bioinformatics

Contributions to In Silico Genome Annotation

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B3 L5 R5209

Abstract Genome annotation is an important topic since it provides information for the foundation of downstream genomic and biological research. It is considered as a way of summarizing part of existing knowledge about the genomic characteristics of an organism. Annotating different regions of a genome sequence is known as structural annotation while identifying functions of these regions are considered as a functional annotation. In silico approaches can facilitate both tasks that otherwise would be difficult and time-consuming. This study contributes to genome annotation by introducing

Computational Methods for ChIP-seq Data Analysis and Applications

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B3 L5 5209

Abstract The development of Chromatin immunoprecipitation followed by sequencing (ChIP-seq) technology has enabled the construction of genome-wide maps of protein-DNA interaction. Such maps provide information about transcriptional regulation at the epigenetic level (histone modifications and histone variants) and at the level of transcription factor (TF) activity. This dissertation presents novel computational methods for ChIP-seq data analysis and applications. The work of this dissertation addresses four main challenges. First, I address the problem of detecting histone modifications from

Novel Computational Methods to Predict Drug–target Interactions Using Graph Mining and Machine Learning Approaches

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B3 L5 R5220

Abstract Computational drug repurposing aims at finding new medical uses for existing drugs. The identification of novel drug-target interactions (DTIs) can be a useful part of such a task. Finding computationally DTIs is a convenient strategy to identify potentially new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer a high false positive prediction rate. Here, we present a comprehensive review of the recent progress in the field of DTI prediction from data-centric and algorithmic-centric perspectives that can help in constructing novel reliable

Genetic Algorithms for Optimization of Machine-learning Models and their Applications in Bioinformatics

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B3 L5 R5209

Abstract ​​​Machine-learning (ML) techniques have been widely applied to solve different problems in biology. However, biological data are large and complex, which often results in extremely intricate ML models. Frequently, these models may have poor performance or may be computationally unfeasible. This study presents a set of novel computational methods and focuses on the application of genetic algorithms (GAs) for the simplification and optimization of ML models and their applications to biological problems. The dissertation addresses the following three challenges. The first challenge is

Novel Computational Methods that Facilitate Development of Cyanofactories for Free Fatty Acid Production by Olaa Motwalli

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B3 L5 R5209

Abstract Finding a source from which high-energy-density biofuels can be derived at an industrial scale has become an urgent challenge for renewable energy production. Some microorganisms can produce free fatty acids (FFA) as precursors towards such high-energy-density biofuels. In particular, photosynthetic cyanobacteria are capable of directly converting carbon dioxide into FFA. However, current engineered strains need several rounds of engineering to reach the level of FFA production for it to be commercially viable. Thus, new chassis strains that require less engineering are needed

AI4GH Seminar Series - Machine Learning with Biological Knowledge Graphs

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B2 R5220

Abstract Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval, and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. We develop novel methods for feature learning on biological knowledge graphs and apply them to the prediction of edges in the knowledge graph representing problems of finding candidate genes of diseases, or drugs

Neural Inductive Matrix Factorization for Predicting Disease-Gene Associations

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B3 R5208

In silico prioritization of undiscovered associations can help find causal genes of newly discovered diseases. Some existing methods are based on known associations and side information of diseases and genes. We exploit the possibility of using a neural network model, Neural Inductive Matrix Completion (NIMC) in disease-gene prediction.

AI4GH Seminar Series| Artificial Intelligence for Genomics and Health: Introduction and Overview

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B2 R5220

Abstract Genomics and health are prime areas for the development and application of novel intelligent algorithms due to a large amount of available research data, the high complexity of the problem, and the significant social and economic benefits of improving health outcomes in humans. In the past, genomics and health have been one of the key drivers in the development of Artificial Intelligence methods, including machine learning, expert systems, or graph-based algorithms. I will introduce the new seminar series on Artificial Intelligence for Genomics and Health (AI4GH), and then discuss our

AI4GH Seminar Series - Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

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B2 R5220

Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge.

