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To integrate cancer chemoprevention information on a genomics basis, we have built a Web site named GenoCache (Genomics and Cancer Chemoprevention) that would provide users with a molecular biological view of cancer chemoprevention. The site is organized in four viewpoints: substances, genes or gene products with their genetic variations, cells or tissues with various cancer development states, and "pathways" or sequential interactions between these objects. We linked the relevant genes to dbSNP.pmid:16779267 pmcid:PMC1560473 fatcat:okoezhrkwrgqpdjsiil6iwzca4
We developed a platform that visualizes all the dimensions of so-called 'OMIC' data from genomic, transcriptomic, and proteomic domains, and helps users identify interesting data dimensions that might be associated with a set of clinical features of diseases. For this, we organized the textual descriptions of Clinical Synopsis fields in OMIM (Online Mendelian Inheritance in Man) into a quantitative format, and developed a Web-based interactive and graphical search system.pmid:17238569 pmcid:PMC1839352 fatcat:l5tm2lnv5beapalwsppsj2snlq
We describe a system that extracts disease-gene relations from M edLine. We constructed a dictionary for disease and gene names from six public databases and extracted relation candidates by dictionary matching. Since dictionary matching produces a large number of false positives, we developed a method of machine learning-based named entity recognition (NER) to filter out false recognitions of disease/gene names. We found that the performance of relation extraction is heavily dependent upon thedoi:10.1142/9789812701626_0002 fatcat:w5kyfvessndrzautvqekzkjnfu
more »... performance of NER filtering and that the filtering improves the precision of relation extraction by 26.7% at the cost of a small reduction in recall.
The increasing volume and diversity of transcriptome data in the public domain offer an opportunity to advance new questions and hypotheses. We anticipate that tools that can visualize the gap in the distribution of information between the scientific literature and actual data would prompt such questions. We focused on the roles played by various genes in tissues, and have developed a database that contrasts information on gene expression in tissues with PubMed text and transcriptome data. Datapmid:18999036 pmcid:PMC2656066 fatcat:ecwvwgj4wreehcqoyi5jpqbjae
more »... pairs of tissues and the genes that might be expressed there were automatically extracted from text with vocabularies for the genes and tissues. The anatomical categories of various expressed sequence tag (EST) libraries were also automatically determined. These types of information were linked using the hierarchical structure of the Metathesaurus in UMLS.
Automatic recognition of relations between a specific disease term and its relevant genes or protein terms is an important practice of bioinformatics. Considering the utility of the results of this approach, we identified prostate cancer and gene terms with the ID tags of public biomedical databases. Moreover, considering that genetics experts will use our results, we classified them based on six topics that can be used to analyze the type of prostate cancers, genes, and their relations.doi:10.1186/1471-2105-7-s3-s4 pmid:17134477 pmcid:PMC1764448 fatcat:jg4tzqgmvrht3d7kbtgtspjcge
more »... : We developed a maximum entropy-based named entity recognizer and a relation recognizer and applied them to a corpus-based approach. We collected prostate cancer-related abstracts from MEDLINE, and constructed an annotated corpus of gene and prostate cancer relations based on six topics by biologists. We used it to train the maximum entropy-based named entity recognizer and relation recognizer. Results: Topic-classified relation recognition achieved 92.1% precision for the relation (an increase of 11.0% from that obtained in a baseline experiment). For all topics, the precision was between 67.6 and 88.1%. Conclusion: A series of experimental results revealed two important findings: a carefully designed relation recognition system using named entity recognition can improve the performance of relation recognition, and topic-classified relation recognition can be effectively addressed through a corpus-based approach using manual annotation and machine learning techniques.
The complexity of decision-making and diversity of tasks in pediatric practice can lead to medical errors. To confirm the hypothesis that physicians' emotions influence the occurrence of medical errors, we analyzed medical adverse event reports to assess the effect of emotional factors on pediatrician decision-making and medical errors. Methods: This study involved case analyses of reports of pediatrician-related medical adverse events drawn from a Japanese national medical database. Wedoi:10.4264/numa.78.3_135 fatcat:bppj47v4s5caxlo65vmd7pblna
more »... 310 adverse medical event reports involving pediatrician errors recorded over a 6-year period. Reports involving decision-making errors were extracted and analyzed by the patient's age, doctor's experience, severity of the adverse event, event circumstances, timing of errors by decision-making stage, and the presence of emotional factors. Results: We found decision-making errors in 58.6% of the examined medical adverse events reports. Most errors occurred in the situation awareness and decision stages. Overall, 53.2% of cases involving decision-making errors showed emotional involvement in the adverse event occurrence. The three emotional factors that most affected errors were trust, optimism, and distraction. Conclusions: Over half of the cases of errors in the decision-making process had an emotional component. The finding that trust influenced medical errors suggests that even positive emotions may affect errors. More awareness of the emotional aspects of clinical decision-making and research approaches that address emotion will help to reduce medical errors and improve patient safety.
