Computational biology

Novel Data Mining Methods for Virtual Screening of Biological Active Chemical Compounds by Othman Soufan

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H2 B9

Abstract Drug discovery is a process that takes many years and hundreds of millions of dollars to reveal a con dent conclusion about a specific treatment. Part of this sophisticated process is based on preliminary investigations to suggest a set of chemical compounds as candidate drugs for the treatment. Computational resources have been playing a significant role in this part through a step known as virtual screening. From a data mining perspective, the availability of rich data resources is key in training prediction models. Yet, the difficulties imposed by big expansion in data and its

Mining Genome-Scale Growth Phenotype Data through Constant-Column Biclustering

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B3 L5 R5209

Growth phenotype profiling of genome-wide gene-deletion strains overstresses conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. In this dissertation, we first demonstrate that detecting such "co-fit" gene groups can be cast as a less well-studied problem in biclustering, i.e., constant-column biclustering. Despite significant advances in biclustering techniques, very few were designed for mining in growth phenotype data.

Hierarchical Approximation Methods for Option Pricing and Stochastic Reaction Networks

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KAUST

In biochemically reactive systems with small copy numbers of one or more reactant molecules, stochastic effects dominate the dynamics. In the first part of this thesis, we design novel efficient simulation techniques, based on multilevel Monte Carlo methods and importance sampling, for a reliable and fast estimation of various statistical quantities for stochastic biological and chemical systems under the framework of Stochastic Reaction Networks (SRNs). In the second part of this thesis, we design novel numerical methods for pricing financial derivatives. Option pricing is usually challenging due to a combination of two complications: 1) The high dimensionality of the input space, and 2) The low regularity of the integrand on the input parameters. We address these challenges by using different techniques for smoothing the integrand to uncover the available regularity and improve quadrature methods' convergence behavior. We develop different ways of smoothing that depend on the characteristics of the problem at hand. Then, we approximate the resulting integrals using hierarchical quadrature methods combined with Brownian bridge construction and Richardson extrapolation.

Omics-based Metastasis Prediction using Machine Learning and Deep Learning

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KAUST

Knowing metastasis is the primary cause of cancer-related deaths incentivized research to unravel the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications. In this regard, predicting metastasis onset has also been explored using artificial intelligence (AI) approaches that are machine learning (ML), and more recently, deep learning (DL).

Drug Repositioning through the Development of Diverse Computational Methods using Machine Learning, Deep Learning, and Graph Mining

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KAUST

In this dissertation, we combined artificial intelligence and machine/deep learning with chemical and biological properties to develop several computational methods to solve biomedical domain problems, specifically drug repositioning, and demonstrated their efficiencies and capabilities. We developed three network-based DTI prediction methods using machine learning, graph embedding, and graph mining. These methods significantly improved prediction performance, and the best-performing method even reduces the error rate by more than 33% across all datasets compared to the best state-of-the-art method. As it is more insightful to predict continuous values that indicate how tightly the drug binds to a specific target, we conducted a comparison study of current regression-based methods that predict drug-target binding affinities (DTBA). Our methods demonstrated their efficiency and capability by achieving high prediction performance and identifying therapeutic targets for several cancer types. We further conducted a lung cancer case study of findings that support the novel predicted targets.

A personal path from mathematics to biomedical research

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B2 L5 R5209

A personal presentation describing the role of computational biology and bioinformatics in addressing state-of-the-art biomedical challenges. First, I will provide an overview of the transition between a Ph.D. in Mathematics to a postdoc in Computational Biology: How did it happen? What were the challenges? Secondly, I will briefly present several current case studies where computational biology (in several flavors) is core to understanding novel biological data related to multi-omic data analysis, spatial profiling, gene therapy, and more.

Amin Allam (CEMSE PhD) wins the second prize in the DREAM Challenge 1B

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Amin Allam (CEMSE PhD student in the InfoCloud group) supervised by Prof Panos Kalnis has won the second prize in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge 1B. Amin Allam utilized Bayesian inference to achieve the 2nd place.

Alumni Feature: Arturo Magana Mora

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CBRC Ph.D. graduate Arturo Magana Mora is currently a postdoctoral researcher at the prestigious National Institute of Advanced Industrial Science and Technology (AIST) in Tokyo, Japan.

Siqing Hou defends his Master’s thesis

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Siqing Hou, a Master's student under the supervision of Associate Professor Xin Gao, successfully defended his thesis.

CBRC goes to ECCB 2018

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The European Conference on Computational Biology (ECCB) is one of the most important conferences in the field of bioinformatics and computational biology. Held in a different European city annually, this year's conference will take place in the historic city of Athens, Greece on September 8-12, 2018.

Meet KAUST prospective student: Xiaopeng Xu

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Xiaopeng Xu is a computer science graduate who will join KAUST in the fall of 2020. Xu obtained a master’s degree in computer science from KAUST, and a bachelor’s degree in bioinformatics from Huazhong University of Science and Technology, respectively. He will join KAUST in the fall of 2020 as a Ph.D. candidate and a member of the KAUST Structural and Functional Bioinformatics group under the supervision of Professor Xin Gao.
KAUST-CEMSE-CS-SFB-Zhongxiao-Li

Zhongxiao Li

Ph.D. Student, Computer Science
Zhongxiao Li is a Ph.D. student under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST).​ Zhongxiao obtained his Bachelor of Science degree from Southern University of Science and Technology, China in 2018. Research Interests Zhongxiao's research interests include Bioinformatics and Computational Biology.

