Scientific Program

Conference Series Ltd invites all the participants across the globe to attend 6th International Conference on Biostatistics and Bioinformatics Atlanta, Georgia, USA.

Day 2 :

Keynote Forum

Mikhail Moshkov

King Abdullah University of Science and Technology, Saudi Arabia

Keynote: Multi-stage optimization of decision trees: two applications
OMICS International Biostatistics 2017 International Conference Keynote Speaker Mikhail Moshkov photo
Biography:

Mikhail Moshkov is Professor in the CEMSE Division at King Abdullah University of Science and Technology, Saudi Arabia since October 1, 2008. He has earned his Master’s degree from Nizhni Novgorod State University, received his Doctorate from Saratov State University, and habilitation from Moscow State University. From 1977 to 2004, He was with Nizhni Novgorod State University. Since 2003, he has worked in Poland in the Institute of Computer Science, University of Silesia, and since 2006, also in the Katowice Institute of Information Technologies. His main areas of research are complexity of algorithms, combinatorial optimization, and data mining. He is the author or coauthor of five research monographs published by Springer

Abstract:

Multi-stage optimization of decision trees is one of the extensions of dynamic programming. It allows us to optimize decision trees sequentially relative to a number of cost functions. We will discuss two applications of this technique: finding of minimum average depth of a decision tree for sorting eight elements and creation of an algorithm for reduct minimization. The question about minimum average depth of a decision tree for sorting of eight elements was open since 1968 and was considered by D Knuth in his famous book The Art of Computer Programming, Volume 3, Sorting and Searching. Reduct is a minimal set of conditional attributes in a decision table which gives the same information about decision attribute as the whole set of conditional attributes. The problem of reduct minimization is closely connected with the feature selection. The end of the presentation is devoted to the introduction to KAUST

OMICS International Biostatistics 2017 International Conference Keynote Speaker Herman Ray  photo
Biography:

Herman Ray has received his PhD from the University of Louisville, where he has conducted research at the JG Brown Cancer Center. He currently has several manuscripts published in clinical trial design and bioinformatics as well as STEM education policy in the secondary education system. He is now an Associate Professor of Statistics at Kennesaw State University as well as the Director of the Center for Statistics and Analytical Research which is housed within the new Analytics and Data Science Institute

Abstract:

The single arm, two-stage clinical trial design is a popular methodology to evaluate oncology treatments in the phase II setting. The designs are typically augmented with an ad hoc toxicity monitoring rule which is imposed outside of the formal two-stage design but there are also several designs that formally incorporate both endpoints simultaneously. There are many problems that prevent the designs from being used in practice which includes point estimation after the execution of the study. We will examine an unbiased estimator that accounts for both endpoints simultaneously along with the correlation between the endpoints. The behavior of the estimate is examined through simulation studies. It is compared to the maximum likelihood estimator

  • Workshop

Session Introduction

Abdel-Salam Gomaa

Qatar University, Qatar

Title: Parametric regression analysis for Biostatisticians
Biography:

Abdel-Salam Gomaa holds BS and MS (2004) degrees in Statistics from Cairo University and MS (2006) and PhD (2009) degrees in Statistics from Virginia Polytechnic Institute and State University (Virginia Tech, USA). Prior to joining Qatar University as an Assistant Professor and a Coordinator of the Statistical Consulting Unit and Coordinator for the Statistics Programs, he taught at Faculty of Economics and Political Science (Cairo University), Virginia Tech, and Oklahoma State University. Also, he worked at JPMorgan Chase Co. as Assistant Vice President in Mortgage Banking and Business Banking Risk Management Sectors. He has published several research papers and delivered numerous talks and workshops. He has awarded couples of the highest prestige awards such as Teaching Excellence from Virginia Tech, Academic Excellence Award, Freud International Award, and Mary G Natrella Scholarship from American Statistical Association (ASA) and American Society for Quality (ASQ), for outstanding graduate study of the theory and application of Quality Control, Quality Assurance, Quality Improvement, and Total Quality Management. He is a Member of the ASQ and ASA

Abstract:

