2012 Proceeding- General Chairs:
- Doheon Kim,
- Sophia Ananiadou,
- Program Chairs:
- Min Song,
- Hua Xu
It is our great pleasure to welcome you to the ACM Sixth International Workshop on Data and Text Mining in Biomedical Informatics (DTMBIO'12), in conjunction with the 21st ACM International Conference on Information and Knowledge Management (CIKM'12).
Biomedical researchers face the current challenge of making effective use of the enormous amount of electronic biomedical data in order to better understand and explain complex biological systems. The biomedical data repositories include data in a wide variety of forms, including genomic sequences, gene expression profiles, proteomics, metabolomics, epigenomics, microbiomics, electronics medical records, literature information, and so on. The ability to automatically and effectively extract, integrate, understand and make use of information embedded in such heterogeneous - structured and unstructured - data remains a challenging task. The aim of the 2012 workshop has been to bring together researchers in the areas of data and text mining and computational biology, who are interested in integrating and analyzing heterogeneous, structured and unstructured data.
The papers accepted for presentation and publication in this volume cover a variety of topics, including biomedical text mining, data-driven hypothesis generation, clinical genomics, systems biology, proteomics, and clinical test design. We hope that these proceedings will serve as a valuable and up-to-date reference concerning the application of data- and textmining techniques within biomedical informatics.
Proceeding Downloads
Detecting type 2 diabetes causal single nucleotide polymorphism combinations from a genome-wide association study dataset with optimal filtration
The identification of causal single nucleotide polymorphisms (SNPs) for complex diseases like type 2 diabetes (T2D) is a challenge because of the low statistical power of individual markers from a genome-wide association study (GWAS). SNP combinations ...
Lexicon-free and context-free drug names identification methods using hidden markov models and pointwise mutual information
The paper concerns the issue of extraction of medicine names from free text documents written in Polish. Using lexicon-based approaches, it is impossible to identify unknown or misspelled medicine names. In this paper, we present the results of ...
Clinical entity recognition using structural support vector machines with rich features
Named entity recognition (NER) is an important task for natural language processing (NLP) of clinical text. Conditional Random Fields (CRFs), a sequential labeling algorithm, and Support Vector Machines (SVMs), which is based on large margin theory, are ...
Inferring appropriate eligibility criteria in clinical trial protocols without labeled data
We consider the user task of designing clinical trial protocols and propose a method that outputs the most appropriate eligibility criteria from a potentially huge set of candidates. Each document d in our collection D is a clinical trial protocol which ...
Predicting baby feeding method from unstructured electronic health record data
Obesity is one of the most important health concerns in United States and is playing an important role in rising rates of chronic health conditions and health care costs. The percentage of the US population affected with childhood obesity and adult ...
Extracting structured information from free-text medication prescriptions using dependencies
We explore an information extraction task where the goal is to determine the correct values for fields which are relevant to prescription drug administration such as dosage amount, frequency and route. The data set is a collection of prescriptions from ...
Indexing methods for efficient protein 3D surface search
This paper exploits efficient indexing techniques for protein structure search where protein structures are represented as vectors by 3D-Zernike Descriptor (3DZD). 3DZD compactly represents a surface shape of protein tertiary structure as a vector, and ...
Protein complex prediction via bottleneck-based graph partitioning
Detecting protein complexes is one of essential and fundamental tasks in understanding various biological functions or processes. Therefore, precise identification of protein complexes is indispensible. For more precise detection of protein complexes, ...
Finding associations among SNPS for prostate cancer using collaborative filtering
- Rohit Kugaonkar,
- Aryya Gangopadhyay,
- Yelena Yesha,
- Anupam Joshi,
- Yaacov Yesha,
- Michael Grasso,
- Mary Brady,
- Napthali Rishe
Prostate cancer is the second leading cause of cancer related deaths among men. Because of the slow growing nature of prostate cancer, sometimes surgical treatment is not required for less aggressive cancers. Recent debates over prostate-specific ...
TNMCA: generation and application of network motif based inference models for drug repositioning
Since the increase of the public biomedical data, Undiscovered Public Knowledge (UPK, proposed by Swanson) became an important research topic in the biological field. Drug repositioning is one of famous UPK tasks which infer alternative indications for ...
High precision rule based PPI extraction and per-pair basis performance evaluation
Virtually all current PPI extraction studies focus on improving F-score, aiming to balance the performance on both precision and recall. However, in many realistic scenarios involving large corpora, one can benefit more from an extremely high precision ...
Rule-based whole body modeling for analyzing multi-compound effects
Essential reasons including robustness, redundancy, and crosstalk of biological systems, have been reported to explain the limited efficacy and unexpected side-effects of drugs. Many pharmaceutical laboratories have begun to develop multi-compound drugs ...
Index Terms
- Proceedings of the ACM sixth international workshop on Data and text mining in biomedical informatics
Recommendations
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% |