Yongkang Long

Ph.D. Student, Computer Science
Yongkang Long is a Ph.D. student under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests Yongkang's research interests include Bioinformatics and Computational Biology.

Xin Gao

Program Chair, Computer Science
Professor Gao is a world-leading expert in developing novel AI solutions for challenges in biology, biomedicine, and health and wellness, with a focus on AI-based drug development, large language models in biomedicine, biomedical imaging analysis, and omics-based disease detection and diagnostics.

Xiaopeng Xu

Postdoctoral Research Fellow, Computer Science
Xiaopeng Xu is a Postdoctoral Fellow in the KAUST Structural and Functional Bioinformatics group under the supervision of Professor Xin Gao. Before joining Postdoctoral Fellow program, Xiaopeng obtained Ph.D. and master’s degree in computer science from King Abdullah University of Science and Technology (KAUST), and a bachelor’s degree in bioinformatics from Huazhong University of Science and Technology, respectively. Research Interests Xiaopeng's research interests included Bioinformatics, artificial intelligence and computational biology.

Wenkai Han

Ph.D. Student, Computer Science
Wenkai Han is a Ph.D. student under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Wenkai obtained his Bachelor of Science degree from the University of Science and Technology of China in 2018. Research Interests Wenkai's research interests include Bioinformatics and Computational Biology.

Vasiliki Kordopati

Vasiliki Kordopati obtained Master degree in Computer Science under the co-supervision of Professor Boon Ooi at Photonics Laboratory and Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) from King Abdullah University of Science and Technology (KAUST). Vasiliki is a co-founder of Oæsis, a King Abdullah University of Science and Technology (KAUST) startup spinout, where she is responsible for product development. A computer engineer by training, she received a bachelor's degree in Computer Engineering from the University of Patras, Greece. Research Interest Vasiliki's

Somayah Albaradei

Ph.D. Student, Computer Science
Somayah Albaradei is a Ph.D. student at Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Somayah obtained her master's degree in computer science from the University of Manitoba, Canada in 2014. Professional Profile 2014: Lecturer at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2010- 2011: Teacher Assistant at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2009-2010: Collaborator

Siqing Hou

Siqing Hou obtained his Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Master Thesis, 2018: Neural Inductive Matrix Completion for Predicting Disease-Gene Associations Research Interests Siqing Hou was working in the fields of Bioinformatics, Computational Biology and Machine Learning. Professional Profile Research Student, University of Birmingham, Jul 2015 - Aug 2018 Software Effort Estimation, Machine Learning Education Profile M.Sc. Computer

Sheng Wang

Sheng Wang worked as a Research Scientist at Prof. Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Sheng got his Ph.D. in Bioinformatics from the Institute of Theoretical Physics at the Chinese Academy of Sciences in 2010. He was then a postdoc fellow at Toyota Technological Institute in Chicago during 2010 and 2014. During 2014 and 2017, he was a research professional at the Department of Human Genetics at the University of Chicago. Since March 2017, he joined the SFB group as a research scientist. Currently, Sheng is a

Mario Messih

Mario Messih is a Former Visiting student from the Sapienza University of Rome, Italy at Professor Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Mario Finished his Ph.D. study with Prof. Anna Tramontano at the Sapienza University of Rome, Italy, and currently, Mario is a postdoc fellow at Yale University. Research Interests Mario's research interests included Bioinformatics and computational biology. His main interests lie at the intersection of machine learning, statistics and their applications on analyzing large

Heba Abu Samra

Heba Abu Samra obtained her Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Master Thesis (2013): A Comparative Study of Feature Selection and Classification Methods for Gene Expression Data Research Interests Heba's research interests included Bioinformatics and Computational Biology.

Haoyang Li

Ph.D. Student, Computer Science
Haoyang Li is a Ph.D. candidate in the KAUST Structural and Functional Bioinformatics research group under the supervision of Professor Xin Gao. Before joining KAUST, Haoyang majored in Bioinformatics and graduated from Jilin University (JLU), located in Changchun, Jilin, China. Research Interests Haoyang has a broad interest in developing AI models to address biological and healthcare problems. Recently, his research has focused on developing AI models for spatial biology, including spatial multi-omics, spatiotemporal omics, and pathological imaging, aimed at translational and clinical

Chiheb Ben Hammouda

Chiheb Ben Hammouda is a Ph.D. candidate at Stochastic Numerics Research Group (STOCHNUM) under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Prior to joining Ph.D., Chiheb obtained a Master's degree in Applied Mathematics and Computational Sciences from KAUST, and a Bachelor's degree in multidisciplinary engineering from Ecole Polytechnique de Tunisie, Tunisia. Research Interests Chiheb's research interests include Stochastic modeling, Numerical simulation and analysis, Quantitative finance in particular option pricing, Stochastic

Amin Allam

Amin Allam obtained his Ph.D. in Computer Science at the King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Panagiotis Kalnis in 2017. Amin is a lecturer in the Computer Science department, Faculty of Computers and Information, Cairo University, Egypt. He got his bachelor's degree in Computers and Information in 2003 from Cairo University. Then, he got a Master's degree titled "An Arabic-Disambiguating Engine Based on Statistical Approaches" in Computer Science from the same university in 2008. Research Interest Amin's research interests include