This short course is designed to provide some of the advanced and common statistical techniques for researches and practitioners. The short course will demonstrate the parametric simple and multiple linear regression models, Logistic Regression, Poisson Regression, ZIP Poisson Regression models and their applications in Biostatistics, Medical, Biology, Business, Mass Communication, Engineering, public health, Epidemiology, pharmaceutical and biomedical fields. These examples and case-studies will be implemented using the SPSS and SAS.  Attendees will learn concepts, techniques and some secrets about the followings: How to use the F-test to determine if your predictor variables have a statistically significant relationship with your outcome/response variable? What are the assumptions for linear regression analysis and what you should do to meet these assumptions? Why adjusted R2 is smaller than R2 and what these numbers mean when comparing between several models? For which predictors are the regression coefficients significantly different than zero and how can you select the significant variables? What is the difference between regression and ANOVA and when are they equivalent? When and how to use the Generalized Linear Models with some examples? How to select the best model? What is the general strategy to approach any modeling problem? How to report the results in your research paper/report?

  • Poster Presentations
Speaker
Biography:

Estella Chen-Quin is an Associate Professor in the Department of Molecular and Cellular Biology at Kennesaw State University. She investigates the cancer genetics of mitochondrial lesions during tumor formation. She has completed her PhD at Yale University and Postdoctoral studies at Emory University

Abstract:

A link between tumorigenesis and mutations in the mitochondrial genes of the electron transport system (ETS) has long been posited, due to the high prevalence of mitochondrial mutations in all tumors. However, whether mitochondrial mutations play a causal role in cancer is unknown. Here, we analyze somatic mutations of ETS complex II (Succinate Dehydrogenase; SDH). People with inherited (germline) mutations in SDH have a known cancer predisposition; however, the path from inherited predisposition to cancer-causing lesions is not understood. Mutational lesions in the ETS disrupt the normal flow of electrons, increasing levels of reactive oxygen species (ROS) and creating a mutagenic source. Increased levels of ROS stabilize the mitogenic factor HIF-1. To identify and visualize which SDH protein domains are selected for somatic missense mutations during tumorigenesis, we created the 3D protein heatmap to identify potential cancer driver domains. A structural analysis via homology modeling allows us to highlight cancer-associated mutations on the ETS protein structure. This may identify areas of the mitochondrial proteins that promote cancer when altered and inform on the cellular mechanisms involved. Mutations are scored for their predicted protein effect using Polyphen2; and potential driver domains are identified by their mean Polyphen2 score. Cancer data is taken from the Cancer Genome Atlas; control data is from the 1000 Genomes Project. We use Pymol and the porcine crystal protein structure to create the 3D protein heatmaps, providing comparative analysis between the control and cancer set

  • Clinical Biostatistics | Statistical Methods | Regression Analysis | Systems Biology in Bioinformatics | Biostatistics applications | Biometric security | Big Data Analytics | Modern data analysis
Speaker

Chair

Yedidi Narasimha Murty

BI Solutions Architect, USA

Speaker

Co-Chair

Meenakshi Nadimpalli

Reliable Software Resources Inc., USA

Session Introduction

Anam Riaz

National College of Business Administration and Economics, Pakistan

Title: Exponential behavior of health indicators of Pakistan
Speaker
Biography:

Anam Riaz is PhD Research Scholar in the discipline of Statistics in National College of Business Administration and Economics Lahore, Pakistan. Higher Education Commission (HEC) of Pakistan awarded her scholarship for PhD. She has published 02 research papers in journals and many articles were presented in national and international conferences

Abstract:

The study is being to describe the relationship of Basic Medical Staff (BMS) and Life Expectancy (LE) of Pakistan. Another relationship has been developed between Infrastructure (INF) of health sector and Life Expectancy. A bivariate exponential distribution has been developed using the hazard rate and found the correlation coefficients of the BMS and LE and INF and LE. The properties and different characteristics have been derived for new bivariate exponential distribution. Empirical study of new bivariate distribution has been found using the Crude Death Rate, Infant Mortality Rate, Life Expectancy, Medical Staff and Infrastructure of Pakistan

Biography:

Gbenga J Abiodun is a young Scientist whose research interest focuses on biomathematics, epidemiology and mathematical modelling of the impacts of climate (variability and change) on vector-borne and infectious diseases. He has completed his Masters and Doctoral degrees in Mathematics at the University of the Western Cape (UWC) in 2012 and 2016, respectively. He has worked extensively on infectious diseases and published peer-reviewed papers in high-profile international journals

Abstract:

