Dataset with annotated 12-lead ECG records. The exams were taken in 811 counties in the state of Minas Gerais/Brazil by the Telehealth Network of Minas Gerais (TNMG) between 2010 and 2016. And organized by the CODE (Clinical outcomes in digital electrocardiography) group. Requesting access Researchers affiliated to educational or research institutions might make requests to access this data dataset. Requests will be analyzed on an individual basis and should contain: Name of PI and host organisation; Contact details (including your name and email); and, the scientific purpose of data access request. If approved, a data user agreement will be forwarded to the researcher that made the request (through the email that was provided). After the agreement has been signed (by the researcher or by the research institution) access to the dataset will be granted. Openly available subset: A subset of this dataset (with 15% of the patients) is openly available. See: "CODE-15%: a large scale annotated dataset of 12-lead ECGs" https://doi.org/10.5281/zenodo.4916206. Content The folder contains: A column separated file containing basic patient attributes. The ECG waveforms in the wfdb format. Additional references The dataset is described in the paper "Automatic diagnosis of the 12-lead ECG using a deep neural network". https://www.nature.com/articles/s41467-020-15432-4. Related publications also using this dataset are: - [1] G. Paixao et al., “Validation of a Deep Neural Network Electrocardiographic-Age as a Mortality Predictor: The CODE Study,” Circulation, vol. 142, no. Suppl_3, pp. A16883–A16883, Nov. 2020, doi: 10.1161/circ.142.suppl_3.16883.- [2] A. L. P. Ribeiro et al., “Tele-electrocardiography and bigdata: The CODE (Clinical Outcomes in Digital Electrocardiography) study,” Journal of Electrocardiology, Sep. 2019, doi: 10/gf7pwg.- [3] D. M. Oliveira, A. H. Ribeiro, J. A. O. Pedrosa, G. M. M. Paixao, A. L. P. Ribeiro, and W. Meira Jr, “Explaining end-to-end ECG automated diagnosis using contextual features,” in Machine Learning and Knowledge Discovery in Databases. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Ghent, Belgium, Sep. 2020, vol. 12461, pp. 204--219. doi: 10.1007/978-3-030-67670-4_13.- [4] D. M. Oliveira, A. H. Ribeiro, J. A. O. Pedrosa, G. M. M. Paixao, A. L. Ribeiro, and W. M. Jr, “Explaining black-box automated electrocardiogram classification to cardiologists,” in 2020 Computing in Cardiology (CinC), 2020, vol. 47. doi: 10.22489/CinC.2020.452.- [5] G. M. M. Paixão et al., “Evaluation of mortality in bundle branch block patients from an electronic cohort: Clinical Outcomes in Digital Electrocardiography (CODE) study,” Journal of Electrocardiology, Sep. 2019, doi: 10/dcgk.- [6] G. M. M. Paixão et al., “Evaluation of Mortality in Atrial Fibrillation: Clinical Outcomes in Digital Electrocardiography (CODE) Study,” Global Heart, vol. 15, no. 1, p. 48, Jul. 2020, doi: 10.5334/gh.772.- [7] G. M. M. Paixão et al., “Electrocardiographic Predictors of Mortality: Data from a Primary Care Tele-Electrocardiography Cohort of Brazilian Patients,” Hearts, vol. 2, no. 4, Art. no. 4, Dec. 2021, doi: 10.3390/hearts2040035.- [8] G. M. Paixão et al., “ECG-AGE FROM ARTIFICIAL INTELLIGENCE: A NEW PREDICTOR FOR MORTALITY? THE CODE (CLINICAL OUTCOMES IN DIGITAL ELECTROCARDIOGRAPHY) STUDY,” Journal of the American College of Cardiology, vol. 75, no. 11 Supplement 1, p. 3672, 2020, doi: 10.1016/S0735-1097(20)34299-6.- [9] E. M. Lima et al., “Deep neural network estimated electrocardiographic-age as a mortality predictor,” Nature Communications, vol. 12, 2021, doi: 10.1038/s41467-021-25351-7.- [10] W. Meira Jr, A. L. P. Ribeiro, D. M. Oliveira, and A. H. Ribeiro, “Contextualized Interpretable Machine Learning for Medical Diagnosis,” Communications of the ACM, 2020, doi: 10.1145/3416965.- [11] A. H. Ribeiro et al., “Automatic diagnosis of the 12-lead ECG using a deep neural network,” Nature Communications, vol. 11, no. 1, p. 1760, 2020, doi: 10/drkd.- [12] A. H. Ribeiro et al., “Automatic Diagnosis of Short-Duration 12-Lead ECG using a Deep Convolutional Network,” Machine Learning for Health (ML4H) Workshop at NeurIPS, 2018.- [13] A. H. Ribeiro et al., “Automatic 12-lead ECG classification using a convolutional network ensemble,” 2020. doi: 10.22489/CinC.2020.130.- [14] V. Sangha et al., “Automated Multilabel Diagnosis on Electrocardiographic Images and Signals,” medRxiv, Sep. 2021, doi: 10.1101/2021.09.22.21263926.- [15] S. Biton et al., “Atrial fibrillation risk prediction from the 12-lead ECG using digital biomarkers and deep representation learning,” European Heart Journal - Digital Health, 2021, doi: 10.1093/ehjdh/ztab071. Code: The following github repositories perform analysis that use this dataset: - https://github.com/antonior92/automatic-ecg-diagnosis- https://github.com/antonior92/ecg-age-prediction Related Datasets: - CODE-test: An annotated 12-lea

