data mining


Summary: Use of sophisticated analysis tools to sort through, organize, examine, and combine large sets of information.

Top Publications

  1. Yao Y, Salas A, Logan I, Bandelt H. mtDNA data mining in GenBank needs surveying. Am J Hum Genet. 2009;85:929-33; author reply 933 pubmed publisher
  2. Service R. Biology's dry future. Science. 2013;342:186-9 pubmed publisher
  3. Li J, Zheng S, Chen B, Butte A, Swamidass S, Lu Z. A survey of current trends in computational drug repositioning. Brief Bioinform. 2016;17:2-12 pubmed publisher
    ..Finally, we highlight potential opportunities and use-cases, including a few target areas such as cancers. We conclude with a brief discussion of the remaining challenges in computational drug repositioning. ..
  4. Lamprell G, Braithwaite J. Mainstreaming gender and promoting intersectionality in Papua New Guinea's health policy: a triangulated analysis applying data-mining and content analytic techniques. Int J Equity Health. 2017;16:65 pubmed publisher
    ..Second, there should be greater focus on activists and civil society groups as the stakeholders most likely to make a difference in gender equity. ..
  5. van den Bergh T, Tamò G, Nobili A, Tao Y, Tan T, Bornscheuer U, et al. CorNet: Assigning function to networks of co-evolving residues by automated literature mining. PLoS ONE. 2017;12:e0176427 pubmed publisher
    ..The resulting set of mutations indeed showed many instances of increased enantioselectivity. ..
  6. Piscitelli A, Tarallo V, Guarino L, Sannia G, Birolo L, Pezzella C. New lipases by mining of Pleurotus ostreatus genome. PLoS ONE. 2017;12:e0185377 pubmed publisher
    ..PleoLip241 was used to remove the hydrophobic layer from wool surface in order to improve its dyeability. The encouraging results obtained with lipase treated wool led to forecast PleoLip241 applicability in this field. ..
  7. Craig D. Understanding the links between privacy and public data sharing. Nat Methods. 2016;13:211-2 pubmed
  8. Zheng H, Langner K, Shields G, Hou J, Kowiel M, Allen F, et al. Data mining of iron(II) and iron(III) bond-valence parameters, and their relevance for macromolecular crystallography. Acta Crystallogr D Struct Biol. 2017;73:316-325 pubmed publisher
    ..The significance of this data-driven method for parameter discovery, and how the spin state affects the interpretation of heme-containing proteins and iron-binding sites in macromolecular structures, are discussed. ..
  9. McMurry J, Juty N, Blomberg N, Burdett T, Conlin T, Conte N, et al. Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data. PLoS Biol. 2017;15:e2001414 pubmed publisher
    ..We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines. ..

More Information

Publications108 found, 100 shown here

  1. Boscarino J, Moorman A, Rupp L, Zhou Y, Lu M, Teshale E, et al. Comparison of ICD-9 Codes for Depression and Alcohol Misuse to Survey Instruments Suggests These Codes Should Be Used with Caution. Dig Dis Sci. 2017;62:2704-2712 pubmed publisher
    ..ICD-9 codes were limited in predicting current depression and alcohol misuse, suggesting that caution should be exercised when using ICD-9 codes to assess depression or alcohol misuse among CHC patients. ..
  2. Rüping S, Anguita A, Bucur A, Cirstea T, Jacobs B, Torge A. Improving the implementation of clinical decision support systems. Conf Proc IEEE Eng Med Biol Soc. 2013;2013:3214-7 pubmed publisher
    ..This, in turn, allows to use data mining tools to automatically create hypotheses for CDS models, which reduces the manual workload in the creation of a ..
  3. Morrow J, Riegler M, Gilchrist A, Shearman D, Frommer M. Comprehensive transcriptome analysis of early male and female Bactrocera jarvisi embryos. BMC Genet. 2014;15 Suppl 2:S7 pubmed publisher
    ..Our data contribute fundamental information to sex-determination research, and provide candidates for the sourcing of gene promoters for transgenic pest-management strategies of tephritid fruit flies. ..