The reasons for malaria resurgence mostly in Africa are yet to be well understood. Although the causes are often linked to regional climate change, it is important to understand the impact of climate variability on the dynamics of the disease. However, this is difficult due to the unavailability of long-term malaria data over the study areas. In this study, we develop a climate-based mosquito-human malaria model to study malaria dynamics in the human population over KwaZulu-Natal, one of the epidemic provinces in South Africa, from 1970-2005. We compare the model output with available observed monthly malaria cases over the province from September 1999 to December 2003. We further use the model outputs to explore the relationship between the climate variables (rainfall and temperature) and malaria incidence over the province using principal component analysis, wavelet power spectrum and wavelet coherence analysis. The model produces a reasonable fit with the observed data and it captures all the spikes in malaria prevalence. Our results highlight the importance of climate factors on malaria transmission and show the seasonality of malaria epidemics over the province. Results from the principal component analyzes further suggest that, there are two principal factors associated with climates variables and the model outputs. One of the factors indicate high loadings on Susceptible, Exposed and Infected human, while the other is more correlated with Susceptible and Recovered humans. However, both factors reveal the inverse correlation between Susceptible-Infected and Susceptible-Recovered humans respectively. Through the spectrum analysis, we notice a strong annual cycle of malaria incidence over the province and ascertain a dominant of one-year periodicity. Consequently, our findings indicate that an average of 0 to 120-day lag is generally noted over the study period, but the 120-day lag is more associated with temperature than rainfall. This is consistence with other results obtained from our analyses that malaria transmission is more tightly coupled with temperature than with rainfall in KwaZulu-Natal province

Biography:

Revathy Duvedi has been in the Pharma/Biotech industry for more than 26 years and have been Icon for more than 10 years. She has been focusing primarily on Oncology therapeutic area for more than 10 years

Abstract:

The purpose of the paper is to share methods and nuances of how to analyze clinical trial data in oncology. The following will be shared: Kaplan-Meier analyses, handling of missing data, competing risks related to overall survival, issues related to proportional hazards model assumption violations, interval censoring and recurrence event analyses. SAS codes and Macros will also be shared. Typically these topics are spread out in various papers. We will provide a consolidated paper documenting all the issues and methods. The intention is for the paper to be used as a roadmap or blueprint on how to approach analyses in oncology trials covering a variety of indications

Biography:

Mohamad S Hasan is a PhD candidate in Statistics at the University of Georgia. He has completed his Masters in Statistical Computing from the University of Central Florida. He intends to start his job career as an Assistant Professor in Statistics, Biostatistics, and Bioinformatics

Abstract:

The large scale multiple testing inherent to high throughput biological data necessitates very high statistical stringency and thus true effects in data are difficult to detect unless they have high effect sizes. One solution to this problem is to use independent information to prioritize the most promising features of the data and thus increase the power to detect them. Weighted p-values provide a general framework for doing this in a statistically rigorous fashion. However, calculating weights that incorporate the independent information and optimize statistical power remains a challenging problem despite recent advances in this area. Existing methods tend to perform poorly in the common situation that true positive features are rare and of low effect size. We introduce Covariate Rank Weighting a method for calculating approximate optimal weights conditioned on the ranking of tests by an external covariate. This approach uses the probabilistic relationship between covariate ranking and test effect size to calculate more informative weights that are not diluted by null effects as is common with group-based methods. This relationship can be calculated theoretically for normally distributed covariates or estimated empirically in other cases. We show via simulations and applications to data that this method outperforms existing methods by a large margin in the rare/low effect size scenario and has at least comparable performance in all scenarios

  • E-Poster
Biography:

In this paper, a sequential algorithm of graph nodes classification and their partial order definition are applying to protein network using for a study of the key players required for connecting ABA signaling and ABA-mediated drought and thermo tolerance. Suggested classification procedure allows finding in the network using for research of plants stability to drought and extreme temperatures proteins, the most important clusters for thermo stability and impacts to provide them conditions that are more convenient. It is possible to allocate output proteins DREB2C, ABA receptors PYLs, which are the most important for thermo stability of plants by their biochemical characteristics. In the network, there are only two multi node clusters and only one of them has edges connected with allocated output proteins. Details of this multi node cluster with allocated output proteins are analyzing

Abstract:

G Sh Tsitsiashvili is is professor of the chair of Algebra, Geometry and Analysis at Far Eastern Federal University. 68 years since the birth of Tsitsiashvili Gurami Shalvovich (19 December, 1948), doctor of physical-mathematical sciences, Professor, main scientific researcher of Institute for Applied Mathematics Far Eastern Branch of RAS