It is becoming increasingly clear that the next generation of web search and advertising will rely on a deeper understanding of user intent and task modeling, and a correspondingly richer interpretation of content on the web. How we get there, in particular, how we understand web content in richer terms than bags of words and links, is a wide open and fascinating question. I will discuss some of the options here, and look closely at the role that information extraction can play. Speaker Bio Raghu Ramakrishnan is Chief Scientist for Audience and Cloud Computing at Yahoo!, and is a Research Fellow, heading the Community Systems area in Yahoo! Research. He was Professor of Computer Sciences at the University of Wisconsin-Madison, and was founder and CTO of QUIQ, a company that pioneered question-answering communities, powering Ask Jeeves' AnswerPoint as well as customer-support for companies such as Compaq. His research has influenced query optimization in commercial database systems, and the design of window functions in SQL:1999. His paper on the Birch clustering algorithm received the SIGMOD 10-Year Test-of-Time award, and he has written the widely-used text "Database Management Systems" (with Johannes Gehrke). He is Chair of ACM SIGMOD, on the Board of Directors of ACM SIGKDD and the Board of Trustees of the VLDB Endowment, and has served as editor-in-chief of the Journal of Data Mining and Knowledge Discovery, associate editor of ACM Transactions on Database Systems, and the Database area editor of the Journal of Logic Programming. Ramakrishnan is a Fellow of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE), and has received several awards, including a Distinguished Alumnus Award from IIT Madras, a Packard Foundation Fellowship in Science and Engineering, an NSF Presidential Young Investigator Award, and an ACM SIGMOD Contributions Award.

