Jong C. Park

Accomplishments and challenges in literature data mining for biology

We review recent results in literature data mining for biology and discuss the need and the steps for a challenge evaluation for this field. Literature data mining has progressed from simple recognition of terms to extraction of interaction relationships from complex sentences, and has broadened from recognition of protein interactions to a range of problems such as improving homology search, identifying cellular location, and so on. To encourage participation and accelerate progress in this expanding field, we propose creating challenge evaluations, and we describe two specific applications in this context.

E3Miner: a text mining tool for ubiquitin-protein ligases

Ubiquitination is a regulatory process critically involved in the degradation of >80% of cellular proteins, where such proteins are specifically recognized by a key enzyme, or a ubiquitin-protein ligase (E3). Because of this important role of E3s, a rapidly growing body of the published literature in biology and biomedical fields reports novel findings about various E3s and their molecular mechanisms. However, such findings are neither adequately retrieved by general text-mining tools nor systematically made available by such protein databases as UniProt alone. E3Miner is a web-based text mining tool that extracts and organizes comprehensive knowledge about E3s from the abstracts of journal articles and the relevant databases, supporting users to have a good grasp of E3s and their related information easily from the available text. The tool analyzes text sentences to identify protein names for E3s, to narrow down target substrates and other ubiquitin-transferring proteins in E3-specific ubiquitination pathways and to extract molecular features of E3s during ubiquitination. E3Miner also retrieves E3 data about protein functions, other E3-interacting partners and E3-related human diseases from the protein databases, in order to help facilitate further investigation. E3Miner is freely available through http://e3miner.biopathway.org.

Emotion Interaction System for a Service Robot

This paper introduces an emotion interaction system for a service robot. The purpose of emotion interaction systems in service robots is to make people feel that the robot is not a mere machine, but reliable living assistant in the home. The emotion interaction system is composed of the emotion recognition, generation, and expression systems. A users emotion is recognized by multi-modality, such as voice, dialogue, and touch. The robots emotion is generated according to a psychological theory about emotion: OCC (Ortony, Clore, and Collins) model, which focuses on the users emotional state and the information about environment and the robot itself. The generated emotion is expressed by facial expression, gesture, and the musical sound of the robot. Because the proposed system is composed of all the three components that are necessary for a full emotional interaction cycle, it can be implemented in the real robot system and be tested. Even though the multi- modality in emotion recognition and expression is still in its rudimentary stages, the proposed system is shown to be extremely useful in service robot applications. Furthermore, the proposed framework can be a cornerstone for the design of emotion interaction and generation systems for robots.

Monitoring the evolutionary aspect of the Gene Ontology to enhance predictability and usability

BackgroundMuch effort is currently made to develop the Gene Ontology (GO). Due to the dynamic nature of information it addresses, GO undergoes constant updates whose results are released at regular intervals as separate versions. Although there are a large number of computational tools to aid the development of GO, they are operating on a particular version of GO, making it difficult for GO curators to anticipate the full impact of particular changes along the time axis on a larger scale. We present a method for tapping into such an evolutionary aspect of GO, by making it possible to keep track of important temporal changes to any of the terms and relations of GO and by consequently making it possible to recognize associated trends.ResultsWe have developed visualization methods for viewing the changes between two different versions of GO by constructing a colour-coded layered graph. The graph shows both versions of GO with highlights to those GO terms that are added, removed and modified between the two versions. Focusing on a specific GO term or terms of interest over a period, we demonstrate the utility of our system that can be used to make useful hypotheses about the cause of the evolution and to provide new insights into more complex changes.ConclusionsGO undergoes fast evolutionary changes. A snapshot of GO, as presented by each version of GO alone, overlooks such evolutionary aspects, and consequently limits the utilities of GO. The method that highlights the differences of consecutive versions or two different versions of an evolving ontology with colour-coding enhances the utility of GO for users as well as for developers. To the best of our knowledge, this is the first proposal to visualize the evolutionary aspect of GO.

Automatic extension of Gene Ontology with flexible identification of candidate terms

MOTIVATION Gene Ontology (GO) has been manually developed to provide a controlled vocabulary for gene product attributes. It continues to evolve with new concepts that are compiled mostly from existing concepts in a compositional way. If we consider the relatively slow growth rate of GO in the face of the fast accumulation of the biological data, it is much desirable to provide an automatic means for predicting new concepts from the existing ones. RESULTS We present a novel method that predicts more detailed concepts by utilizing syntactic relations among the existing concepts. We propose a validation measure for the automatically predicted concepts by matching the concepts to biomedical articles. We also suggest how to find a suitable direction for the extension of a constantly growing ontology such as GO. AVAILABILITY http://autogo.biopathway.org SUPPLEMENTARY INFORMATION Supplementary materials are available at Bioinformatics online.

