5^th International Conference on Biometrics & Biostatistics October 20-21,2016 Houston,Texas,USA

Biography:

Upendra Kumar Devisetty earned his PhD from University of Nottingham, UK and completed Post-doctoral studies at the University of California Davis and Oregon State University. He is currently working as Science Informatician at CyVerse, a life sciences cyberinfrastructure funded by the National Science Foundation (NSF). He has published more than 10 papers in peer-reviewed journals and has been invited to speak at several international conferences

Abstract:

CyVerse (formerly iPlant Collaborative) is a life sciences cyberinfrastructure funded by the National Science Foundation (NSF). The infrastructure’s purpose is to scale science, domain expertise, and knowledge by providing a variety of computational tools, services, and platforms for storing, sharing, and analyzing large and diverse biological datasets. The Discovery Environment (DE) in CyVerse provides a modern web interface for running powerful computing, data, and analysis applications. By providing a consistent user interface for accessing tools and computing resources needed for specialized scientific analyses, the DE facilitates data exploration and scientific discovery. DE merges the “science gateway” functionality and the bioinformatics “work bench” with high-performance data management to allow seamless access to reusable computational workflows that can run at very large scales. It is common in bioinformatics to build new analysis methods utilizing multiple programs, libraries, and modules. However, each analysis that uses these tools requires specific versions of the operating system and underlying software. Docker is a container virtualization technology that wraps software of interest (e.g., a bioinformatics tool) together with all its software dependencies so it can run in a reproducible manner regardless of the environment. CyVerse has adopted Docker for integrating software apps that run in the DE’s Compute Cluster. The user creates a Dockerfile, which is sent to CyVerse and used to build the Docker image containing the tool. After the image has been deployed on the DE’s compute cluster, the user can build an web app in the DE to enable other researches easily use the tool

Biography:

Azam Yazdani has completed her PhD in Statistical Causal Inference from Cambridge University, England, and Friedrich Schiller Jena University, Germany. She introduced the granularity directed acyclic graph (GADG) algorithm which is recognized at the 2015 atlantic causal inference conference and won “The Thomas R. Ten Have Award”. The GDAG algorithm integrates different biological levels of granularity in large scales to identify causal networks. She has carried out first-rate studies on robust statistical structures. Her research work aims to tackle the problems of finding principals which govern the mechanism of disease to provide a better understanding of the disease mechanisms applying her expertise on data integration in large scales and generating causal relationships.

Abstract:

Background: Analyses of individual metabolites does not provide information about likely targets of intervention because of co-linearity among the metabolites. One approach to directly incorporate relationships among the metabolome is to consider networks.

 

Method: Powerful and advanced analytic strategies are required to identify pathways from genome and environmental exposures to disease endpoints using comprehensive data from different biological granularities. To address the emerging challenge of relating information among different biological granularities, a granularity directed acyclic graph (GDAG) algorithm has been introduced, validated and applied to create strong instrumental variables from the genome granularity (Mendelian principle) to identify robust causal/Bayesian networks over traits. Analyzing biological causal networks provides important insights into the hierarchical regulation of physiology and metabolism.

 

Results: For the application, information from 1,034,945 genetic variants distributed across the genome was extracted and then employed by the GDAG algorithm to identify a metabolomics causal network among 122 serum metabolites. Five modules, largely corresponding to functional categories (e.g. amino acids), were identified over the network and module boundaries were determined using directionality and causal effect sizes. Based on causal network parameters, individual metabolites were identified as hypothesized targets for intervention and prediction.

 

Conclusion: Given the metabolomics causal network, metabolites with direct effect on risk factors (e.g. triglyceride) were identified which improved the understanding of the role of metabolites in quantitative risk factor phenotypes. Finally, pathways from the genome to risk factors via metabolites were determined to reveal underlying biological networks. Future steps will connect these results to cardiovascular disease end points.

Biography:

Dr. Wang got his Ph.D degree in genome science and technology at the University of Tennessee in 2011, with the thesis on the TGF-beta function in neuron development.  After graduate, he moved on and switched to bioinformatics and computational biology. Currently he is working as a postdoc at MD Anderson Cancer Center, University of Texas. His main research interest focuses on data mining, machine learning, especially with their integration and application in cancer genomic and precision medicine.  Dr. Wang has published 16 scientific papers in international well-recognized journals. He has been ACM  SIG and ISMB members and also served as journal reviewer for many top-tier journals in the field of bioinformatics and systems biology

Abstract:

Accurate prediction of the functional effects of genetic variation in cancer is critical for realizing the promise of precision medicine. Due to a lack of statistically rigorous approaches and training data, differentiating driver mutations from passenger mutations remains a major challenge in cancer research. We develope a novel Bayesian method, xDriver that combines mutations and their sequence-derived functional features (such as GERP scores) with gene expression in a population of tumor samples to identify mutations that significantly alter gene expression landscapes. We demonstrate using 752 breast cancer samples in the cancer genome atlas that our integrative approach is able to significantly improve the accuracy of driver mutation identification over existing approaches that do not perform such integration. In particular, our approach is able to enhance the functional prioritization of so-called “tail” (rare) mutations and more accurately delineate cancer subtype specific mutations (such as PIK3CA mutants associated with lymph node negative patients). Importantly, scores generated by our model achieve the best agreement with in vitro functional cell viability data obtained from transfected Ba/F3 and MCF10A cell-lines, compared to predictions from other commonly used algorithms. Our results exemplify the importance of integrating gene expression in predicting candidate driver mutations. This integrative study has the potential to impact functional genomic experiments and is expected to link cancer genomic event to precision medicine