Self-citation analysis data based on PubMed Central subset (2002-2005) ---------------------------------------------------------------------- Created by Shubhanshu Mishra, Brent D. Fegley, Jana Diesner, and Vetle Torvik on April 5th, 2018 ## Introduction This is a dataset created as part of the publication titled: Mishra S, Fegley BD, Diesner J, Torvik VI (2018) Self-Citation is the Hallmark of Productive Authors, of Any Gender. PLOS ONE. It contains files for running the self citation analysis on articles published in PubMed Central between 2002 and 2005, collected in 2015. The dataset is distributed in the form of the following tab separated text files: * Training_data_2002_2005_pmc_pair_First.txt (1.2G) - Data for first authors * Training_data_2002_2005_pmc_pair_Last.txt (1.2G) - Data for last authors * Training_data_2002_2005_pmc_pair_Middle_2nd.txt (964M) - Data for middle 2nd authors * Training_data_2002_2005_pmc_pair_txt.header.txt - Header for the data * COLUMNS_DESC.txt file - Descriptions of all columns * model_text_files.tar.gz - Text files containing model coefficients and scores for model selection. * results_all_model.tar.gz - Model coefficient and result files in numpy format used for plotting purposes. v4.reviewer contains models for analysis done after reviewer comments. * README.txt file ## Dataset creation Our experiments relied on data from multiple sources including properitery data from Thompson Rueter's (now Clarivate Analytics) Web of Science collection of MEDLINE citations. Author's interested in reproducing our experiments should personally request from Clarivate Analytics for this data. However, we do make a similar but open dataset based on citations from PubMed Central which can be utilized to get similar results to those reported in our analysis. Furthermore, we have also freely shared our datasets which can be used along with the citation datasets from Clarivate Analytics, to re-create the datased used in our experiments. These datasets are listed below. If you wish to use any of those datasets please make sure you cite both the dataset as well as the paper introducing the dataset. * MEDLINE 2015 baseline: https://www.nlm.nih.gov/bsd/licensee/2015_stats/baseline_doc.html * Citation data from PubMed Central (original paper includes additional citations from Web of Science) * Author-ity 2009 dataset: - Dataset citation: Torvik, Vetle I.; Smalheiser, Neil R. (2018): Author-ity 2009 - PubMed author name disambiguated dataset. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4222651_V1 - Paper citation: Torvik, V. I., & Smalheiser, N. R. (2009). Author name disambiguation in MEDLINE. ACM Transactions on Knowledge Discovery from Data, 3(3), 1–29. https://doi.org/10.1145/1552303.1552304 - Paper citation: Torvik, V. I., Weeber, M., Swanson, D. R., & Smalheiser, N. R. (2004). A probabilistic similarity metric for Medline records: A model for author name disambiguation. Journal of the American Society for Information Science and Technology, 56(2), 140–158. https://doi.org/10.1002/asi.20105 * Genni 2.0 + Ethnea for identifying author gender and ethnicity: - Dataset citation: Torvik, Vetle (2018): Genni + Ethnea for the Author-ity 2009 dataset. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9087546_V1 - Paper citation: Smith, B. N., Singh, M., & Torvik, V. I. (2013). A search engine approach to estimating temporal changes in gender orientation of first names. In Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries - JCDL ’13. ACM Press. https://doi.org/10.1145/2467696.2467720 - Paper citation: Torvik VI, Agarwal S. Ethnea -- an instance-based ethnicity classifier based on geo-coded author names in a large-scale bibliographic database. International Symposium on Science of Science March 22-23, 2016 - Library of Congress, Washington DC, USA. http://hdl.handle.net/2142/88927 * MapAffil for identifying article country of affiliation: - Dataset citation: Torvik, Vetle I. (2018): MapAffil 2016 dataset -- PubMed author affiliations mapped to cities and their geocodes worldwide. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4354331_V1 - Paper citation: Torvik VI. MapAffil: A Bibliographic Tool for Mapping Author Affiliation Strings to Cities and Their Geocodes Worldwide. D-Lib magazine : the magazine of the Digital Library Forum. 2015;21(11-12):10.1045/november2015-torvik * IMPLICIT journal similarity: - Dataset citation: Torvik, Vetle (2018): Author-implicit journal, MeSH, title-word, and affiliation-word pairs based on Author-ity 2009. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4742014_V1 * Novelty dataset for identify article level novelty: - Dataset citation: Mishra, Shubhanshu; Torvik, Vetle I. (2018): Conceptual novelty scores for PubMed articles. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-5060298_V1 - Paper citation: Mishra S, Torvik VI. Quantifying Conceptual Novelty in the Biomedical Literature. D-Lib magazine : The Magazine of the Digital Library Forum. 2016;22(9-10):10.1045/september2016-mishra - Code: https://github.com/napsternxg/Novelty * Expertise dataset for identifying author expertise on articles: * Source code provided at: https://github.com/napsternxg/PubMed_SelfCitationAnalysis Note: The dataset is based on a snapshot of PubMed (which includes Medline and PubMed-not-Medline records) taken in the first week of October, 2016. Check here for information to get PubMed/MEDLINE, and NLMs data Terms and Conditions Additional data related updates can be found at Torvik Research Group ## Acknowledgments This work was made possible in part with funding to VIT from NIH grant P01AG039347 and NSF grant 1348742. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ## License Self-citation analysis data based on PubMed Central subset (2002-2005) by Shubhanshu Mishra, Brent D. Fegley, Jana Diesner, and Vetle Torvik is licensed under a Creative Commons Attribution 4.0 International License. Permissions beyond the scope of this license may be available at https://github.com/napsternxg/PubMed_SelfCitationAnalysis.

Sample of database transaction.