OncoSearch: cancer gene search engine with literature evidence

In order to identify genes that are involved in oncogenesis and to understand how such genes affect cancers, abnormal gene expressions in cancers are actively studied. For an efficient access to the results of such studies that are reported in biomedical literature, the relevant information is accumulated via text-mining tools and made available through the Web. However, current Web tools are not yet tailored enough to allow queries that specify how a cancer changes along with the change in gene expression level, which is an important piece of information to understand an involved genes role in cancer progression or regression. OncoSearch is a Web-based engine that searches Medline abstracts for sentences that mention gene expression changes in cancers, with queries that specify (i) whether a gene expression level is up-regulated or down-regulated, (ii) whether a certain type of cancer progresses or regresses along with such gene expression change and (iii) the expected role of the gene in the cancer. OncoSearch is available through http://oncosearch.biopathway.org.

CoMAGC: a corpus with multi-faceted annotations of gene-cancer relations

BackgroundIn order to access the large amount of information in biomedical literature about genes implicated in various cancers both efficiently and accurately, the aid of text mining (TM) systems is invaluable. Current TM systems do target either gene-cancer relations or biological processes involving genes and cancers, but the former type produces information not comprehensive enough to explain how a gene affects a cancer, and the latter does not provide a concise summary of gene-cancer relations.ResultsIn this paper, we present a corpus for the development of TM systems that are specifically targeting gene-cancer relations but are still able to capture complex information in biomedical sentences. We describe CoMAGC, a corpus with multi-faceted annotations of gene-cancer relations. In CoMAGC, a piece of annotation is composed of four semantically orthogonal concepts that together express 1) how a gene changes, 2) how a cancer changes and 3) the causality between the gene and the cancer. The multi-faceted annotations are shown to have high inter-annotator agreement. In addition, we show that the annotations in CoMAGC allow us to infer the prospective roles of genes in cancers and to classify the genes into three classes according to the inferred roles. We encode the mapping between multi-faceted annotations and gene classes into 10 inference rules. The inference rules produce results with high accuracy as measured against human annotations. CoMAGC consists of 821 sentences on prostate, breast and ovarian cancers. Currently, we deal with changes in gene expression levels among other types of gene changes. The corpus is available at http://biopathway.org/CoMAGCunder the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0).ConclusionsThe corpus will be an important resource for the development of advanced TM systems on gene-cancer relations.

Extracting contrastive information from negation patterns in biomedical literature

Expressions of negation in the biomedical literature often encode information of contrast as a means for explaining significant differences between the objects that are so contrasted. We show that such information gives additional insights into the nature of the structures and/or biological functions of these objects, leading to valuable knowledge for subcategorization of protein families by the properties that the involved proteins do not have in common. Based on the observation that the expressions of negation employ mostly predictable syntactic structures that can be characterized by subclausal coordination and by clause-level parallelism, we present a system that extracts such contrastive information by identifying those syntactic structures with natural language processing techniques and with additional linguistic resources for semantics. The implemented system shows the performance of 85.7% precision and 61.5% recall, including 7.7% partial recall, or an F score of 76.6. We apply the system to the biological interactions as extracted by our biomedical information-extraction system in order to enrich proteome databases with contrastive information.

Interpretation of Natural Language Queries for Relational Database Access with Combinatory Categorial Grammar

In this paper, we describe a proposal to derive formal language queries from natural language queries with a combinatory categorial grammar (CCG). CCGs are well known to provide a means of deriving all the levels of information for natural language, i.e., syntax, semantics and discourse, at the same time. In our proposal, we utilize an extra level of representation for formal language queries for the aforementioned derivation. The syntactic coverage is shown with various natural language queries, including compound nouns, modification markers, various types of ellipses, numerical expressions, and subordinate and coordinate constructions. The general purpose CCG lexicon is semi-automatically augmented with the database fields and entries. We also discuss the performance of an implemented natural language query processing system.

Toward finer-grained sentiment identification in product reviews through linguistic and ontological analyses

We propose categories of finer-grained polarity for a more effective aspect-based sentiment summary, and describe linguistic and ontological clues that may affect such fine-grained polarity. We argue that relevance for satisfaction, contrastive weight clues, and certain adver-bials work to affect the polarity, as evidenced by the statistical analysis.