  4. Xu R, Wang Q. Comparing a knowledge-driven approach to a supervised machine learning approach in large-scale extraction of drug-side effect relationships from free-text biomedical literature. BMC Bioinformatics. 2015;16 Suppl 5:S6 pubmed publisher
    ..In summary, we automatically constructed a large-scale higher-level drug phenotype relationship knowledge, which can have great potential in computational drug discovery. ..
  5. Glotov A, Tiys E, Vashukova E, Pakin V, Demenkov P, Saik O, et al. Molecular association of pathogenetic contributors to pre-eclampsia (pre-eclampsia associome). BMC Syst Biol. 2015;9 Suppl 2:S4 pubmed publisher
    ..Recently, we have shown a similar result for inversely comorbid diseases. This suggests that comorbid and inversely comorbid diseases have common features concerning structural organization of associative molecular genetic networks. ..
  6. Corchado J, Bichindaritz I, De Paz J. Distributed artificial intelligence models for knowledge discovery in bioinformatics. Biomed Res Int. 2015;2015:846785 pubmed publisher
  7. Archambault P, Van De Belt T, Kuziemsky C, Plaisance A, Dupuis A, McGinn C, et al. Collaborative writing applications in healthcare: effects on professional practice and healthcare outcomes. Cochrane Database Syst Rev. 2017;5:CD011388 pubmed publisher
  8. Leitner F, Krallinger M, Cesareni G, Valencia A. The FEBS Letters SDA corpus: a collection of protein interaction articles with high quality annotations for the BioCreative II.5 online challenge and the text mining community. FEBS Lett. 2010;584:4129-30 pubmed publisher
  9. Madden S, Clarke C, Gaule P, Aherne S, O Donovan N, Clynes M, et al. BreastMark: an integrated approach to mining publicly available transcriptomic datasets relating to breast cancer outcome. Breast Cancer Res. 2013;15:R52 pubmed
    ..The value of this tool will be in the preliminary assessment of putative biomarkers in breast cancer. It will be of particular use to research groups with limited bioinformatics facilities. ..
  10. Pafilis E, Frankild S, Fanini L, Faulwetter S, Pavloudi C, Vasileiadou A, et al. The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text. PLoS ONE. 2013;8:e65390 pubmed publisher along with dictionary files and the manually annotated gold-standard corpus. The ORGANISMS web resource can be found at ..
  11. Chien P, Tseng Y, Hsu Y, Lai Y, Weng S. Frequency and pattern of Chinese herbal medicine prescriptions for urticaria in Taiwan during 2009: analysis of the national health insurance database. BMC Complement Altern Med. 2013;13:209 pubmed publisher
    ..Frequent itemset mining, as applied to data mining, was used to analyse co-prescription of CHM for patients with urticaria...
  12. Rybinski M, Aldana Montes J. Calculating semantic relatedness for biomedical use in a knowledge-poor environment. BMC Bioinformatics. 2014;15 Suppl 14:S2 pubmed publisher
    ..Our method improves flexibility of the existing methods without a notable loss of quality. It is a legitimate alternative to the costly construction of specialized knowledge-rich resources. ..
  13. Winnenburg R, Sorbello A, Ripple A, Harpaz R, Tonning J, Szarfman A, et al. Leveraging MEDLINE indexing for pharmacovigilance - Inherent limitations and mitigation strategies. J Biomed Inform. 2015;57:425-35 pubmed publisher
    ..ADEs extracted from MEDLINE indexing are complementary to, not a replacement for, other sources. ..
  14. Incoronato M, Aiello M, Infante T, Cavaliere C, Grimaldi A, Mirabelli P, et al. Radiogenomic Analysis of Oncological Data: A Technical Survey. Int J Mol Sci. 2017;18: pubmed publisher
    ..The impact of single and combined techniques will be discussed in light of their suitability in correlation and predictive studies of specific oncologic diseases. ..