Abstract The predictability of epidemiological indicators can help estimate dependent variables, assist in decision-making to support public policies, and explain the scenarios experienced by different countries worldwide. This study aimed to forecast the Human Development Index (HDI) and life expectancy (LE) for Latin American countries for the period of 2015-2020 using data mining techniques. All stages of the process of knowledge discovery in databases were covered. The SMOReg data mining algorithm was used in the models with multivariate time series to make predictions; this algorithm performed the best in the tests developed during the evaluation period. The average HDI and LE for Latin American countries showed an increasing trend in the period evaluated, corresponding to 4.99 ± 3.90% and 2.65 ± 0.06 years, respectively. Multivariate models allow for a greater evaluation of algorithms, thus increasing their accuracy. Data mining techniques have a better predictive quality relative to the most popular technique, Autoregressive Integrated Moving Average (ARIMA). In addition, the predictions suggest that there will be a higher increase in the mean HDI and LE for Latin American countries compared to the mean values for the rest of the world.

Author-ity 2009 baseline dataset. Prepared by Vetle Torvik 2009-12-03 The dataset comes in the form of 18 compressed (.gz) linux text files named authority2009.part00.gz - authority2009.part17.gz. The total size should be ~17.4GB uncompressed. • How was the dataset created? The dataset is based on a snapshot of PubMed (which includes Medline and PubMed-not-Medline records) taken in July 2009. A total of 19,011,985 Article records and 61,658,514 author name instances. Each instance of an author name is uniquely represented by the PMID and the position on the paper (e.g., 10786286_3 is the third author name on PMID 10786286). Thus, each cluster is represented by a collection of author name instances. The instances were first grouped into "blocks" by last name and first name initial (including some close variants), and then each block was separately subjected to clustering. Details are described in Torvik, V., & Smalheiser, N. (2009). Author name disambiguation in MEDLINE. ACM Transactions On Knowledge Discovery From Data, 3(3), doi:10.1145/1552303.1552304 Torvik, V. I., Weeber, M., Swanson, D. R., & Smalheiser, N. R. (2005). A Probabilistic Similarity Metric for Medline Records: A Model for Author Name Disambiguation. Journal Of The American Society For Information Science & Technology, 56(2), 140-158. doi:10.1002/asi.20105 Note that for Author-ity 2009, some new predictive features (e.g., grants, citations matches, temporal, affiliation phrases) and a post-processing merging procedure were applied (to capture name variants not capture during blocking e.g. matches for subsets of compound last name matches, and nicknames with different first initial like Bill and William), and a temporal feature was used -- this has not yet been written up for publication. • How accurate is the 2009 dataset (compared to 2006 and 2009)? The recall reported for 2006 of 98.8% has been much improved in 2009 (because common last name variants are now captured). Compared to 2006, both years 2008 and 2009 overall seem to exhibit a higher rate of splitting errors but lower rate of lumping errors. This reflects an overall decrease in prior probabilites -- possibly because e.g. a) new prior estimation procedure that avoid wild estimates (by dampening the magnitude of iterative changes); b) 2008 and 2009 included items in Pubmed-not-Medline (including in-process items); and c) and the dramatic (exponential) increase in frequencies of some names (J. Lee went from ~16,000 occurrences in 2006 to 26,000 in 2009.) Although, splitting is reduced in 2009 for some special cases like NIH funded investigators who list their grant number of their papers. Compared to 2008, splitting errors were reduced overall in 2009 while maintaining the same level of lumping errors. • What is the format of the dataset? The cluster summaries for 2009 are much more extenstive than the 2008 dataset. Each line corresponds to a predicted author-individual represented by cluster of author name instances and a summary of all the corresponding papers and author name variants (and if there are > 10 papers in the cluster, an identical summary of the 10 most recent papers). Each cluster has a unique Author ID (which is uniquely identified by the PMID of the earliest paper in the cluster and the author name position. The summary has the following tab-delimited fields: 1. blocks separated by '||'; each block may consist of multiple lastname-first initial variants separated by '|' 2. prior probabilities of the respective blocks separated by '|' 3. Cluster number relative to the block ordered by cluster size (some are listed as 'CLUSTER X' when they were derived from multiple blocks) 4. Author ID (or cluster ID) e.g., bass_c_9731334_2 represents a cluster where 9731334_2 is the earliest author name instance. Although not needed for uniqueness, the id also has the most frequent lastname_firstinitial (lowercased). 5. cluster size (number of author name instances on papers) 6. name variants separated by '|' with counts in parenthesis. Each variant of the format lastname_firstname middleinitial, suffix 7. last name variants separated by '|' 8. first name variants separated by '|' 9. middle initial variants separated by '|' ('-' if none) 10. suffix variants separated by '|' ('-' if none) 11. email addresses separated by '|' ('-' if none) 12. range of years (e.g., 1997-2009) 13. Top 20 most frequent affiliation words (after stoplisting and tokenizing; some phrases are also made) with counts in parenthesis; separated by '|'; ('-' if none) 14. Top 20 most frequent MeSH (after stoplisting; "-") with counts in parenthesis; separated by '|'; ('-' if none) 15. Journals with counts in parenthesis (separated by "|"), 16. Top 20 most frequent title words (after stoplisting and tokenizing) with counts in parenthesis; separated by '|'; ('-' if none) 17. Co-author names (lowercased lastname and first/middle initials) with counts in parenthesis; separated by '|'; ('-' if none) 18. Co-author IDs with counts in parenthesis; separated by '|'; ('-' if none) 19. Author name instances (PMID_auno separated '|') 20. Grant IDs (after normalization; "-" if none given; separated by "|"), 21. Total number of times cited. (Citations are based on references extracted from PMC). 22. h-index 23. Citation counts (e.g., for h-index): PMIDs by the author that have been cited (with total citation counts in parenthesis); separated by "|" 24. Cited: PMIDs that the author cited (with counts in parenthesis) separated by "|" 25. Cited-by: PMIDs that cited the author (with counts in parenthesis) separated by "|" 26-47. same summary as for 4-25 except that the 10 most recent papers were used (based on year; so if paper 10, 11, 12... have the same year, one is selected arbitrarily)

