Day 1 :
- Genomics & Bioinformatics
Session Introduction
Sergey Suchkov
Moscow Engineering Physical Institute, Russia
Title: Personalized & Precision Medicine (PPM) as a model of Healthcare Services of the next-step generation to secure the National Stability and The Biosafety
Biography:
Sergey Suchkov was born in the City of Astrakhan, Russia, in a dynasty of medical doctors, graduated from Astrakhan State Medical University and was awarded with MD. Then maintained his PhD and Doctor’s Degree. And later was working for Helmholtz Eye Research Institute and Moscow Regional Clinical Research Institute (MONIKI). Dr Suchkov was a Secretary-in-Chief of the Editorial Board, Biomedical Science, an international journal published jointly by the USSR Academy of Sciences and the Royal Society of Chemistry, UK.
Abstract:
A new systems approach to diseased states and wellness result in a new branch in the healthcare services, namely, personalized medicine (PM). To achieve the implementation of PM concept into the daily practice including clinical cardiology, it is necessary to create a fundamentally new strategy based upon the subclinical recognition of bioindicators (biopredictors and biomarkers) of hidden abnormalities long before the disease clinically manifests itself.
Each decision-maker values the impact of their decision to use PM on their own budget and well-being, which may not necessarily be optimal for society as a whole. It would be extremely useful to integrate data harvesting from different databanks for applications such as prediction and personalization of further treatment to thus provide more tailored measures for the patients and persons-at-risk resulting in improved outcomes whilst securing the healthy state and wellness, reduced adverse events, and more cost effective use of health care resources. One of the most advanced areas in cardiology is atherosclerosis, cardiovascular and coronary disorders as well as in yocarditis. A lack of medical guidelines has been identified by the majority of responders as the predominant barrier for adoption, indicating a need for the development of best practices and guidelines to support the implementation of PM into the daily practice of cardiologists!
Implementation of PM requires a lot before the current model “physician-patient” could be gradually displaced by a new model “medical advisor-healthy person-at-risk”. This is the reason for developing global scientific, clinical, social, and educational projects in the area of PM to elicit the content of The new branch.
Laszlo Takacs
University of Debrecen, Hungary
Title: Future of blood plasma profiling for medical diagnostics -a review of current trends
Biography:
Laszlo Takacs is the CEO/CSO for Biosystems International. Leading the management team, his responsibilities include formulating and implementing the science based business strategy of Biosystems International. He has funded Biosystems International in France and Hungary. Prior to Biosystems, he worked at Pfizer and Amgen in various management roles in biotech and drug R&D and translational medicine. Before his industrial experience, he was the Head of the Special Unit of NIAAA, National Institute of Health. He continues his academic activities by teaching Medical Genome Biology at the University of Debrecen in Hungary.
Abstract:
Emerging research data indicates that circulating cell free nucleic acids, profiling of proteins and its variants and metabolites all carry significant diagnostics value. Here, current trends and the potentials in the integration of multiplatform data will be presented. As a specific example, protein epitope profiling technology with QuantiPlasmaTM biochips developed together with Randox Ltd. will be described. Variability of the proteome impacts epitopes, thus studying epitome dynamic is expected to open new avenues in protein biomarker research. To illustrate the value results of asymptomatic lung cancer detection will be shown. Integrated with imaging and established cancer biomarkers an epitomic blood test will be highly accurate in detecting asymptomatic lung cancer and reduce the number of imaging tests (Spiral CT) and unnecessary biopsies with 50%.
Jing Doo Wang
Asia University, Taiwan
Title: Reducing the gap between phenotypes and genotypes via comparing tagged whole genomic sequences
Biography:
Jing Doo Wang obtained his BS Degree in Computer Science and Information Engineering from the Tatung Institute of Technology, Taiwan (1989); MS and PhD Degree in Computer Science and Information Engineering from the National Chung Cheng University in 1993 and 2002 respectively. He has been associated with Asia University since 2003, where he is currently an Associate Professor in the Department of Computer Science and Information Engineering, and also holds a joint appointment with the Department of Bioinformatics and Medical Engineering. His research interests lies in the areas of bioinformatics, text mining for trend analysis, class ambiguity analysis and cloud computing.
Abstract:
With the progress of next generation sequences (NGS) nowadays, it is possible to have whole (complete) genomic sequences of instances of distinct organisms available. It is interesting and attractive to partition selected instances of organisms into classes according to features (phenotypes) defined or observed by domain experts precisely and then to extract and identify some distinctive genomic subsequences as biomarkers by comparing their whole genomic sequences. To overcome the computational bottleneck of whole genomic sequences comparison across those instances of organisms, a scalable approach based on previous work is applied to extract the maximal repeats from these tagged whole genomic sequences and meanwhile compute class frequency distributions of these maximal repeats extracted. These repeats with extremely biased class frequency distribution or just appearing in all instance of one class, if existing, may provide valuable hints or clues for biologists to further analyze or inspect whether the relationship between defined features (phenotypes) and extracted repeats (genotypes) is significant or not. Most of all, above computation of maximal repeat extraction could be achieved via cloud computing that can provide scalable computing environment if necessary. The method as described above opens a novel direction of researches to explore the connections between phenotypes and genotypes by comparing tagged whole genomic sequences.
Blake A. Miller
Ross University School of Veterinary Medicine, Saint Kitts and Nevis
Title: Proteomic characterization of dairy cattle plasma: establishment of biomarkers for endometritis
Biography:
Blake A. Miller is completing his PhD from Ross University School of Veterinary Medicine in St. Kitts. His background is on postgenomics technologies and their applications in diagnostics and selection indices.
Abstract:
Early diagnosis of endometritis following calving in dairy cattle is difficult because subclinical endometritis is poorly described. The goal of this study is to utilize a gel-free mass-spectrometry based proteomics approach to compare plasma proteome of dairy cattle with endometritis to those without. Blood plasma was collected from a commercial herd seven days postpartum (N=20, 10 with endometritis, 10 without). The plasma was then subjected to an acetone protein extraction. The protein pellet was analysed using mass spectrometry to identify and quantify all proteins present. Differential abundance of proteins between treatment groups was determined using both fold change (�?�1.5 increase OR �?�.75 decrease) and a statistical ANOVA test (P<0.05). 181 non-redundant proteins were quantified with a total of 33 differentially abundant proteins identified between one or more comparisons. The tool ReviGO was used to summarize gene ontology terms associated with differentially abundant proteins. Proteins that met the statistical requirements for differential abundance are extensively involved in immune function. Gene ontology for differentially abundant proteins includes associations with innate immune recognition processes, acute phase responses and immune regulation. These differentially abundant proteins provide physiological information on endometritis. The most promising single potential marker identified here is the �??uncharacterized protein G5E513,�?� a protein only previously defined by RNA-transcripts. This poorly described protein is abundant in cows with endometritis, and maybe a potential biomarker of the disease.