scholarly journals Measuring phenotype-phenotype similarity through the interactome

2018 ◽  
Vol 19 (S5) ◽  
Author(s):  
Jiajie Peng ◽  
Weiwei Hui ◽  
Xuequn Shang
Keyword(s):  
Phenotype Similarity

scholarly journals Best Paper Selection

2017 ◽  
Vol 26 (01) ◽  
pp. e13-e14
Keyword(s):  
Rare Diseases ◽  
Adverse Drug Event ◽  
Medical Data ◽  
Drug Event ◽  
Data Repository ◽  
Common Disease ◽  
Target Validation ◽  
Genetic Determinants ◽  
Safety Research ◽  
Phenotype Similarity

Banda JM, Evans L, Vanguri RS, Tatonetti NP, Ryan PB, Shah NH. A curated and standardized adverse drug event resource to accelerate drug safety research. Sci Data 2016;3:160026 https://www.nature.com/articles/sdata201626 Bauer CR, Ganslandt T, Baum B, Christoph J, Engel I, Lobe M, Mate S, Staubert S, Drepper J, Prokosch HU, Winter A, Sax U. Integrated Data Repository Toolkit (IDRT). A Suite of Programs to Facilitate Health Analytics on Heterogeneous Medical Data. Methods Inf Med 2016;55(2):125-35 https://methods.schattauer.de/en/contents/archivestandard/issue/2324/manuscript/25160.html Greene D, NIHR BioResource, Richardson S, Turro E. Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases. Am J Hum Genet 2016;98(3):490-9 https://linkinghub.elsevier.com/retrieve/pii/S0002-9297(16)00014-8 Sarntivijai S, Vasant D, Jupp S, Saunders G, Bento AP, Gonzalez D, Betts J, Hasan S, Koscielny G, Dunham I, Parkinson H, Malone J. Linking rare and common disease: mapping clinical diseasephenotypes to ontologies in therapeutic target validation. J Biomed Semantics 2016;7-8 https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-016-0051-7

scholarly journals Best Paper Selection

2017 ◽  
Vol 26 (01) ◽  
pp. 151-151
Keyword(s):  
Rare Diseases ◽  
Adverse Drug Event ◽  
Medical Data ◽  
Drug Event ◽  
Data Repository ◽  
Common Disease ◽  
Target Validation ◽  
Genetic Determinants ◽  
Safety Research ◽  
Phenotype Similarity

Banda JM, Evans L, Vanguri RS, Tatonetti NP, Ryan PB, Shah NH. A curated and standardized adverse drug event resource to accelerate drug safety research. Sci Data 2016;3:160026 https://www.nature.com/articles/sdata201626 Bauer CR, Ganslandt T, Baum B, Christoph J, Engel I, Lobe M, Mate S, Staubert S, Drepper J, Prokosch HU, Winter A, Sax U. Integrated Data Repository Toolkit (IDRT). A Suite of Programs to Facilitate Health Analytics on Heterogeneous Medical Data. Methods Inf Med 2016;55(2):125-35 https://methods.schattauer.de/en/contents/archivestandard/issue/2324/manuscript/25160.html Greene D, NIHR BioResource, Richardson S, Turro E. Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases. Am J Hum Genet 2016;98(3):490-9 https://linkinghub.elsevier.com/retrieve/pii/S0002-9297(16)00014-8 Sarntivijai S, Vasant D, Jupp S, Saunders G, Bento AP, Gonzalez D, Betts J, Hasan S, Koscielny G, Dunham I, Parkinson H, Malone J. Linking rare and common disease: mapping clinical diseasephenotypes to ontologies in therapeutic target validation. J Biomed Semantics 2016;7-8 https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-016-0051-7

Psychiatric and hostility factors related to use of anabolic steroids in monozygotic twins

2006 ◽  
Vol 21 (8) ◽  
pp. 563-569 ◽  
Author(s):  
Thomas A. Pagonis ◽  
Nikiforos V. Angelopoulos ◽  
George N. Koukoulis ◽  
Christos S. Hadjichristodoulou ◽  
Paraskevi N. Toli
Keyword(s):  
Anabolic Steroids ◽  
Monozygotic Twins ◽  
The Other ◽  
Time Interval ◽  
Psychological Consequences ◽  
Paranoid Ideation ◽  
Psychometric Instruments ◽  
Androgenic Steroids ◽  
Active Effects ◽  
Phenotype Similarity

AbstractIntroductionAnabolic androgenic steroids (AAS) are derived by chemical manipulation of the testosterone molecule. The specified category of drugs produces anabolic, androgenic and psycho-active effects including elevated aggressive, hostile, violent and anti social behavior.ObjectiveThe objective of this case report observational study was to evaluate the possible psychological consequences of AS use in the twin user of each pair, compared with the non-user twin.MethodologyWe studied two pairs of male monozygotic twins: one pair 24 years old and the other 31 years old, with absolute genome and phenotype similarity. One of the twins of each pair used AAS while the other did not. Both pairs lived in Hellenic provincial towns and followed a common training and nutrition regime. The psychometric instruments used were the Symptoms Check List-90 (SCL-90) and the Hostility and Direction of Hostility Questionnaire (HDHQ). The psychometric evaluations took place within a time interval of 6 months.ResultsThe study found high levels of aggressiveness, hostility, anxiety and paranoid ideation in the twins who used AS. The non-user twins showed no deviation from their initial status.ConclusionThe use of AAS induced several important psychiatric changes in monozygotic twins which were not present in the twin who did not use AAS.

scholarly journals DeepMiR2GO: Inferring Functions of Human MicroRNAs Using a Deep Multi-Label Classification Model

2019 ◽  
Vol 20 (23) ◽  
pp. 6046 ◽  
Author(s):  
Jiacheng Wang ◽  
Jingpu Zhang ◽  
Yideng Cai ◽  
Lei Deng
Keyword(s):  
Heterogeneous Network ◽  
Vital Role ◽  
Classification Model ◽  
Cellular Regulation ◽  
Protein Protein Interaction ◽  
Topological Features ◽  
Embedding Strategy ◽  
Go Terms ◽  
Phenotype Similarity ◽  
Better Than

MicroRNAs (miRNAs) are a highly abundant collection of functional non-coding RNAs involved in cellular regulation and various complex human diseases. Although a large number of miRNAs have been identified, most of their physiological functions remain unknown. Computational methods play a vital role in exploring the potential functions of miRNAs. Here, we present DeepMiR2GO, a tool for integrating miRNAs, proteins and diseases, to predict the gene ontology (GO) functions based on multiple deep neuro-symbolic models. DeepMiR2GO starts by integrating the miRNA co-expression network, protein-protein interaction (PPI) network, disease phenotype similarity network, and interactions or associations among them into a global heterogeneous network. Then, it employs an efficient graph embedding strategy to learn potential network representations of the global heterogeneous network as the topological features. Finally, a deep multi-label classification network based on multiple neuro-symbolic models is built and used to annotate the GO terms of miRNAs. The predicted results demonstrate that DeepMiR2GO performs significantly better than other state-of-the-art approaches in terms of precision, recall, and maximum F-measure.

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