Contributions to Computational Methods for Association Extraction from Biomedical Data: Applications to Text Mining and In Silico Toxicology

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B2 L5 R5220

Abstract The task of association extraction involves identifying links between different entities. Here, we make contributions to two applications related to the biomedical field. The first application is in the domain of text mining aiming at extracting associations between methylated genes and diseases from biomedical literature. Gathering such associations can benefit disease diagnosis and treatment decisions. We developed the DDMGD database to provide a comprehensive repository of information related to genes methylated in diseases, gene expression, and disease progression. Using DEMGD, a

Towards rational design of biosynthesis pathways

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B3 R5209

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

AI4GH Seminar Series - Towards Rational Design of Biosynthesis Pathways

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B2 R5220

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

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B3 R5209

Abstract Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity, many driver mutations within a gene occur at low frequencies, which make it challenging to distinguish them from other non-driver mutations. Motivated by these challenges, we

VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B3 L5 R5209

Abstract Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data. We have developed VSIM an automated and easy to use web application for interpretation and visualization of a variety of genomics data, it identifies the candidate disease variants by referencing to four databases Clinvar, GWAS, DIDA, and PharmGKB, and predicted the pathogenic variants. Moreover, it investigates the attitude towards

AI4GH Seminar Series - Genome-scale Regression Analysis Reveals a Linear Relationship for Promoters and Enhancers After Combinatorial Drug Treatment

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B2 R5220

Drug combination therapy for the treatment of cancers and other multifactorial diseases has the potential of increasing the therapeutic effect while reducing the likelihood of drug resistance. In order to reduce the time and cost spent on comprehensive screens, methods are needed which can model additive effects of possible drug combinations.

AI4GH Seminar Series - VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B2 R5220

Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data.

Zero in on computational challenges in Nanopore sequencing

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B9 L2 H1

In this talk, I will first give an overview of the research activities in Structural and Functional Bioinformatics Group (http://sfb.kaust.edu.sa). I will then focus on our efforts on developing computational methods to tackle key open problems in Nanopore sequencing. In particular, I will introduce our recent works on developing a collection of computational methods to decode raw electrical current signal sequences into DNA sequences, to simulate raw signals of Nanopore, and to efficiently and accurately align electrical current signal sequences with DNA sequences. Then, I will further introduce their applications in clinical and environmental fields.

Knowledge-based methods in life sciences

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B9 L2 H2 R2325

In my research I aim to understand how formalized knowledge bases can be used to systematically structure and integrate biological knowledge, and how to utilize these formalized knowledge bases as background knowledge to improve scientific discovery in biology and biomedicine. To achieve these aims, I develop methods for representing, integrating, and analyzing data and knowledge with the specific aim to make the combination of data and formalized knowledge accessible to data analytics and machine learning in bioinformatics. Biomedicine, and life sciences in general, are an ideal domain for knowledge-driven data analysis methods due to the large number of formal knowledge bases that have been developed to capture the broad, diverse, and heterogeneous data and knowledge.

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).

Professor Robert Hoehndorf's PhenomeNET method wins at Ontology matching workshop

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Professor Robert Hoehndorf and Postdoctoral Fellow Miguel Angel Rodriguez Garcia are the winners of the Ontology Alignment Evaluation Initiative Challenge sponsored by Pistoia Alliance.

Student life: studying and working at the Computational Bioscience Research Center (KAUST)

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For Ph.D. students Arturo Magana Mora, Haitham Ashoor and Olaa Motwalli, April 2017 was a particularly nerve-wracking month. After years of hard work and sleepless nights, they finally defended their thesis, bringing them one step closer to graduation.

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.

Alumni Feature: Abdullah Khamis

1 min read ·
Abdullah Khamis has had quite an impressive career so far. After graduating with a Ph.D. degree in Computer Science from KAUST under the supervision of Professor Vladimir Bajic and Associate Professor Xin Gao, he now works as the Director of Data Science in the Center for Strategic Studies (CSS) of the Royal Court of Saudi Arabia.