Asia Pacific Journal of Risk and Insurance Impact Factor 2024-2025 - ResearchHelpDesk - As the official journal of the Asia-Pacific Risk and Insurance Association, the Asia-Pacific Journal of Risk and Insurance (APJRI) focuses on risk management and insurance issues of importance to the Asia-Pacific region. An interdisciplinary publication, APJRI facilitates the exchange of research in risk and insurance mathematics, economics, finance, and corporate practice. The journal welcomes theoretical and applied research papers on a variety of specific topics. Topics Actuarial pricing and reserving Insurance operations Economics and regulation Corporate/enterprise risk management and finance Catastrophe risk Social insurance and employee benefits Local/regional/international insurance markets Abstracting & Indexing Asia-Pacific Journal of Risk and Insurance is covered by the following services: Baidu Scholar Bibliography of Asian Studies Cabell's Whitelist CNKI Scholar (China National Knowledge Infrastructure) CNPIEC - cnpLINKer Dimensions EBSCO (relevant databases) EBSCO Discovery Service EconBiz EconLit ERIH PLUS (European Reference Index for the Humanities and Social Sciences) Genamics JournalSeek Google Scholar Index Islamicus J-Gate JournalGuide JournalTOCs KESLI-NDSL (Korean National Discovery for Science Leaders) Microsoft Academic MyScienceWork Naver Academic Naviga (Softweco) Norwegian Register for Scientific Journals, Series and Publishers Primo Central (ExLibris) ProQuest (relevant databases) Publons QOAM (Quality Open Access Market) ReadCube Research Papers in Economics (RePEc) Semantic Scholar Sherpa/RoMEO Summon (ProQuest) TDNet Ulrich's Periodicals Directory/ulrichsweb WanFang Data WorldCat (OCLC)

Dataset, transaction database.

Acta medica marisiensis Impact Factor 2024-2025 - ResearchHelpDesk - Acta Medica Marisiensis is the official publication of the University of Medicine, Pharmacy, Sciences and Technology of Târgu MureÅŸ, being published by University Press. The journal publishes high-quality articles on various subjects related to research and medical practice from the all the medical and pharmaceutical fields, ranging from basic to clinical research and corresponding to different article types such as: reviews, original articles, case reports, case series, letter to editor or brief reports. The journal also publishes short information or editorial notes in relation to different aspects of the medical and academic life. The journal addresses the entire academic community of specialists and researchers activating in different fields of medicine, dental medicine and pharmacy, in an attempt to provide them the latest research developments in their field of activity. Acta Medica Marisiensis is indexed in the following international databases: Baidu Scholar Case Celdes Chemical Abstracts Service (CAS) - CAplus Chemical Abstracts Service (CAS) - SciFinder CNKI Scholar (China National Knowledge Infrastructure) CNPIEC DOAJ (Directory of Open Access Journals) EBSCO (relevant databases) EBSCO Discovery Service (since 01 July 2010, first indexed number - no.4/2010) Genamics JournalSeek Google Scholar J-Gate JournalTOCs KESLI-NDSL (Korean National Discovery for Science Leaders) Meta (formerly Sciencescape) Naviga (Softweco) Primo Central (ExLibris) Publons ReadCube ResearchGate Sherpa/RoMEO Summon (Serials Solutions/ProQuest) TDNet Ulrich's Periodicals Directory/ulrichsweb WanFang Data WorldCat (OCLC)