  15. Komenda M, Karolyi M, Pokorná A, Vaitsis C. Medical and Healthcare Curriculum Exploratory Analysis. Stud Health Technol Inform. 2017;235:231-235 pubmed
    ..The achieved results from the selected analytical issues are presented here in clear visual interpretations in an attempt to visually describe the entire medical and healthcare curriculum. ..
  16. Martin Sanchez F, Aguiar Pulido V, López Campos G, Peek N, Sacchi L. Secondary Use and Analysis of Big Data Collected for Patient Care. Yearb Med Inform. 2017;26:28-37 pubmed publisher
    ..This contribution from the IMIA working group on "Data mining and Big Data analytics" focuses on the literature published during the last two years, covering the ..
  17. Zwierzyna M, Overington J. Classification and analysis of a large collection of in vivo bioassay descriptions. PLoS Comput Biol. 2017;13:e1005641 pubmed publisher
    ..Finally, we combine information mined from text with curated annotations stored in ChEMBL to investigate the patterns of usage of different animal models across a range of experiments, drug classes, and disease areas. ..
  18. Kim Y, Yoon D, Byun J, Park H, Lee A, Kim I, et al. Extracting information from free-text electronic patient records to identify practice-based evidence of the performance of coronary stents. PLoS ONE. 2017;12:e0182889 pubmed publisher
    ..Post surveillance of the practice based evidence in the PCI data warehouse indicated that the biodegradable-polymer DES might have a lower risk of TVR than the durable-polymer DES. ..
  19. Dahabreh I, Kent D. Can the learning health care system be educated with observational data?. JAMA. 2014;312:129-30 pubmed publisher
  20. Cano I, Tényi Á, Schueller C, Wolff M, Huertas Migueláñez M, Gomez Cabrero D, et al. The COPD Knowledge Base: enabling data analysis and computational simulation in translational COPD research. J Transl Med. 2014;12 Suppl 2:S6 pubmed publisher
    ..We plan to further extend the COPDKB by offering it as a repository to publish and semantically integrate data from relevant clinical trials. The COPDKB is freely available after registration at ..
  21. Chen L, Vallmuur K, Nayak R. Injury narrative text classification using factorization model. BMC Med Inform Decis Mak. 2015;15 Suppl 1:S5 pubmed publisher
    ..With the selection of right dimension k, Non Negative Matrix Factorization-model method achieves 10 CV accuracy of 0.93. ..
  22. Sousa S, Leitão J, Martins R, Sanches J, Suri J, Giorgetti A. Bioinformatics Applications in Life Sciences and Technologies. Biomed Res Int. 2016;2016:3603827 pubmed publisher
  23. Alborzi S, Devignes M, Ritchie D. ECDomainMiner: discovering hidden associations between enzyme commission numbers and Pfam domains. BMC Bioinformatics. 2017;18:107 pubmed publisher
    ..These EC-Pfam associations could be used to annotate some 58,722 protein chains in the PDB which currently lack any EC annotation. The ECDomainMiner database is publicly available at . ..
  24. Hornbrook M, Goshen R, Choman E, O Keeffe Rosetti M, Kinar Y, Liles E, et al. Early Colorectal Cancer Detected by Machine Learning Model Using Gender, Age, and Complete Blood Count Data. Dig Dis Sci. 2017;62:2719-2727 pubmed publisher
  25. Burger A, Paschke A, Romano P, Marshall M, Splendiani A. Semantic Web applications and tools for the life sciences: SWAT4LS 2010. BMC Bioinformatics. 2012;13 Suppl 1:S1 pubmed publisher
  26. Golden A, Djorgovski S, Greally J. Astrogenomics: big data, old problems, old solutions?. Genome Biol. 2013;14:129 pubmed publisher
  27. Posada D. Phylogenomics for Systematic Biology. Syst Biol. 2016;65:353-6 pubmed publisher
  28. Weichenberger C, Pozharski E, Rupp B. Twilight reloaded: the peptide experience. Acta Crystallogr D Struct Biol. 2017;73:211-222 pubmed publisher
  29. Gabel E, Hofer I, Satou N, Grogan T, Shemin R, Mahajan A, et al. Creation and Validation of an Automated Algorithm to Determine Postoperative Ventilator Requirements After Cardiac Surgery. Anesth Analg. 2017;124:1423-1430 pubmed publisher
  30. Long E, Xu S, Liu Z, Wu X, Zhang X, Wang J, et al. Construction and implications of structural equation modeling network for pediatric cataract: a data mining research of rare diseases. BMC Ophthalmol. 2017;17:74 pubmed publisher
    ..the interrelationship and the effectiveness of potential factors of pediatric cataract, for the exploration of data mining strategy in the scenarios of rare diseases...
