Ontology-Based MEDLINE Document Classification

An increasing and overwhelming amount of biomedical information is available in the research literature mainly in the form of free-text. Biologists need tools that automate their information search and deal with the high volume and ambiguity of free-text. Ontologies can help automatic information processing by providing standard concepts and information about the relationships between concepts. The Medical Subject Headings (MeSH) ontology is already available and used by MEDLINE indexers to annotate the conceptual content of biomedical articles. This paper presents a domain-independent method that uses the MeSH ontology inter-concept relationships to extend the existing MeSH-based representation of MEDLINE documents. The extension method is evaluated within a document triage task organized by the Genomics track of the 2005 Text REtrieval Conference (TREC). Our method for extending the representation of documents leads to an improvement of 17% over a non-extended baseline in terms of normalized utility, the metric defined for the task. The SVMlight software is used to classify documents

Death, dying and informatics: misrepresenting religion on MedLine

BACKGROUND: The globalization of medical science carries for doctors worldwide a correlative duty to deepen their understanding of patients' cultural contexts and religious backgrounds, in order to satisfy each as a unique individual. To become better informed, practitioners may turn to MedLine, but it is unclear whether the information found there is an accurate representation of culture and religion. To test MedLine's representation of this field, we chose the topic of death and dying in the three major monotheistic religions. METHODS: We searched MedLine using PubMed in order to retrieve and thematically analyze full-length scholarly journal papers or case reports dealing with religious traditions and end-of-life care. Our search consisted of a string of words that included the most common denominations of the three religions, the standard heading terms used by the National Reference Center for Bioethics Literature (NRCBL), and the Medical Subject Headings (MeSH) used by the National Library of Medicine. Eligible articles were limited to English-language papers with an abstract. RESULTS: We found that while a bibliographic search in MedLine on this topic produced instant results and some valuable literature, the aggregate reflected a selection bias. American writers were over-represented given the global prevalence of these religious traditions. Denominationally affiliated authors predominated in representing the Christian traditions. The Islamic tradition was under-represented. CONCLUSION: MedLine's capability to identify the most current, reliable and accurate information about purely scientific topics should not be assumed to be the same case when considering the interface of religion, culture and end-of-life care

Automatically linking MEDLINE abstracts to the Gene Ontology

Much has been written recently about the need for effective tools and methods for mining the wealth of information present in biomedical literature (Mack and Hehenberger, 2002; Blagosklonny and Pardee, 2001; Rindflesch et al., 2002)—the activity of conceptual biology. Keyword search engines operating over large electronic document stores (such as PubMed and the PNAS) offer some help, but there are fundamental obstacles that limit their effectiveness. In the first instance, there is no general consensus among scientists about the vernacular to be used when describing research about genes, proteins, drugs, diseases, tissues and therapies, making it very difficult to formulate a search query that retrieves the right documents. Secondly, finding relevant articles is just one aspect of the investigative process. A more fundamental goal is to establish links and relationships between facts existing in published literature in order to “validate current hypotheses or to generate new ones” (Barnes and Robertson, 2002)—something keyword search engines do little to support

Pengaruh Pendidikan dan Pelatihan Terhadap Peningkatan Kompetensi Pegawai di Badan Kepegawaian dan Pengembangan Sumber Daya Manusia (BKPSDM) Kota Bekasi

The objective of this study is to find out the influence of education and training to increasing of employee competence at Manpower and Human Resource Development Agency (BKPSDM) of Bekasi City. This study used quantitative research method. The data was collected from questionnaire and literature. The population in this study is Government Employees at the Manpower and Human Resource Development Agency (BKPSDM) of Bekasi City. Sampling technique used in this study is total sampling with sample totalled 58 samples. Result of this study identified that education and training at the Manpower and Human Resource Development Agency (BKPSDM) of Bekasi City has significant influence to increasing employees competence

Colonic Protein Fermentation and Promotion of Colon Carcinogenesis by Thermolyzed Casein

