Hisham Al-Mubaid

Publications (5)0 Total impact

• Article: A text-mining technique for extracting gene-disease associations from the biomedical literature.

  Hisham Al-Mubaid, Rajit K Singh
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  ABSTRACT: We propose a new text mining technique to identify associations between biological entities, specifically genes-diseases associations, from the biomedical literature. The proposed method is very simple and straightforward; it uses two sets (a positive set and a negative set) of documents and utilises the concepts of expectation (ex), evidence (ev), and Z-scores in combining positive and negative evidences in determining the significant gene-disease associations from Medline documents. Moreover, the method offers an efficient way to handle gene names, aliases, symbols, and abbreviations. We evaluated the method in discovering gene-to-disease associations from literature and the experimental results are impressive. We verified our results and confirmed the effectiveness of the proposed technique by various ways. For example, we ran the technique on some discovered and known genes-diseases relationships. Our method was able to discover associations between genes and various diseases like Amyotrophic lateral sclerosis, Tuberous Sclerosis, Autism, Homocystinuria, Bipolar Disorder, Atherosclerosis and more.
  International Journal of Bioinformatics Research and Applications 01/2010; 6(3):270-86.
• Article: Measuring Semantic Similarity Between Biomedical Concepts Within Multiple Ontologies.

  Hisham Al-Mubaid, Hoa A. Nguyen
  IEEE Transactions on Systems, Man, and Cybernetics, Part C. 01/2009; 39:389-398.
• Source

  Available from: psu.edu

  Conference Proceeding: Semantic distance of concepts within a unified framework in the biomedical domain.

  Hisham Al-Mubaid, Hoa A. Nguyen
  Proceedings of the 2007 ACM Symposium on Applied Computing (SAC), Seoul, Korea, March 11-15, 2007; 01/2007
• Source

  Available from: psu.edu

  Article: A cluster-based approach for semantic similarity in the biomedical domain.

  Hisham Al-Mubaid, Hoa A Nguyen
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  ABSTRACT: We propose a new cluster-based semantic similarity/distance measure for the biomedical domain within the framework of UMLS. The proposed measure is based mainly on the cross-modified path length feature between the concept nodes, and two new features: (1) the common specificity of two concept nodes, and (2) the local granularity of the clusters. We also applied, for comparison purpose, five existing general English ontology-based similarity measures into the biomedical domain within UMLS. The proposed measure was evaluated relative to human experts' ratings, and compared with the existing techniques using two ontologies (MeSH and SNOMED-CT) in UMLS. The experimental results confirmed the efficiency of the proposed method, and showed that our similarity measure gives the best overall results of correlation with human ratings. We show, further, that using MeSH ontology produces better semantic correlations with human experts' scores than SNOMED-CT in all of the tested measures.
  Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2006; 1:2713-7.
• Conference Proceeding: Using MEDLINE as Standard Corpus for Measuring Semantic Similarity in the Biomedical Domain.

  Hisham Al-Mubaid, Hoa A. Nguyen
  Sixth IEEE International Symposium on BioInformatics and BioEngineering (BIBE 2006), 16-18 October 2006, Arlington, Virginia, USA; 01/2006