Sarah Alghamdi defends her master's thesis

1 min read ·
Sarah Alghamdi successfully defended her master's thesis on April 10, 2018. Her project, entitled "Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Ageing and Longevity in Inbred Mouse Strains", was well-received by a panel of professors that included Professor Vladimir Bajic, Associate Professor Xin Gao and Assistant Professor Robert Hoehndorf.

Meshari Alazami defends his thesis proposal

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Meshari Alazami, a Ph.D. student under the supervision of Associate Professor Xin Gao, successfully defended his thesis proposal on May 22, 2018. His topic, "Towards Rational Design of Biosynthesis Pathways", was well received by committee members Associate Professor Xin Gao, Distinguished Professor Takashi Gojobori and Assistant Professor Robert Hoehndorf.

Maxat Kulmanov defends his thesis proposal

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Maxat Kulmanov, a Ph.D. candidate under the supervision of Assistant Professor Robert Hoehndorf, defended his thesis proposal on July 31, 2018.

Computing solutions for biological problems

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Xin Gao’s research is building bridges between computer science and biology.

Meshari Alazmi defends his Ph.D. thesis

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Meshari Alazmi, a Ph.D. candidate under the supervision of Associate Professor Xin Gao, defended his Ph.D. dissertation on October 24, 2018.

Azza Althagafi defends her Master's thesis

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Azza Althagafi, a student under the supervision of Assistant Professor Robert Hoehndorf, defended her Master's thesis on November 1, 2018.

Sara Althubaiti defends her Master's thesis

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Sara Althubaiti, a student under the supervision of Assistant Professor Robert Hoehndorf, defended her Master's thesis on November 5, 2018.

Robert Hoehndorf promoted to associate professor at KAUST

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KAUST faculty member Robert Hoehndorf was recently promoted from the rank of assistant professor to associate professor. Hoehndorf’s promotion caps a year-long process where the German researcher’s scientific and scholarly output was measured and evaluated by internal and external reviews.

Talented KAUST Ph.D. student aims to revolutionize healthcare with machine learning

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KAUST Ph.D. student, Yu Li, is a talented young computer scientist with an interest in developing novel computational methods and algorithms to solve and understand the principles behind the “bio-world.”

Xin Gao promoted to rank of professor

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Computational Bioscience Research Center (CBRC) would like to congratulate Xin Gao for being promoted to the rank of Professor, effective January 1, 2021. Prof. Gao joined KAUST in October 2010 and became a CBRC PI in January 2013. He has since then produced tremendous work in the areas of computational biology, bioinformatics, machine learning, and algorithms. Dr. Xin Gao is also the Acting Associate Director of the Computational Bioscience Research Center and the lead of the Structural and Functional Bioinformatics Group at KAUST. Prior to joining KAUST, he was a Lane Fellow at Lane Center

KAUST alumnus named in Forbes' “30 Under 30 Asia list” – Class of 2022

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KAUST alumnus Yu Li (Ph.D. '20) was recently named amongst the Forbes' "30 Under 30 Asia list" – Class of 2022. Forbes recognized the current assistant professor of computer science and engineering at The Chinese University of Hong Kong (CUHK) for his work developing algorithms to solve problems in biology and health care.

AI tool predicts function of unknown proteins

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Powered by logic and large language models, DeepGO-SE predicts the biological role of proteins and could be used to aid drug discovery.
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.

