2010 Proceeding- General Chairs:
- Doheon Kim,
- Hagit Shatkay,
- Program Chairs:
- Min Song,
- Shamkant Navathe
It is our great pleasure to welcome you to the ACM Fourth International Workshop on Data and Text Mining in Biomedical Informatics (DTMBIO'10), in conjunction with the 19th ACM International Conference on Information and Knowledge Management (CIKM'10). The rapid development of bioinformatics techniques is tightly coupled with development within several fields in computer science, including data mining, information retrieval, and database management systems, among others. A fundamental topic of research within bioinformatics is how to make effective use of the tremendous amount of biological and biomedical data to improve the understanding of biological systems. Such data include, but are not limited to, gene and protein sequences, gene expression profiles from microarray experiments, protein structure predictions resulting from high-throughput computational methods, proteinprotein interactions from proteomic studies, single nucleotide polymorphisms profiles from SNP arrays, and much bibliographic information from electronic medical journals. The need to automatically and effectively extract, understand, integrate, and make use of information embedded in such heterogeneous unstructured data drives the current research in bioinformatics.
This year's workshop continues the tradition of bringing together researchers that work in the field of data mining, text mining, and computational biology and providing a forum to present and discuss current research topics at the interface of the related fields. The mission of DTMBIO is to promote a tighter connection between literature search and data analysis within biomedical informatics. In particular, this year we focus on the following two themes: (1) Data and text mining for biomedical applications, and the identification of relevant background knowledge in database annotations or in text documents, such as scientific publications.. And (2) Knowledge discovery through the integration of heterogeneous biomedical data collected from electronic bulletin boards, scientific publications, and any type of experiments. Furthermore, we put more focus this year on the integration of bioinformatics and medical informatics toward translational research.
The papers accepted for presentation and publication in this volume cover a variety of topics, including bio-text mining, translational bioinformatics, systems bioinformatics, bio-ontology management, sequence analysis for massively parallel sequencing, protein-protein interactions, biomedical data classification, and biomedical information retrieval. We hope that these proceedings will serve as a valuable and up-to-date reference about the application of data- and text-mining techniques within biomedical informatics.
Proceeding Downloads
Context-specific gene regulatory networks subdivide intrinsic subtypes of breast cancer
Breast cancer is a highly heterogeneous disease with respect to molecular alterations and cellular composition making therapeutic and clinical outcome unpredictable. This diversity creates a significant challenge in developing tumor classifications that ...
Automatic classification of sentences for evidence based medicine
AIM Given a set of pre-defined medical categories used in Evidence Based Medicine, we aim to automatically annotate sentences in medical abstracts with these labels. METHOD We construct a corpus of 1,000 medical abstracts annotated by hand with medical ...
Processing SPARQL queries with regular expressions in RDF databases
As the Resource Description Framework (RDF) data model is widely used for modeling and sharing a lot of online bioinformatics resources such as Uniprot (dev.isb-sib.ch/projects/uniprot-rdf) or Bio2RDF (bio2rdf.org), SPARQL -- a W3C recommendation query ...
Discovering biological processes and side effects relationship using the process-drug-side effect network
The side effect of drugs often results from a response to the unintended target of a drug. Recently there have been researches identifying targets of known drugs based on the side effect information. These researches, however, did not consider the ...
Word sense disambiguation for event trigger word detection
This paper describes a method for detecting event trigger words in biomedical text based on a word sense disambiguation (WSD) approach. We first investigate the applicability of existing WSD techniques to trigger word disambiguation in the BioNLP 2009 ...
Combining syntactic information and domain-specific lexical patterns to extract drug-drug interactions from biomedical texts
A drug-drug interaction (DDI) occurs when one drug influences the level or activity of another drug. The increasing volume of the scientific literature overwhelms health care professionals trying to be kept up-to-date with all published studies on DDI. ...
Deriving a test collection for clinical information retrieval from systematic reviews
In this paper, we describe the construction of a test collection for evaluating clinical information retrieval. The purpose of this test collection is to provide a basis for researchers to experiment with PECO-structured queries. Systematic reviews are ...
Unsupervised word sense disambiguation in biomedical texts with co-occurrence network and graph kernel
This paper proposes an unsupervised word sense disambiguation method for the biomedical domain. In this paper, a network representation of co-occurrence data is first defined to represent both word senses and word contexts. The representation expresses ...
Dynamic concept ontology construction for pubmed queries
Exploring PubMed to find relevant information is challenging and time-consuming, as PubMed typically returns a large list of articles as a result of query. Existing works in improving the search quality on PubMed have focused on helping PubMed query ...
Effect of classifiers in consensus feature ranking for biomedical datasets
Many informative aspects of medical datasets may be extracted from comparative study of features discriminative power. Recently, consensus feature rankings have been proposed to achieve robust, unbiased and reliable rankings of attributes. We have ...
Recent research for MEDLINE/PubMed: short review
MEDLINE is the largest biomedical bibliographic database in the world. In this paper we discuss recent information retrieval research for MEDLINE and its information retrieval system PubMed by reviewing eight recent papers from different areas: ...
Index Terms
- Proceedings of the ACM fourth international workshop on Data and text mining in biomedical informatics
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Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| DTMBIO '14 | 211 | 22 | 10% |
| DTMBIO '13 | 18 | 11 | 61% |
| DTMBIO '09 | 18 | 8 | 44% |
| Overall | 247 | 41 | 17% |