  31. Hu H, Chen Y, Tang K. A novel decision-tree method for structured continuous-label classification. IEEE Trans Cybern. 2013;43:1734-46 pubmed
    ..5 algorithm using eight real data sets. The empirical results indicate that the proposed method outperforms the C4.5 algorithm with regard to prediction accuracy, prediction specificity, and computational complexity. ..
  32. Parikh R, Kakad M, Bates D. Integrating Predictive Analytics Into High-Value Care: The Dawn of Precision Delivery. JAMA. 2016;315:651-2 pubmed publisher
  33. Li F, Zhang M, Fu G, Ji D. A neural joint model for entity and relation extraction from biomedical text. BMC Bioinformatics. 2017;18:198 pubmed publisher
    ..In addition, parameter sharing is an alternative method for neural models to jointly process this task. Our work can facilitate the research on biomedical text mining. ..
  34. Lu Y, Wu Y, Liu J, Li J, Zhang P. Understanding Health Care Social Media Use From Different Stakeholder Perspectives: A Content Analysis of an Online Health Community. J Med Internet Res. 2017;19:e109 pubmed publisher
    ..These findings could help improve social media services to facilitate diverse stakeholder engagement for health information sharing and social interaction more effectively. ..
  35. Perez Riverol Y, Bai M, da Veiga Leprevost F, Squizzato S, Park Y, Haug K, et al. Discovering and linking public omics data sets using the Omics Discovery Index. Nat Biotechnol. 2017;35:406-409 pubmed publisher
  36. Holzinger A, Dehmer M, Jurisica I. Knowledge Discovery and interactive Data Mining in Bioinformatics--State-of-the-Art, future challenges and research directions. BMC Bioinformatics. 2014;15 Suppl 6:I1 pubmed publisher
  37. Drawnel F, Zhang J, Küng E, Aoyama N, Benmansour F, Araujo Del Rosario A, et al. Molecular Phenotyping Combines Molecular Information, Biological Relevance, and Patient Data to Improve Productivity of Early Drug Discovery. Cell Chem Biol. 2017;24:624-634.e3 pubmed publisher
    ..Our results advocate for application of molecular phenotyping in early drug discovery, promoting biological relevance as a key selection criterion early in the drug development cascade. ..
  38. Davis F, Sutzko D, Grey S, Mansour M, Jain K, Nypaver T, et al. Predictors of surgical site infection after open lower extremity revascularization. J Vasc Surg. 2017;65:1769-1778.e3 pubmed publisher
    ..Several patient, operative, and hospital-related risk factors that predict postoperative SSI were identified, suggesting that targeted improvements in perioperative care may decrease complications and improve vascular patient outcomes. ..
  39. Huang T, Chen L, Song J, Zheng M, Yang J, Zhang Z. Integrated Analysis of Multiscale Large-Scale Biological Data for Investigating Human Disease 2016. Biomed Res Int. 2016;2016:6585069 pubmed
  40. Takeda K, Takahashi K, Masukawa H, Shimamori Y. Influence on Learning of a Collaborative Learning Method Comprising the Jigsaw Method and Problem-based Learning (PBL). Yakugaku Zasshi. 2017;137:659-664 pubmed publisher
    ..This finding suggests that the use of text mining to analyze qualitative data could enable teachers to evaluate the effectiveness of various methods employed to improve learning. ..