Yu Li

Ph.D. Student, Computer Science
Research Interests Yu Li is a Ph.D. student at KAUST, majoring in Computer Science. His advisor is Prof. Xin Gao. His research interests are Deep Learning, Machine Learning, and Bioinformatics. He got his Master degree in Computer Science at KAUST in December 2016. Before that, he got his Bachelor degree in Biosciences at the University of Science and Technology of China (USTC). Scientific and Professional Membership Association for the Advancement of Artificial Intelligence(AAAI) Member International Society for Computational Biology (ISCB) Member Education Profile Ph.D student, Computer

Yang Liu

Ph.D. Student, Computer Science
Yang Liu is an M.S./Ph.D. candidate in the KAUST Bio-Ontology research group under the supervision of Professor Robert Hoehndorf. Before joining KAUST. Research Interests Yang's interests include bioinformatics, machine learning and human genomics. She is interested in building models for human disease diagnosis and prognosis, especially for cancer, and applying statistical methods to analyze human omics data. Education Profile B.Sc, Bioengineering, Harbin Institute of Technology, Harbin, China, 2016-2020. M.Sc, Bioengineering, King Abdullah University of Science and Technology, Thuwal, Saudi

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

Ph.D. Student, Computer Science
Xiaopeng Xu is a Ph.D. candidate in the KAUST Structural and Functional Bioinformatics group under the supervision of Professor Xin Gao. Before joining Ph.D. program, Xiaopeng obtained a 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.

Trisevgeni Rapakoulia

Trisevgeni Rapakoulia obtained her Ph.D. degree (2016-2019) in Computer Science 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 Trisevgeni's research interests include bioinformatics, System Biology, Prediction of harmful SNPs, Genome-Wide Association Studies and Artificial Intelligence, Machine Learning Approaches, Evolutionary Algorithms, Feature Selection Techniques. Research field: study the effects of drugs and drug combinations in the transcriptome level

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

Tilman Hinnerichs

Visiting Student, Bio-Ontology Research Group
Tilman Hinnerichs was a visiting student at Professor Robert Hoehndorf's Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Research Interests Tilman's research interests included ​Machine Learning, Bioinformatics, Ontologies, Reasoning, Logic, Particle Methods. He was particularly interested in the application of machine-learning algorithms to problems of the bioscience field. Education Profile ​Since 2016: Diploma Student, Computer Science, TU Dresden​. 2015-2016: Student, Human Medicine, Charité Berlin.

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

Shahad Qathan

M.S. Student, Computer Science
Shahad Qathan is an MS candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Research Interests Artificial Intelligence, more specifically Machine Learning and its applications in healthcare and Bioinformatics Education Profile B.Sc, Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia, 2020

Senay Kafkas

Research Scientist, Bio-Ontology Research Group
Research Interests Senay's research interests include biomedical text/data mining, Natural Language Processing (NLP), and Next Generation Sequencing (NGS) data analysis. Scientific and Professional Membership ​International Society of Computational Biology (ISCB) (2010-present). KAUST Affiliations Bio-Ontology Research Group (BORG).​ Computational Bioscience Research Center (CBRC). Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE). Education Profile Ph.D., Computer Engineering, Eastern Mediterranean University, Cyprus, 2012. M.Sc., Computer Engineering, Eastern

Sara Althubaiti

Ph.D. Student, Computer Science
Sara Althubaiti is currently a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Prior to this, Sara received her master's degree in computer science with a focus on bioinformatics from KAUST in December 2018. Research Interests Sara's research interests include bioinformatics, text mining, ontologies, and cancer. Her research focuses on applying machine learning methods in cancer biology and development specifically in the field of finding driver genes and mutations in

Robert Hoehndorf

Associate Professor, Computer Science
Robert Hoehndorf is an Associate Professor of Computer Science at KAUST. His research focuses on bioinformatics, knowledge representation and reasoning, Semantic Web technologies, and neuro-symbolic methods.

Renmin Han

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Renmin Han is a Research Scientist at the Structural and Functional Bioinformatics Group (SFB) of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Renmin obtained his Ph.D. degree in Computer Science from the Institute of Computing Technology, Chinese Academy of Sciences, China. Research Interests ​Renmin is interested in the development of computer science. Currently, he is working on imaging processing in electron micrography, especially for the reconstruction of biological ultrastructures. His work is to use computational methods to improve the boundary of

Rahaf Shiqdar

Visiting Student, Bio-Ontology Research Group
Rahaf Shiqdar was a visiting student from ​Umm Al-Qura University ​at Professor Robert Hoehndorf's Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). Research Interests Rahaf's research interests included ​Bioinformatics, Machine and Deep learning.