In Japan, a range of patients with traumatic brain injury (TBI) has been recorded in a nationwide database (Japan Neurotrauma Data Bank; JNTDB). This study aimed to externally validate three international prediction models using JNTDB data: Trauma and Injury Severity Score (TRISS), Corticosteroid Randomization After Significant Head Injury (CRASH), and International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT). We also aimed to validate the applicability of these modelsdoi:10.1371/journal.pone.0221791 pmid:31449548 pmcid:PMC6709937 fatcat:pwfaqubmvfbz5gqnbooxggw5zi
more »... in the Japanese population. Of 1,091 patients registered in the JNTDB from July 2009 to June 2011, we analyzed data for 635 patients. We examined factors associated with mortality in-hospital and unfavorable outcomes 6 months after TBI by applying the TRISS, CRASH, and IMPACT models. We also conducted an external validation of these models based on these data. The patients' mean age was 60.1 ±21.1 years, and 342 were alive at the time of discharge (53.9%). Univariate analysis revealed eight major risk factors for mortality in-hospital: age, Glasgow Coma Scale (GCS), Injury Severity Score (ISS), systolic blood pressure, heart rate, mydriasis, acute epidural hematoma (AEDH), and traumatic subarachnoid hemorrhage. A similar analysis identified five risk factors for unfavorable outcomes at 6 months: age, GCS, ISS, mydriasis, and AEDH. For mortality in-hospital, the TRISS had a satisfactory area under the curve value (0.75). For unfavorable outcomes at 6 months, the CRASH (basic and computed tomography) and IMPACT (core and core extended) models had satisfactory area under the curve values (0.86, 0.86, 0.81, and 0.85, respectively). The TRISS, CRASH, and IMPACT models were suitable for application to the JNTDB population, indicating these models had high value in Japanese patients with neurotrauma.
Writing -review & editing: Jimpei Misawa, Rie Ichikawa, Akiko Shibuya, Yukihiro Maeda, Teruyoshi Hishiki, Yoshiaki Kondo. ...doi:10.1371/journal.pone.0203985 fatcat:5ceutbvdvvct5n52ggifiqpd2a
This study aims to use the conceptual framework of social determinants of health (SDH) to elucidate the social determinants that affect the use of complementary and alternative medicine (CAM) from the perspectives of both intermediary and structural determinants. Data were derived from a survey mailed to 1,500 randomly selected residents (20-69 years old; May-July 2009) of Sendai city in Japan. A generalized linear model was used in the analysis, with CAM use over the past one month as thedoi:10.1371/journal.pone.0200578 pmid:30011303 pmcid:PMC6047791 fatcat:2dkupc7k45bozfuthz5mdzxixa
more »... dent variable, SDH structural and intermediary determinants as independent variables, and demographic characteristics, indicators of health status, and the evaluation of health or healthcare systems as control variables. The prevalence of CAM usage was 62.1%. The generalized linear model showed that middle subjective social status (OR = 1.47; 95% CI: 1.04-2.07) as structural determinants was significantly associated with CAM usage. Adding the intermediary determinants, the same effect was observed. When demographic characteristics, indicators of health status, and the evaluation of health or healthcare systems were introduced as control variables, the associations of the structural determinants disappeared, revealing that hope (OR = 1.25; 95%CI: 1.04-1.50) as intermediary determinants was associated with the use of CAM. Female sex (OR = 1.47; 95% CI: 1.02-2.12) and health anxiety (OR = 1.68; 95% CI: 1.20-2.34) were associated with CAM usage. We found that intermediary rather than structural determinants were associated with CAM usage. Hope as an intermediary determinant was particularly associated with CAM usage.