  41. Shah N, Tenenbaum J. The coming age of data-driven medicine: translational bioinformatics' next frontier. J Am Med Inform Assoc. 2012;19:e2-4 pubmed
  42. Jalali A, Buckley E, Lynch J, Schwab P, Licht D, Nataraj C. Prediction of periventricular leukomalacia occurrence in neonates after heart surgery. IEEE J Biomed Health Inform. 2014;18:1453-60 pubmed publisher
    ..A data mining approach has been employed to generate a set of rules for classification of subjects as healthy or PVL affected...
  43. Loharch S, Bhutani I, Jain K, Gupta P, Sahoo D, Parkesh R. EpiDBase: a manually curated database for small molecule modulators of epigenetic landscape. Database (Oxford). 2015;2015: pubmed publisher
    ..EpiDBase provides comprehensive resources for further design, development and refinement of small molecule modulators of epigenetic markers. ..
  44. Ravikumar K, Rastegar Mojarad M, Liu H. BELMiner: adapting a rule-based relation extraction system to extract biological expression language statements from bio-medical literature evidence sentences. Database (Oxford). 2017;2017: pubmed publisher
    ..There is a marked improvement by over 20% in the overall performance of the BELMiner's capability to extract BEL statement on the test set. The system is available as a REST-API at
  45. Yergens D, Dutton D, Fiest K. Automated Identification of National Health Survey Research Topics in the Academic Literature. Stud Health Technol Inform. 2017;235:211-215 pubmed
    ..This study investigates, through the use of automated text data mining, an approach to identify and collate the type of academic literature being published using national health ..
  46. Mo T, Liu W, Ji W, Zhao J, Chen T, Ding W, et al. Biosynthetic Insights into Linaridin Natural Products from Genome Mining and Precursor Peptide Mutagenesis. ACS Chem Biol. 2017;12:1484-1488 pubmed publisher
    ..Our results reveal valuable insights into linaridin biosynthesis and highlight the potential to explore this class of natural products by genome mining and by biosynthetic engineering studies. ..
  47. Gibbens N. Chief veterinary officer's update: Big data is our best shot at challenging extreme breeding. Vet Rec. 2017;181:133-134 pubmed publisher
    ..In his latest update for Veterinary Record, Nigel Gibbens, the UK's Chief Veterinary Officer, looks back at the past year and discusses the UK's achievements and the challenges that it faces. ..
  48. Hassan S, Farhan M, Mangayil R, Huttunen H, Aho T. Bioprocess data mining using regularized regression and random forests. BMC Syst Biol. 2013;7 Suppl 1:S5 pubmed publisher
    ..In this work, the applicability of regularized regression (Lasso) and random forests (RF) in bioprocess data mining was examined, and their performance was benchmarked against multiple linear regression...
  49. Izzo M, Mortola F, Arnulfo G, Fato M, Varesio L. A digital repository with an extensible data model for biobanking and genomic analysis management. BMC Genomics. 2014;15 Suppl 3:S3 pubmed publisher
    ..The web interface allows the operators to easily manage, query, and annotate the files, without dealing with the technicalities of the data grid. ..
  50. Rinaldi F, Clematide S, Marques H, Ellendorff T, Romacker M, Rodriguez Esteban R. OntoGene web services for biomedical text mining. BMC Bioinformatics. 2014;15 Suppl 14:S6 pubmed publisher
    ..The web services leverage a state-of-the-art platform for text mining (OntoGene) which has been tested in several community-organized evaluation challenges,with top ranked results in several of them. ..
  51. Larralde M, Lawson T, Weber R, Moreno P, Haug K, Rocca Serra P, et al. mzML2ISA & nmrML2ISA: generating enriched ISA-Tab metadata files from metabolomics XML data. Bioinformatics. 2017;33:2598-2600 pubmed publisher Documentation is available from or Supplementary data are available at Bioinformatics online. ..
  52. Milovanovic I, Busarcevic M, Trbovich A, Ivovic V, Uzelac A, Djurkovic Djakovic O. Evidence for host genetic regulation of altered lipid metabolism in experimental toxoplasmosis supported with gene data mining results. PLoS ONE. 2017;12:e0176700 pubmed publisher
    ..We propose that the observed changes in Chl metabolism are part of the host defense response. Further insight into the lipid metabolism in T. gondii infection may provide novel targets for therapeutic agents. ..