Peng Yang

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Peng Yang worked as a Postdoctoral research fellow at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST) during 2016-2018. Currently, Peng is a research scientist at Baidu Research. Research Interests ​Machine Learning and Data Mining problem, especially online active learning, multi-task learning, recommender systems, and deep learning. Algorithms for bioinformatics problems, especially in disease gene prediction, drug-target interaction prediction, protein complex, and microbial community pattern detection

Othman Soufan

Research Interests I’m a Ph.D. in Computer Science; currently focusing on developing drug discovery and biomedical applications through data mining and graph mining techniques. I excel in analyzing data, extracting unseen useful patterns and achieving a better informative decision-making process. The result of my work can be found in one filed patent and several peer-reviewed publications in international journals ranked top in the respective domain of specialty. I have a strong business background, both academic and empirical, derived from my education and family business. Being transparent

Maxat Kulmanov

Research Scientist, Bio-Ontology Research Group
Maxat Kulmanov is a Research Scientist in the Bio-Ontology Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Maxat's research interests include bioinformatics, knowledge representation and reasoning, machine learning, neural networks, semantic web, and algorithms. He is interested in knowledge discovery and data integration using artificial intelligence and semantic web technologies in biology and biomedicine. Professional Profile 2020-Present: Postdoctoral Research Fellow, Computational Bioscience Research Center, King Abdullah University of

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

MD Nurul Muttakin

Ph.D. Student, Computer Science
Nurul Muttakin, is an M.S./Ph.D. candidate in the Bio-Ontology Research Group (BORG) under the supervision of Professor Robert Hoehndorf. Before joining KAUST Nurul completed his B.Sc. in CSE from Bangladesh University of Engineering and Technology, Bangladesh. Research Interest Nurul is Interested in the application of Artificial Intelligence in Bioinformatics tasks like protein function prediction as well as understanding the theory behind it. Education Profile B.Sc. in CSE, Bangladesh University of Engineering and Technology, Bangladesh

Maha Thafar

Ph.D. Student, Computer Science
Research Interests I am a computer scientist focusing on developing novel computational methods using artificial intelligence, machine learning, deep learning, and graph mining techniques to solve biomedical problems. I am developing these methods to be applied to large-scale chemical and biomedical data. Currently, I'm working on compound-protein interactions prediction and binding affinity prediction projects. My objective is to empower women in my country with a flourishing academic background that enables building better careers for themselves. Additionally, to create a research culture

Luke Slater

Ph.D. Student, Computer Science
Luke Slater is working as a Research Fellow in the Institute of Cancer and Genomics with an interest in ontology and ontology accessories, and their combination with other interdisciplinary techniques to gain novel and practically useful insights into information.

Kexin Niu

Ph.D. Student, Bioengineering
Kexin Niu, is an M.S./Ph.D. candidate in the Bio-Ontology Research Group (BORG) under the supervision of Professor Robert Hoehndorf. Before joining KAUST Kexin obtained her bachelor's degree in Bioinformatics from Southern University of Science and Technology, Shenzhen, China. "What I like best about KAUST is the communication of diverse cultures and diverse disciplines, which enables me to have more possibilities." Research Interest Kexin’ s Research interests include but are not limited to bioinformatics and application of machine learning method in biomedical data. Education Profile 2016 -

Jun Chen

Ph.D. Student, Computer Science
Jun Chen is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Mohamed Elhoseiny at King Abdullah University of Science and Technology (KAUST). Education and Early Career Jun obtained his bachelor's degree in Computer Science from Xi’an Jiaotong-Liverpool University in China, 2018. He joined KAUST as a Master student under the supervision of Prof. Robert Hoehndorf and received his master's degree in Computer Science in 2019. Research Interests Jun's research interests include machine learning, artificial intelligence, natural language processing, knowledge

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. student 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 focuses on computational biology and bioinformatics. As a visiting student before, Haoyang did some research in the field of medical image analysis and metabolic reprogramming. Recently, Haoyang focuses on spatial transcriptomics techniques and has an interest in developing related methods.

