799 research outputs found
Supplemental_Material_2 - Chinese Herbal Medicine Versus Other Interventions in the Treatment of Type 2 Diabetes: A Systematic Review of Randomized Controlled Trials
No full textSupplemental_Material_2 for Chinese Herbal Medicine Versus Other Interventions in the Treatment of Type 2 Diabetes: A Systematic Review of Randomized Controlled Trials by Ao Yu, David Adelson, and David Mills in Journal of Evidence-Based Integrative Medicine</p
Supplementary_Table - Chinese Herbal Medicine Versus Other Interventions in the Treatment of Type 2 Diabetes: A Systematic Review of Randomized Controlled Trials
No full textSupplementary_Table for Chinese Herbal Medicine Versus Other Interventions in the Treatment of Type 2 Diabetes: A Systematic Review of Randomized Controlled Trials by Ao Yu, David Adelson, and David Mills in Journal of Evidence-Based Integrative Medicine</p
337.2: The Epidemiology of Hereditary Pancreatitis in Australia and its effect on patient of Total Pancreatectomy with Islet Auto-Transplantation (TPIAT).
No full textAbstract - 337.2Denghao Wu, James Zuiani, Christopher Drogemuller, Sunita De Sousa, David Adelson, David J Torpy, Patrick Toby H Coate
Professor David Adelson
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Professor Adelson&#39;s research interests are genome evolution and architecture of mammals. Biological mechanisms underlying genome evolution are believed to originate with retrotransposon insertions that can ultimately lead to segmental (gene) duplications/deletions, incorporation of retrotransposons into protein coding genes (exaptation) or gene duplication via retro-gene formation. The resulting &quot;churning&quot; of both non-protein coding regions and protein domains are believed to be two of the major forces that drive speciation and adaptation. Current primary research aim is to understand the magnitude and rate of change associated with retrotransposon insertion.&nbsp;</p>
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This is an important research problem both in terms of our understanding of evolutionary mechanisms and processes but also because these processe frequently give rise to mutations or structural variation affecting gene regulation and function. These alterations can result in disease or alter economically important agricultural traits.</p>
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Research projects include characterizing the degree of horizontal gene transfer in vertebrates, model organism databases, data mining and determining the effect of disregulated retrotransposons on genome stability.</p>
Chromosomal assignment of six genes (EIF4G3, HSP90, RBBP6, IL8, TERT, and TERC) in four species of the genus Equus
No full textWe mapped six genes (EIF4G3, HSP90, RBBP6, IL8, TERT, and TERC) on the chromosomes of Equus caballus, Equus asinus, Equus grevyi, and Equus burchelli by fluorescence in situ hybridization. Our results add six type I markers to the cytogenetic map of these species and provide new information on the comparative genomics of the genus Equus.Pamela Vidale, Francesca M. Piras, Solomon G. Nergadze, Livia Bertoni, Andrea Verini-Supplizi, David Adelson, Gérard Guérin and Elena Giulott
Personalized medicine support system : resolving conflict in allocation to risk groups and predicting patient molecular response to targeted therapy
No full textTreatment management in cancer patients is largely based on the use of a standardized set of predictive and prognostic factors. The former are used to evaluate specific clinical interventions, and they can be useful for selecting treatments because they directly predict the response to a treatment. The latter are used to evaluate a patient’s overall outcomes, and can be used to identify the risks or recurrence of a disease. Current intelligent systems can be a solution for transferring advancements in molecular biology into practice, especially for predicting the molecular response to molecular targeted therapy and the prognosis of risk groups in cancer medicine. This framework primarily focuses on the importance of integrating domain knowledge in predictive and prognostic models for personalized treatment. Our personalized medicine support system provides the needed support in complex decisions and can be incorporated into a treatment guide for selecting molecular targeted therapies.Haneen Banjar, David Adelson, Fred Brown, and Tamara Leclerc
bíogo: a simple high-performance bioinformatics toolkit for the Go language
No full textbiogo is a framework designed to ease development and maintenance of computationally intensive bioinformatics applications (Kortschak and Adelson 2014). The library is written in the Go programming language, a garbage-collected, strictly typed compiled language with built in support for concurrent processing, and performance comparable to C and Java. It provides a variety of data types and utility functions to facilitate manipulation and analysis of large scale genomic and other biological data. biogo uses a concise and expressive syntax, lowering the barriers to entry for researchers needing to process large data sets with custom analyses while retaining computational safety and ease of code review. We believe biogo provides an excellent environment for training and research in computational biology because of its combination of strict typing, simple and expressive syntax, and high performance.R. Daniel Kortschak, Josh Bleecher Snyder, Manolis Maragkakis, and David L. Adelso
Intelligent techniques using molecular data analysis in leukaemia: an opportunity for personalized medicine support system
No full textThe use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient’s genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice.Haneen Banjar, David Adelson, Fred Brown, and Naeem Chaudhr
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