While traditional, complementary and alternative medicine (TCAM) is gaining increased interest worldwide, the structural factors associated with the usage of TCAM at the social level have not been sufficiently explored. We aim to understand the social structure of uncertainty in society that affects the TCAM usage for men and women. We studied 32 countries using data from the International Social Survey Programme and the World Bank. In this study, we defined TCAM usage as visits to andoi:10.1186/s12906-019-2662-x pmid:31500604 pmcid:PMC6734350 fatcat:qfaoljoknbaqlgrnj7iq3gb2wa
more »... e/traditional/folk health care practitioner during the past 12 months. We performed a correlation analysis and used a generalized linear model . The prevalence of TCAM usage in terms of visits to practitioners was 26.1% globally, while usage varied across the 32 countries. Generalized linear models showed that unemployment rate was associated with the prevalence of TCAM usage in terms of visits to practitioners. At the social-structural level TCAM usage involving visits to practitioners was related to job insecurity. Job insecurity led to a decrease in TCAM usage regarding visits to practitioners. These findings suggest that it is necessary to consider the social-structural factors of uncertainty in society when designing health policies related to TCAM.
Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing - BioNLP '08
This paper presents a novel prediction approach for protein sub-cellular localization. We have incorporated text and sequence-based approaches.doi:10.3115/1572306.1572324 fatcat:qqnwijntibckja6jb7eu4ifvwe
Hishiki and Jun'ichi Tsujii The authors recognised automatically relations between prostate cancer and gene terms with the ID tags of public biomedical databases. ... Recognizing mentions of fine-grained relations between prostate cancer and genes from Medline using machine learning techniques by Hong-Woo Chun Yoshimasa Tsuruoka, Jin-Dong Kim, Rie Shiba, Naoki Nagata, Teruyoshi ...doi:10.1186/1471-2105-7-s3-s1 fatcat:h26jwoukezbvtgt73qlrfhyapy
The human genome sequence defines our inherent biological potential; the realization of the biology encoded therein requires knowledge of the function of each gene. Currently, our knowledge in this area is still limited. Several lines of investigation have been used to elucidate the structure and function of the genes in the human genome. Even so, gene prediction remains a difficult task, as the varieties of transcripts of a gene may vary to a great extent. We thus performed an exhaustivedoi:10.1371/journal.pbio.0020162 pmid:15103394 pmcid:PMC393292 fatcat:qhv2pfjxhvhhtexz55lxyj7gne
more »... ative characterization of 41,118 full-length cDNAs that capture the gene transcripts as complete functional cassettes, providing an unequivocal report of structural and functional diversity at the gene level. Our international collaboration has validated 21,037 human gene candidates by analysis of high-quality full-length cDNA clones through curation using unified criteria. This led to the identification of 5,155 new gene candidates. It also manifested the most reliable way to control the quality of the cDNA clones. We have developed a human gene database, called the H-Invitational Database (H-InvDB; http://www.h-invitational.jp/). It provides the following: integrative annotation of human genes, description of gene structures, details of novel alternative splicing isoforms, non-protein-coding RNAs, functional domains, subcellular localizations, metabolic pathways, predictions of protein three-dimensional structure, mapping of known single nucleotide polymorphisms (SNPs), identification of polymorphic microsatellite repeats within human genes, and comparative results with mouse full-length cDNAs. The H-InvDB analysis has shown that up to 4% of the human genome sequence (National Center for Biotechnology Information build 34 assembly) may contain misassembled or missing regions. We found that 6.5% of the human gene candidates (1,377 loci) did not have a good protein-coding open reading frame, of which 296 loci are strong candidates for nonprotein-coding RNA genes. In addition, among 72,027 uniquely mapped SNPs and insertions/deletions localized within human genes, 13,215 nonsynonymous SNPs, 315 nonsense SNPs, and 452 indels occurred in coding regions. Together with 25 polymorphic microsatellite repeats present in coding regions, they may alter protein structure, causing phenotypic effects or resulting in disease. The H-InvDB platform represents a substantial contribution to resources needed for the exploration of human biology and pathology.
Sheffield, UK Corpus resources for development and evaluation of a biological text mining system Automatically linking MEDLINE abstracts to the Gene Ontology Yoshimasa Tsuruoka, Teruyoshi Hishiki ...doi:10.1002/cfg.338 pmid:18629019 pmcid:PMC2447301 fatcat:vmqqau3myrgsfdfhwotqljapje
Peter Tonellato, Arek Kasprzyk, Teruyoshi Hishiki, Craig Gough, Makoto Shimada, and Naoki Nagata for their helpful discussion and comments on this study. ...doi:10.1016/j.ygeno.2011.10.002 pmid:22019378 fatcat:brfjs4ibdbhs3pp7a7dbwj2vei
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