method known as TRIZ method that can suggest invention directions based on the heuristics or principles to resolve the contradictions among design objectives and engineering parameters. We chose the patent invention for chemical mechanical polishing (CMP) as our case study. However, the platform and techniques could be extended to most cooperative invention domains.

Towards Text-based Emotion Detection A Survey and Possible Improvements

This paper presents an overview of the emerging field of emotion detection from text and describes the current generation of detection methods that are usually divided into the following three main categories: keyword-based, learning-based, and hybrid recommendation approaches. Limitations of current detection methods are examined, and possible solutions are suggested to improve emotion detection capabilities in practical systems, which emphasize on human-computer interactions. These solutions include extracting keywords with semantic analysis, and ontology design with emotion theory of appraisal. Furthermore, a case-based reasoning architecture is proposed to combine these solutions.

Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency

BackgroundProstate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear. We present a computational method for inferring genetic regulatory networks from micorarray data automatically with transcription factor analysis and conditional independence testing to explore the potential significant gene regulatory networks that are correlated with cancer, tumor grade and stage in the prostate cancer.ResultsTo deal with missing values in microarray data, we used a K-nearest-neighbors (KNN) algorithm to determine the precise expression values. We applied web services technology to wrap the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences and predicted the transcription factors that regulate the gene expressions. We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD) as a target dataset to evaluate our method. The predicted results showed that the possible biomarker genes related to cancer and denoted the androgen functions and processes may be in the development of the prostate cancer and promote the cell death in cell cycle. Our predicted results showed that sub-networks of genes SREBF1, STAT6 and PBX1 are strongly related to a high extent while ETS transcription factors ELK1, JUN and EGR2 are related to a low extent. Gene SLC22A3 may explain clinically the differentiation associated with the high grade cancer compared with low grade cancer. Enhancer of Zeste Homolg 2 (EZH2) regulated by RUNX1 and STAT3 is correlated to the pathological stage.ConclusionsWe provide a computational framework to reconstruct the genetic regulatory network from the microarray data using biological knowledge and constraint-based inferences. Our method is helpful in verifying possible interaction relations in gene regulatory networks and filtering out incorrect relations inferred by imperfect methods. We predicted not only individual gene related to cancer but also discovered significant gene regulation networks. Our method is also validated in several enriched published papers and databases and the significant gene regulatory networks perform critical biological functions and processes including cell adhesion molecules, androgen and estrogen metabolism, smooth muscle contraction, and GO-annotated processes. Those significant gene regulations and the critical concept of tumor progression are useful to understand cancer biology and disease treatment.

From reality to mind: a cognitive middle layer of environment concepts for believable agents

The environment is an important but overlooked piece in the construction of multiagent-based scenarios. Richness, believability and variety of scenarios are inseparable from the environment because every action and interaction of agents is based around the environment they are situated in. The prerequisite, however, is that agents must be able to understand the environment and capture its dynamic nature. This paper proposes a cognitive middle layer between agent minds and the environment. Aspects of the reality are mapped to concepts in the middle layer, through which agents can feel and reason about the real environment. The middle layer is modelled with a structured specification based on Web Ontology Language (OWL) to be extensible and reusable. Environmental concepts are integrated into the goal processing of agents to trigger intentions. This paper also reports our initial investigation about the design of a simulation system for multiple environment-aware agents and multiple users.

A network flow approach to predict drug targets from microarray data, disease genes and interactome network - case study on prostate cancer.