  53. Blakeman K. Finding research information on the web: how to make the most of Google and other free search tools. Sci Prog. 2013;96:61-84 pubmed
  54. Chawla N, Davis D. Bringing big data to personalized healthcare: a patient-centered framework. J Gen Intern Med. 2013;28 Suppl 3:S660-5 pubmed
    ..We present the foundations of work that takes a Big Data driven approach towards personalized healthcare, and demonstrate its applicability to patient-centered outcomes, meaningful use, and reducing re-admission rates. ..
  55. Angus D. Fusing Randomized Trials With Big Data: The Key to Self-learning Health Care Systems?. JAMA. 2015;314:767-8 pubmed publisher
  56. Zehir A, Benayed R, Shah R, Syed A, Middha S, Kim H, et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat Med. 2017;23:703-713 pubmed publisher
    ..Patients were enrolled on genomically matched clinical trials at a rate of 11%. To enable discovery of novel biomarkers and deeper investigation into rare alterations and tumor types, all results are publicly accessible. ..
  57. Luo Y, Li Z, Guo H, Cao H, Song C, Guo X, et al. Predicting congenital heart defects: A comparison of three data mining methods. PLoS ONE. 2017;12:e0177811 pubmed publisher
    ..All three models are precise enough to identify groups at high risk of CHD. They should all be considered for future investigations of other birth defects and diseases. ..
  58. Cremaschi P, Rovida S, Sacchi L, Lisa A, Calvi F, Montecucco A, et al. CorrelaGenes: a new tool for the interpretation of the human transcriptome. BMC Bioinformatics. 2014;15 Suppl 1:S6 pubmed publisher
    ..expression data available in public repositories has grown exponentially in the last years, now requiring new data mining tools to transform them in information easily accessible to biologists...
  59. Yang Z, Yu F, Lin H, Wang J. Integrating PPI datasets with the PPI data from biomedical literature for protein complex detection. BMC Med Genomics. 2014;7 Suppl 2:S3 pubmed publisher
  60. Kitakaze M. Trends in Characteristics of CVD in Asia and Japan: The Importance of Epidemiological Studies and Beyond. J Am Coll Cardiol. 2015;66:196-8 pubmed publisher
  61. Salusso A, Zlocowski N, Mayol G, Zamponi N, Rópolo A. Histone methyltransferase 1 regulates the encystation process in the parasite Giardia lamblia. FEBS J. 2017;284:2396-2409 pubmed publisher
    ..Altogether, these findings suggest that the function of HMT1 is critical for the success and timing of the encystation process, and reinforce the idea that epigenetic marks are critical for cyst formation in G. lamblia. ..
  62. Lee J, Uhlik M, Moxham C, Tomandl D, Sall D. Modern phenotypic drug discovery is a viable, neoclassic pharma strategy. J Med Chem. 2012;55:4527-38 pubmed publisher
  63. Psaty B, Breckenridge A. Mini-Sentinel and regulatory science--big data rendered fit and functional. N Engl J Med. 2014;370:2165-7 pubmed publisher
  64. Raja K, Subramani S, Natarajan J. A hybrid named entity tagger for tagging human proteins/genes. Int J Data Min Bioinform. 2014;10:315-28 pubmed
    ..47, F-score of 75.77 and outperforms most of the state-of-the-art systems. However, the recall of 71.60 still remains low and leaves much room for future improvement. ..
  65. Zheng J, Howsmon D, Zhang B, Hahn J, McGuinness D, Hendler J, et al. Entity linking for biomedical literature. BMC Med Inform Decis Mak. 2015;15 Suppl 1:S4 pubmed publisher
    ..By providing a small benchmark data set and identifying opportunities, we hope to stimulate discussions across natural language processing and bioinformatics and motivate others to develop techniques for this largely untapped domain. ..