Biography:

Wei Zhong, done Ph.D University of Cincinnati College of Medicine, Sr Biostatistician, Project Director at ICON Clinical Research, San Francisco Bay AreaPharmaceuticals

Abstract:

Missing data is a concern in most clinical trials, especially in those with a longitudinal structure. Sensitivity analyses are often used to assess the robustness of statistical inference made under conditions of missing data. There has been an increasing trend to use pattern mixture models (PMMs) to impute missing data in sensitivity analyses. The key to proper implementation of PMMs is to make valid assumptions about the link between observed values and missing values. By applying different assumptions to the data from a phase II trial, we have demonstrated that conclusions drawn from such sensitivity analysis fluctuate depending on what assumptions underlie the missing data imputation. Therefore, PMMs should be used with caution and the plausibility of model assumptions should be evaluated first by the clinical study team, taking into consideration past experience with the study drug and the patient population

Biography:

Murat Yazici has completed his BSc in Statistics and MSc in Quantitative Methods. During his Statistics and Quantitative Methods educations, he studied Bayesian Statistical Estimation as a thesis-project and Fuzzy Robust Regression Analysis as a master thesis. He has several scientific papers and a book chapter study about big data analytics. He has attended to several conferences to present his studies. He works as Senior Data Scientist & Researcher at JForce Information Technologies Inc. which is one of the solutions partners of IBM in Istanbul. His research areas include “analytics studies in many areas such as health, telecommunication, security, banking, insurance, transportation and logistics sectors”.

Abstract:

In statistics, a point process is a set of independent points in time, or geographical or general spaces. It is also a type of random process. The streak of lightning can be an example as a point process in both time and geographical space. The statistical disease mapping and location detection of radio stations in a telecommunication network is given as another example. Also, point processes are powerful instruments in statistics for modeling spatial data which is of interest in various disciplines such as plant ecology, epidemiology, geography, seismology, materials science, telecommunications, economics, astronomy and others. Spatial point processes are a type of stochastic process, each of whose realizations consists of a finite or countably infinite set of points in the plane. They are useful in the analysis of observed patterns of points; where the points represent the locations of some object of study e.g. disease cases, or petty crimes. Furthermore, spatial point processes occur in various areas in biology and ecology, e.g. in connection with the spreading of insect larvae, the distribution of trees in woodlands, the distribution of bird's nests, or experiments with revitalization of eroded areas in the Wadden sea. The geographical epidemiology is interested in the examination and definition of disease. It is also concerned with geographic variations and usually demographic, behavioral, environmental, socioeconomic, and genetic information etc. Some of types of studies of the geographical epidemiology are disease mapping, geographic correlation studies, and disease clustering. The spatial point process in geographical epidemiology is concerned with spatial analysis of geographical epidemiology by using point process.

Biography:

Tapti Sengupta is an Assistant Professor and Coordinator in the Department of Microbiology at West Bengal State University. She started her career as a Fisheries professional focusing on aquatic animal diseases and environmental modelling aspects but currently she is pursuing a research on Mycobacteriosis both in human and animals. She is involved in a number of projects with central and state government funding especially from Department of Biotechnology and ICAR. She is guiding a bunch of potential research scholars and inspires fellow and supervising numerous summer projects along with her departmental and administrative work

Abstract:

Nontuberculous mycobacteria (NTM) are ubiquitous in the environment and are responsible for several ulcerative diseases in human and/or animals, also known as mycobacteriosis. These granulomatous ulcerations caused by NTM infections have been increasing in number over the past decades, especially in immunocompromised and HIV/AIDS patients and in domestic and free living animals. The aim of the study was to ascertain the spectrum of nontuberculous mycobacteria within mycobacterial isolates not belonging to M. tuberculosis and M. avium complexes but phylogenetically related to them. Fishermen and fish handlers were examined for ulcerative lesions typical for mycobacterial infections for consecutive years, 2015 and 2016, from various districts of West Bengal viz. A- Jalpaiguri, B- Murshidabad, C- North 24 Parganas and D- Kolkata, India. Isolated cultures were analyzed within the framework of ecological studies, carried out in fish models. A total of four mycobacterial species were identified based on bio typing and molecular analysis. 306 human samples (swab and pus from the sight of the ulceration, sputum) and 423 environmental samples viz. water and soil relating to the fish handlers, were pooled and screened for two years. 214 human samples were positive for M. fortuitum and M. kansasii whereas 274 environmental samples were positive for M. kansasii and M. smegmatis. M. chelonae was ubiquitous in nature. M. fortuitum showed significant positive correlation with the lower temperature suggesting the role of environmental factors in the rate and degree of infections by the pathogens. M. kansasii and M. smegmatis were found to be opportunistic and presented themselves in primary as well as secondary infections, and in significant levels (p≤0.01) in human samples having direct contact with the contaminated water. Degree of infection showed positive correlation with districts situated in the north of West Bengal. Temperature ranging from 15^o-25^oC. Significant chi square (χ²) value (78.20) was observed at p<0.001 signifying that the degree of infection varied with temperature, with lower temperatures showing greater NTM occurrence visibly in the cooler districts. The present study signifies the prevalence of NTMs in West Bengal which was not reported earlier. The rise in the number and variety of NTMs especially of groups related to M. tuberculosis complex raises questions about drug regimens and undiagnosed symptoms. 70.02% of pathogenic mycobacteria reported in this study, from various districts of West Bengal, emphasize the need for research of these neglected and opportunistic pathogens particularly using multivariate analysis to identify causative factors