Drug Safety (DS) is a domain with significant public health and social impact. Knowledge Engineering (KE) is the Computer Science discipline elaborating on methods and tools for developing “knowledge-intensive” systems, depending on a conceptual “knowledge” schema and some kind of “reasoning” process. The present systematic and mapping review aims to investigate KE-based approaches employed for DS and highlight the introduced added value as well as trends and possible gaps in the domain. Journal articles published between 2006 and 2017 were retrieved from PubMed/MEDLINE and Web of Science® (873 in total) and filtered based on a comprehensive set of inclusion/exclusion criteria. The 80 finally selected articles were reviewed on full-text, while the mapping process relied on a set of concrete criteria (concerning specific KE and DS core activities, special DS topics, employed data sources, reference ontologies/terminologies, and computational methods, etc.). The analysis results are publicly available as online interactive analytics graphs. The review clearly depicted increased use of KE approaches for DS. The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. Moreover, the quantified analysis of using KE for the respective DS core activities highlighted room for intensifying research on KE for ADE monitoring, prevention and reporting. Finally, the assessed use of the various data sources for DS special topics demonstrated extensive use of dominant data sources for DS surveillance, i.e., Spontaneous Reporting Systems, but also increasing interest in the use of emerging data sources, e.g., observational healthcare databases, biochemical/genetic databases, and social media. Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific mechanisms of action, etc. Nevertheless, since the reviewed studies mostly concerned proof-of-concept implementations, more intense research is required to increase the maturity level that is necessary for KE approaches to reach routine DS practice. In conclusion, we argue that efficiently addressing DS data analytics and management challenges requires the introduction of high-throughput KE-based methods for effective knowledge discovery and management, resulting ultimately, in the establishment of a continuous learning DS system.

Asia Pacific Journal of Risk and Insurance Acceptance Rate - ResearchHelpDesk - As the official journal of the Asia-Pacific Risk and Insurance Association, the Asia-Pacific Journal of Risk and Insurance (APJRI) focuses on risk management and insurance issues of importance to the Asia-Pacific region. An interdisciplinary publication, APJRI facilitates the exchange of research in risk and insurance mathematics, economics, finance, and corporate practice. The journal welcomes theoretical and applied research papers on a variety of specific topics. Topics Actuarial pricing and reserving Insurance operations Economics and regulation Corporate/enterprise risk management and finance Catastrophe risk Social insurance and employee benefits Local/regional/international insurance markets Abstracting & Indexing Asia-Pacific Journal of Risk and Insurance is covered by the following services: Baidu Scholar Bibliography of Asian Studies Cabell's Whitelist CNKI Scholar (China National Knowledge Infrastructure) CNPIEC - cnpLINKer Dimensions EBSCO (relevant databases) EBSCO Discovery Service EconBiz EconLit ERIH PLUS (European Reference Index for the Humanities and Social Sciences) Genamics JournalSeek Google Scholar Index Islamicus J-Gate JournalGuide JournalTOCs KESLI-NDSL (Korean National Discovery for Science Leaders) Microsoft Academic MyScienceWork Naver Academic Naviga (Softweco) Norwegian Register for Scientific Journals, Series and Publishers Primo Central (ExLibris) ProQuest (relevant databases) Publons QOAM (Quality Open Access Market) ReadCube Research Papers in Economics (RePEc) Semantic Scholar Sherpa/RoMEO Summon (ProQuest) TDNet Ulrich's Periodicals Directory/ulrichsweb WanFang Data WorldCat (OCLC)