  66. Li C, Liakata M, Rebholz Schuhmann D. Biological network extraction from scientific literature: state of the art and challenges. Brief Bioinform. 2014;15:856-77 pubmed publisher
  67. Liao V, Chen M. Efficient mining gapped sequential patterns for motifs in biological sequences. BMC Syst Biol. 2013;7 Suppl 4:S7 pubmed publisher
    ..Biological data mining yield impact in diverse biological fields, such as discovery of co-occurring biosequences, which is important ..
  68. Miller G. Data sharing in toxicology: beyond show and tell. Toxicol Sci. 2015;143:3-5 pubmed publisher
  69. Moskalev A, Shaposhnikov M, Plyusnina E, Plyusnin S, Shostal O, Aliper A, et al. Exhaustive data mining comparison of the effects of low doses of ionizing radiation, formaldehyde and dioxins. BMC Genomics. 2014;15 Suppl 12:S5 pubmed publisher
    ..They can also be useful in the development of new bio-sensing methods for detection of pollutants in the environment and combating the deleterious effects. ..
  70. Harpaz R, Odgers D, Gaskin G, DuMouchel W, Winnenburg R, Bodenreider O, et al. A time-indexed reference standard of adverse drug reactions. Sci Data. 2014;1:140043 pubmed publisher
    Undetected adverse drug reactions (ADRs) pose a major burden on the health system. Data mining methodologies designed to identify signals of novel ADRs are of deep importance for drug safety surveillance...
  71. Hakala K, Van Landeghem S, Salakoski T, Van de Peer Y, Ginter F. Application of the EVEX resource to event extraction and network construction: Shared Task entry and result analysis. BMC Bioinformatics. 2015;16 Suppl 16:S3 pubmed publisher
    ..A detailed performance and error analysis provides more insight into the relatively low recall rates. ..
  72. Liao X, Xue C, Su F. Tree-based approach for exploring marine spatial patterns with raster datasets. PLoS ONE. 2017;12:e0177438 pubmed publisher
  73. McCarthy L, Vandervalk B, Wilkinson M. SPARQL assist language-neutral query composer. BMC Bioinformatics. 2012;13 Suppl 1:S2 pubmed publisher
  74. Liu J, Pan J, Wang Y, Lin D, Shen D, Yang H, et al. Component analysis of Chinese medicine and advances in fuming-washing therapy for knee osteoarthritis via unsupervised data mining methods. J Tradit Chin Med. 2013;33:686-91 pubmed
    ..for knee osteoarthritis (KOA), and develop new fuming-washing prescriptions for KOA through unsupervised data mining methods. Chinese medicine recipes for fuming-washing therapy for KOA were collected and recorded in a database...
  75. Wu J, Guo W, Zhang X, Yang B, Zhang B. [Study on medication regularity of grand master of traditional Chinese medicine YAN Zheng-hua's Ostreae Concha-containing prescriptions based on data mining]. Zhongguo Zhong Yao Za Zhi. 2014;39:2762-6 pubmed
  76. Soldatova L, Nadis D, King R, Basu P, Haddi E, Baumlé V, et al. EXACT2: the semantics of biomedical protocols. BMC Bioinformatics. 2014;15 Suppl 14:S5 pubmed publisher
    ..It can serve as a reference model for for the translation of biomedical protocols in natural language into a semantically-defined format. ..
  77. Khare R, Good B, Leaman R, Su A, Lu Z. Crowdsourcing in biomedicine: challenges and opportunities. Brief Bioinform. 2016;17:23-32 pubmed publisher
    ..Finally, we identify important emerging trends, opportunities and remaining challenges for future crowdsourcing research in biomedicine. ..
  78. Hetrick K, van der Donk W. Ribosomally synthesized and post-translationally modified peptide natural product discovery in the genomic era. Curr Opin Chem Biol. 2017;38:36-44 pubmed publisher
    ..We also discuss a small number of the many RiPPs discovered in the years 2014-2016. ..
  79. Shi P, Wu T, Li P, Guo B, Fang G, Dong Y. Use of processed data to design an orderly logic gate to construct plasmids in GenoCAD. IET Syst Biol. 2017;11:65-68 pubmed publisher
    ..Finally, the authors compared the constructed plasmid with other successful examples in BLAST and PlasMapper software to demonstrate the rationality of the orderly logic gate. ..
