CIKM WORKSHOP REPORT

Report on ACM Text Mining in Bioinformatics (TMBIO 006)
Min Song
New Jersey Institute of Technology
min.song@njit.edu
Zoran ObradovicTemple University
zoran@core.ist.temple.edu
Abstract

Effective use of knowledge gained by mining such heterogeneous andunstructured data plays a crucial role in all stages of integrative biologicalresearch studies. Text Mining in Bioinformatics has drawn attention from bothbiomedical researchers and computational biologists. In this article we reporton a workshop conducted in conjunction with the ACM CIKM Conference inWashington, D.C. in November 2006. Participants from both academic andindustry in Biomedical Text Mining joined the workshop to discuss the issues
and trends in the field.

1Introduction

Biological researchers increasingly rely on a very large amount of biomedical electronic data tocorrectly interpret and understand their complex biological systems. The biomedical data repositories
deliver different types of data: gene expression data from Microarray experiments, protein
identification and quantification data from proteomics experiments, genomic sequence data gatheredby the Human Genome Project, SNP data from high-throughput SNP arrays, and facts extracted frombiomedical scientific publications. Increasingly such facts from the scientific literature areautomatically extracted and integrated into complex analytical tools that bring together different typesof information, e.g. data from Microarray experiments and sequence annotations.
The focus of this workshop is to bring together researchers that work in the field of text mining andcomputational biology and that want to integrate such heterogeneous unstructured data, which is achallenging task, and want to better embed literature information into bioinformatics solutions. Theworkshop seeks in particular:••

Bioinformatics text mining solutions that identify relevant background knowledge in textual
documents, such as scientific publications, or in database annotations. Such approaches
currently being studied range from term recognition to extraction of complex relationships ofinteraction between proteins.
Ambitious knowledge discovery solutions that process heterogeneous biomedical datacollected from electronic bulletin boards, scientific publications, and any type of experiments.
It is important to identify and solve issues as to how to consolidate information extracted fromtextual documents into other types of data in structured form.

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It is difficult and requires profound understanding of both text mining and computational biology toidentify problems and optimization criteria which, when maximized by text mining algorithms,
actually contribute to a better understanding of biological systems. Identification of appropriate text
mining problems in Bioinformatics and development of evaluation methods for text mining results
are ongoing efforts.

2Workshop

The first international workshop on Text Mining in Bioinformatics (TMBIO) was successful. The
number of papers received was 21 and we accepted 8 papers. The acceptance rate was 38%. We
invited one keynote speaker from South Korea and also had one invited talk. The keynote speaker isDr. Doheon Lee, associate professor of Department of BioSystems at KAIST in South Korea. He is
the head of Bio-Information System National Research Lab at KAIST and is also the director ofGeneral Affairs, Korean Society for Bioinformatics. The invited talk was given by professor
Slobodan Vucetic from Temple University. As the first workshop, we are proud that TMBIO 2006
was successful in many ways. It drew attention from researchers in both Text Mining andBioinformatics research area. The selected papers were invited to two prestigious journals: BMC
Bioinformatics and International Journal of Data Mining and Bioinformatics.
TMBIO 2006 was held in a day. The day was divided into two technical paper sessions, a keynotespeech, and an invited talk. We summarize each below
2.1.1 Keynote Speech
Dr. Doheon Lee, the director of Korean Society for Bioinformatics, delivered his keynote speech on“BioCAD: An Information Fusion Platform for Bio-Network Inference and Analysis.” After giving
us a brief summary of the field of Text Mining, he described his current research on bio-network
inference and analysis. He proposed the BioCAD system, the information fusion software, to provide
a framework for optimization for bio-network inference. He concluded his presentation with issues
and future works in bio-network inference and how Text Mining can play a pivotal role in bionetwork inference.
2.1.2 Morning Session
The morning session was divided into two themes: 1) Biomedical Knowledge Acquisition and 2)
Applications to Biomedical Data. In the Biomedical Knowledge Acquisition session, Tasha Inniss led
the talk with her presentation on “Towards Applying Text Mining and Natural Language Procession
for Biomedical Ontology Acquisition.” She described the research effort of her research group on
building automatic ontology in Age-Related Macular Degeneration. She reported the experimental
results of comparing three different ontology construction methods: 1) Expert-Retinal Specialist, 2)
NLP-NSP, and 3) SAS-Text Miner.
In the Applications to Biomedical Data session, Melissa Rogers presented her research on “EmergingGenome Data to Identify Conserved Bone Morphogenetic Protein(Bmp)2 Gene Expression
Mechanisms”. As a bio-chemist, she emphasized that understanding the evolution o the complex
transcriptional and post-transcriptional mechanisms required novel and interdisciplinary approaches.
As an effort in this direction, she described her effort towards defining Bmp2 gene expression
determinants by applying text mining techniques for computational analyses of emerging genome
data. Another presenter, Brian Westwood, talked about his research on “Application of Correlate
Summation to Data Clustering in the Estrogen-and Salt-Sensitive Female mRen2.Lewis Rat.” Heattempted to do so by using a new congenic rodent model of hypertension. Specifically he compared
two statistical methods designed to identify changes in experimental parameters that are significantly
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linked and reported the positive impact of clustering on the Estrogen-and Salt-Sensitive FemalemRen2.Lewis Rat.
2.1.3 Afternoon Session
The afternoon session was broken into two sub-sessions: 1) Ontology for Text Mining andInformation Extraction. In the Ontology for Text Mining session, Darren Natale gave a presentationon “Conceptual Framework for a Protein Ontology”. He described a framework for the protein
ontology (PRO) and illustrated its use on human proteins from the TGF-beta signaling pathway withthe goal to facilitate use of computer-based inference about proteins. Illhoi Yoo presented his workon “Integrating Biomedical Literature Clustering and Summarization Approaches using BiomedicalOntology”. He introduced a technique for combined document clustering and text summarization.
Based on his previous work on ontology-enriched graphical representation for documents fordocument clustering, he used the graphical model for document clustering and text summarization.
As a last presenter of the session, Padmini Srinivasan gave a talk on her research “GO for GeneDocuments”. Finding information in huge and growing biological datasets is an enormous challenge.
She stressed that Gene Ontology organizes experimental findings in a classification scheme that isallows automated data mining. To build an automatic Gene Ontology, she adopted support vector
machine classifiers to test various forms of data analyses to define criteria for accurate classificationof terms.
In the Information Extraction session, Manabu Torii presented his and his colleagues work on “aComparison Study of Biomedical Short Form Definition Detection Algorithms.” He described acomparative study of abbreviation extraction algorithms together with ways for improving thealgorithms. By providing a solid description of how various abbreviation extraction works, hesuggested the better way of extracting abbreviation. The last presenter, Hyunsoo Kim gave a talk on“Extracting indirect gene relationships from biomedical literatures via matrix factorizations.” He
explored utility of the singular value decomposition (SVD) and non-negative matrix factorization(NMF) in discovering gene relationships from MEDLINE citations. The paper attempted to validate
use of NMF as an alternative to SVD and showed that SVD and NMF are effective retrieval of related
genes from literature.

3Acknowledgement

We give our sincere thanks to SIGIR, to the authors and presenters, to the keynote speaker, and to themembers of the program committee for their contributions to organize an outstanding workshop.

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