Acta medica marisiensis Acceptance Rate - ResearchHelpDesk - Acta Medica Marisiensis is the official publication of the University of Medicine, Pharmacy, Sciences and Technology of Târgu MureÅŸ, being published by University Press. The journal publishes high-quality articles on various subjects related to research and medical practice from the all the medical and pharmaceutical fields, ranging from basic to clinical research and corresponding to different article types such as: reviews, original articles, case reports, case series, letter to editor or brief reports. The journal also publishes short information or editorial notes in relation to different aspects of the medical and academic life. The journal addresses the entire academic community of specialists and researchers activating in different fields of medicine, dental medicine and pharmacy, in an attempt to provide them the latest research developments in their field of activity. Acta Medica Marisiensis is indexed in the following international databases: Baidu Scholar Case Celdes Chemical Abstracts Service (CAS) - CAplus Chemical Abstracts Service (CAS) - SciFinder CNKI Scholar (China National Knowledge Infrastructure) CNPIEC DOAJ (Directory of Open Access Journals) EBSCO (relevant databases) EBSCO Discovery Service (since 01 July 2010, first indexed number - no.4/2010) Genamics JournalSeek Google Scholar J-Gate JournalTOCs KESLI-NDSL (Korean National Discovery for Science Leaders) Meta (formerly Sciencescape) Naviga (Softweco) Primo Central (ExLibris) Publons ReadCube ResearchGate Sherpa/RoMEO Summon (Serials Solutions/ProQuest) TDNet Ulrich's Periodicals Directory/ulrichsweb WanFang Data WorldCat (OCLC)

Recently, various types of biological data, including genomic sequences, have been rapidly accumulating. To discover biological knowledge from such growing heterogeneous data, a flexible framework for data integration is necessary. Ortholog information is a central resource for interlinking corresponding genes among different organisms, and the Semantic Web provides a key technology for the flexible integration of heterogeneous data. We have constructed an ortholog database using the Semantic Web technology, aiming at the integration of numerous genomic data and various types of biological information. To formalize the structure of the ortholog information in the Semantic Web, we have constructed the Ortholog Ontology (OrthO). While the OrthO is a compact ontology for general use, it is designed to be extended to the description of database-specific concepts. On the basis of OrthO, we described the ortholog information from our Microbial Genome Database for Comparative Analysis (MBGD) in the form of Resource Description Framework (RDF) and made it available through the SPARQL endpoint, which accepts arbitrary queries specified by users. In this framework based on the OrthO, the biological data of different organisms can be integrated using the ortholog information as a hub. Besides, the ortholog information from different data sources can be compared with each other using the OrthO as a shared ontology. Here we show some examples demonstrating that the ortholog information described in RDF can be used to link various biological data such as taxonomy information and Gene Ontology. Thus, the ortholog database using the Semantic Web technology can contribute to biological knowledge discovery through integrative data analysis.

469 drugs are overlapped between two networks in total.

All ITDM list.

The predefined minimum support values for items.

Acta Universitatis Sapientiae Economics and Business Impact Factor 2024-2025 - ResearchHelpDesk - Acta Universitatis Sapientiae Economics and Business - As a broad-based professional and international journal, the Acta Universitatis Sapientiae, Economics and Business welcomes submissions of applied and theoretical research papers in all fields of Economics and Business. The journal especially encourages submissions on research from management, marketing, quantitative methods and their applications, regional economics, regional development, agricultural economics, and finance. However, The journal is open to research coming from other areas of Economics and Business as well. The primary interest is to publish academic research, but the journal also welcomes good quality research coming from practitioners. Abstracting & Indexing Baidu Scholar Cabell's Whitelist CNKI Scholar (China National Knowledge Infrastructure) CNPIEC - cnpLINKer Dimensions DOAJ (Directory of Open Access Journals) EBSCO (relevant databases) EBSCO Discovery Service Genamics JournalSeek Google Scholar J-Gate JournalTOCs KESLI-NDSL (Korean National Discovery for Science Leaders) Microsoft Academic MyScienceWork Naviga (Softweco) Primo Central (ExLibris) Publons QOAM (Quality Open Access Market) ReadCube Research Papers in Economics (RePEc) Semantic Scholar Sherpa/RoMEO Summon (ProQuest) TDNet Ulrich's Periodicals Directory/Ulrich web WanFang Data WorldCat (OCLC)

The executing time (in Millisecond) comparison among Apriori, TDApriori and ITDApriori on T10|4D100K dataset.

The support values of all the items for the given 10 transactions.

Item change and scan probability and it effects.