  80. Marx P, Antal P, Bolgár B, Bagdy G, Deakin B, Juhasz G. Comorbidities in the diseasome are more apparent than real: What Bayesian filtering reveals about the comorbidities of depression. PLoS Comput Biol. 2017;13:e1005487 pubmed publisher
    ..The computed interactive comprehensive multimorbidity views over the diseasome are available on the web at Co=MorNet: ..
  81. Kumar D, Hassan M, Pattnaik N, Mohapatra N, Dixit M. Reduced expression of IQGAP2 and higher expression of IQGAP3 correlates with poor prognosis in cancers. PLoS ONE. 2017;12:e0186977 pubmed publisher
    ..Our in-vivo (IHC) data confirmed the in-silico findings completely. Hence, IQGAP2 and IQGAP3 have potential to be used as prognostic markers or therapeutic targets in specific cancers. ..
  82. Wu C, Arighi C, Cohen K, Hirschman L, Krallinger M, Lu Z, et al. BioCreative-2012 virtual issue. Database (Oxford). 2012;2012:bas049 pubmed publisher
  83. Butler D. When Google got flu wrong. Nature. 2013;494:155-6 pubmed publisher
  84. Kuehn B. Scientists mine web search data to identify epidemics and adverse events. JAMA. 2013;309:1883-4 pubmed publisher
  85. Wu F, Li M, Ruan J, Luo F. Systems Biology Approaches to Mining High Throughput Biological Data. Biomed Res Int. 2015;2015:504362 pubmed publisher
  86. Zeng S, Duan L, Chen B, Li P, Liu E. Chemicalome and metabolome profiling of polymethoxylated flavonoids in Citri Reticulatae Pericarpium based on an integrated strategy combining background subtraction and modified mass defect filter in a Microsoft Excel Platform. J Chromatogr A. 2017;1508:106-120 pubmed publisher integrated strategy that combined background subtraction program and modified mass defect filter (MMDF) data mining in a Microsoft Excel platform for chemicalome and metabolome profiling of the polymethoxylated flavonoids (PMFs)..
  87. Covell D. A data mining approach for identifying pathway-gene biomarkers for predicting clinical outcome: A case study of erlotinib and sorafenib. PLoS ONE. 2017;12:e0181991 pubmed publisher
    A novel data mining procedure is proposed for identifying potential pathway-gene biomarkers from preclinical drug sensitivity data for predicting clinical responses to erlotinib or sorafenib...
  88. Kapraun D, Wambaugh J, Ring C, Tornero Velez R, Setzer R. A Method for Identifying Prevalent Chemical Combinations in the U.S. Population. Environ Health Perspect. 2017;125:087017 pubmed publisher
    ..population. We demonstrated how FIM can be used in conjunction with biomonitoring data to narrow a large number of possible chemical combinations down to a smaller set of prevalent chemical combinations. ..
  89. Ljungqvist A, Jenoure P, Engebretsen L, Alonso J, Bahr R, Clough A, et al. The International Olympic Committee (IOC) Consensus Statement on periodic health evaluation of elite athletes March 2009. Br J Sports Med. 2009;43:631-43 pubmed publisher
  90. Mao Y, Van Auken K, Li D, Arighi C, McQuilton P, Hayman G, et al. Overview of the gene ontology task at BioCreative IV. Database (Oxford). 2014;2014: pubmed publisher
    .. ..
  91. Elliott J, Grimshaw J, Altman R, Bero L, Goodman S, Henry D, et al. Informatics: Make sense of health data. Nature. 2015;527:31-2 pubmed publisher
  92. Sacchi L, Dagliati A, Segagni D, Leporati P, Chiovato L, Bellazzi R. Improving risk-stratification of Diabetes complications using temporal data mining. Conf Proc IEEE Eng Med Biol Soc. 2015;2015:2131-4 pubmed publisher
    ..In this work we show how temporal data mining can be fruitfully exploited to improve risk stratification...