Thermolyzed casein is known to promote the growth of aberrant crypt foci (ACF) and colon cancer when it is fed to rats that have been initiated with azoxymethane. We speculated that the promotion was a consequence of increased colonic protein fermentation (i.e., that the thermolysis of the casein decreases its digestibility, increases the amount of protein reaching the colon, and increases colonic protein fermentation and that the potentially toxic products of this fermentation promote colon carcinogenesis). We found that the thermolysis of casein reduces its digestibility and increases colonic protein fermentation, as assessed by fecal ammonium and urinary phenol, cresol, and indol-3-ol. Thermolysis of two other proteins, soy and egg white protein, also increases colonic protein fermentation with increased fecal ammonia and urinary phenols, and thermolysis of all three proteins increases the levels of ammonia and butyric, valeric, and i-valeric acids in the cecal contents. We found, however, that the increased protein fermentation observed with thermolysis is not associated with pro-motion of colon carcinogenesis. With casein, the kinetics of protein fermentation with increasing thermolysis time are clearly different from the kinetics of promotion of ACF growth. The formation of the fermentation products was highest when the protein was thermolyzed for one hour, whereas promotion was highest for protein that had been thermolyzed for two or more hours. With soy and egg white, thermolysis increased colonic protein fermentation but did not promote colon carcinogenesis. Thus, although thermolysis of dietary casein increases colonic protein fermentation, products of this fermentation do not appear to be responsible for the promotion of colon carcinogenesis. Indeed, the results suggest that protein fermentation products do not play an important role in colon cancer promotion

Chi-square-based scoring function for categorization of MEDLINE citations

Objectives: Text categorization has been used in biomedical informatics for identifying documents containing relevant topics of interest. We developed a simple method that uses a chi-square-based scoring function to determine the likelihood of MEDLINE citations containing genetic relevant topic. Methods: Our procedure requires construction of a genetic and a nongenetic domain document corpus. We used MeSH descriptors assigned to MEDLINE citations for this categorization task. We compared frequencies of MeSH descriptors between two corpora applying chi-square test. A MeSH descriptor was considered to be a positive indicator if its relative observed frequency in the genetic domain corpus was greater than its relative observed frequency in the nongenetic domain corpus. The output of the proposed method is a list of scores for all the citations, with the highest score given to those citations containing MeSH descriptors typical for the genetic domain. Results: Validation was done on a set of 734 manually annotated MEDLINE citations. It achieved predictive accuracy of 0.87 with 0.69 recall and 0.64 precision. We evaluated the method by comparing it to three machine learning algorithms (support vector machines, decision trees, na\"ive Bayes). Although the differences were not statistically significantly different, results showed that our chi-square scoring performs as good as compared machine learning algorithms. Conclusions: We suggest that the chi-square scoring is an effective solution to help categorize MEDLINE citations. The algorithm is implemented in the BITOLA literature-based discovery support system as a preprocessor for gene symbol disambiguation process.Comment: 34 pages, 2 figure

American Muslim Health Disparities: The State of the Medline Literature

Background: While religious beliefs and values influence health behaviors, conventional health disparities research rarely examines health outcomes by religious affiliation particularly within multi-ethnic minority communities. Methods: Using a systematic strategy we searched the Medline literature to identify empiric studies that report on health disparities between American Muslims and non-Muslim groups residing in America. In addition to use religious affiliation descriptors for Muslim groups we utilized geographic and ethnicity terms such as “South Asian” or “Pakistani” as proxy terms to help uncover studies of American Muslims. Results: 171 empirical studies were captured. South Asians and Arabs were the most commonly studied groups, and mental health was the most common studied health condition. The overwhelming majority of studies did not assess connections between the Islamic faith and health outcomes. Conclusion: Healthcare disparities among American Muslims remain under-investigated. The few empirical studies of American Muslim groups, or of ethnic groups with large numbers of Muslims, rarely examine relationships between Islam-related factors and health outcomes and thereby miss an opportunity to understand the relationships between religion and health disparities

Formulating MEDLINE queries for article retrieval based on PubMed exemplars

Bibliographic search engines allow endless possibilities for building queries based on specific words or phrases in article titles and abstracts, indexing terms, and other attributes. Unfortunately, deciding which attributes to use in a methodologically sound query is a non-trivial process. In this paper, we describe a system to help with this task, given an example set of PubMed articles to retrieve and a corresponding set of articles to exclude. The system provides the users with unigram and bigram features from the title, abstract, MeSH terms, and MeSH qualifier terms in decreasing order of precision, given a recall threshold. From this information and their knowledge of the domain, users can formulate a query and evaluate its performance. We apply the system to the task of distinguishing original research articles of functional magnetic resonance imaging (fMRI) of sensorimotor function from fMRI studies of higher cognitive functions