BackgroundSystematic approach for drug discovery is an emerging discipline in systems biology research area. It aims at integrating interaction data and experimental data to elucidate diseases and also raises new issues in drug discovery for cancer treatment. However, drug target discovery is still at a trial-and-error experimental stage and it is a challenging task to develop a prediction model that can systematically detect possible drug targets to deal with complex diseases.MethodsWe integrate gene expression, disease genes and interaction networks to identify the effective drug targets which have a strong influence on disease genes using network flow approach. In the experiments, we adopt the microarray dataset containing 62 prostate cancer samples and 41 normal samples, 108 known prostate cancer genes and 322 approved drug targets treated in human extracted from DrugBank database to be candidate proteins as our test data. Using our method, we prioritize the candidate proteins and validate them to the known prostate cancer drug targets.ResultsWe successfully identify potential drug targets which are strongly related to the well known drugs for prostate cancer treatment and also discover more potential drug targets which raise the attention to biologists at present. We denote that it is hard to discover drug targets based only on differential expression changes due to the fact that those genes used to be drug targets may not always have significant expression changes. Comparing to previous methods that depend on the network topology attributes, they turn out that the genes having potential as drug targets are weakly correlated to critical points in a network. In comparison with previous methods, our results have highest mean average precision and also rank the position of the truly drug targets higher. It thereby verifies the effectiveness of our method.ConclusionsOur method does not know the real ideal routes in the disease network but it tries to find the feasible flow to give a strong influence to the disease genes through possible paths. We successfully formulate the identification of drug target prediction as a maximum flow problem on biological networks and discover potential drug targets in an accurate manner.

Market-oriented multiple resource scheduling in grid computing environments

In a grid computing environment, each client has its own job represented as a workflow composed of tasks that require multiple types of computational resources to complete. Developing a mechanism that schedules these workflows to efficiently utilize limited amounts of resources in the grid is a challenging problem. This paper takes a market-oriented approach allowing the job scheduling task to be distributed among clients. In this approach, several workflow agents plan a feasible schedule for their jobs and compete in the resource market. A market broker agent is implemented to coordinate the conflicts in simultaneous access of the same resource. Experiment results show that the performance of the proposed approach surpasses those of first-come-first-serve and a variant of shortest-job-first method in terms of job completion ratio before deadline.

On-line incremental learning in bilateral multi-issue negotiation

In this paper, we assume agents are cooperative negotiators under bounded number of negotiation messages. We implement agents who could incrementally learn from other agents proposal during negotiation in order to speed up the negotiation process. We evaluate their performance in terms of Pareto efficiency, total utility payoffs, and number of negotiating messages. The experiments showed that negotiation learning agents could reach closer to the Pareto efficiency agreement in a much faster speed than such non-learning negotiating agents as simple random agents, rational agents, and cooperative agents.

Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation

BackgroundDuring the last few years, the knowledge of drug, disease phenotype and protein has been rapidly accumulated and more and more scientists have been drawn the attention to inferring drug-disease associations by computational method. Development of an integrated approach for systematic discovering drug-disease associations by those informational data is an important issue.MethodsWe combine three different networks of drug, genomic and disease phenotype and assign the weights to the edges from available experimental data and knowledge. Given a specific disease, we use our network propagation approach to infer the drug-disease associations.ResultsWe apply prostate cancer and colorectal cancer as our test data. We use the manually curated drug-disease associations from comparative toxicogenomics database to be our benchmark. The ranked results show that our proposed method obtains higher specificity and sensitivity and clearly outperforms previous methods. Our result also show that our method with off-targets information gets higher performance than that with only primary drug targets in both test data.ConclusionsWe clearly demonstrate the feasibility and benefits of using network-based analyses of chemical, genomic and phenotype data to reveal drug-disease associations. The potential associations inferred by our method provide new perspectives for toxicogenomics and drug reposition evaluation.

Using sharable ontology to retrieve historical images

We present a framework of utilizing sharable domain ontology and thesaurus to help the retrieval of historical images of the First Emperor of Chinas terracotta warriors and horses. Incorporating the sharable domain ontology in RDF and RDF schemas of semantic web and a thesaurus, we implement methods to allow easily annotating images into RDF instances and parsing natural language like queries into the query schema in XML format. We also implement a partial structural matching algorithm to match the query schema with images at the level of semantic schemas. Therefore the historical images can be retrieved by naïve users of domain specific history in terms of natural language like queries.