Haitham M. Ashoor

Research Interests Haitham Ashoor is a Ph.D. candidate in the Computer Science program at King Abdullah University of Science and Technology (KAUST). He is working under the supervision of Prof. Vladimir Bajic. He has obtained his BSc degree in Computer Engineering from the University of Jordan, Amman Jordan in 2008. He joined KAUST as an MSc student in 2009. In 2011, He finished his master studies under the supervision of Prof. Vladimir Bajic. His main research interests are developing computational methods for next-generation sequencing data analysis and applications of machine learning in

Gian Marco Messa

Ph.D. Student, Computer Science
Gian Marco is a Ph.D. student at the Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). Gian Marco, upon finishing high school in Lecce (Italy), applied to the Politecnico of Milan in the year 2009 in Physics engineering. Due to personal issues, he came back to his hometown to complete the Bachelor's Degree at the University of Salento in Biotechnology. For his master's degree, seeking innovation and cutting edge knowledge, he moved to the University of Bologna, which offered an M. Sc

Fernando Patricio Zhapa-Camacho

Ph.D. Student, Computer Science
Fernando Patricio Zhapa Camacho is an M.S./Ph.D. candidate in the KAUST Bio-Ontology research group under the supervision of Professor Robert Hoehndorf. Before joining KAUST, Fernando obtained a bachelor's degree in Information Technology from Yachay Tech University, Ecuador. Research Interests Fernando’s research interests include knowledge representation and reasoning, machine learning and bioinformatics. He is interested in artificial intelligence and logic integration with applications in biology. Professional Profile: • 2020 to present: Graduate Student, KAUST, Thuwal, Saudi Arabia. •

Francesco Napolitano

​Francesco is a Computer Scientist with a background in Mathematical Models and Machine Learning. He focused his M.Sc. (University of Salerno, SA, Italy) and Ph.D. (University of Salerno and the University of California, Irvine, CA, USA) on supervised and unsupervised data analysis, particularly for clustering of complex, high-dimensional data. He developed and applied data analysis techniques to problems from different fields, including fault detection in avionics and computational pharmacology, before focusing on bioinformatics and systems biology. Research Interests Francesco's main

Dimitrios Kleftogiannis

Dimitrios Kleftogiannis obtained his Ph.D. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group in King Abdullah University of Science and Technology (KAUST), in 2016. Research Interests Dimitrios works mostly on knowledge discovery technologies with applications in Bioinformatics and Computational Biology. ​His research interests include Machine Learning, Artificial Intelligence and Data Analysis of NGS-derived data. He has worked extensively on the prediction of distal DNA regulatory elements focusing on the category of enhancers. He is

Deng Luo

Ph.D. Student, Computer Science
Deng Luo is a Ph.D. Candidate in the Computer Science program under the supervision of Professor Ivan Viola at the Visual Computer Center (VCC) at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. Education and Early Career Deng Luo earned his Bachelor's degree in Bioinformatics from Southern University of Science and Technology in Shenzhen, China in 2016. He has a multidisciplinary background, including bioinformatics, molecular biology, computer science, and entrepreneurship. He joined KAUST and received his Master's degree in Bioscience in 2019. Deng was trained as

Arwa Bin Raies

Research Interests I am a computer scientist whose primary focus is on developing novel machine learning methods to extract useful information from complex datasets. My experience, however, has covered intellectually wide-ranging areas spanning several disciplines in computer science, biology, and chemistry. At the undergraduate level, I focused on computer networks, and I did an internship for six months at Cisco Systems Inc., and I achieved CCNA and CCNP certification. At the Master's level, however, I joined the Computational Bioscience Research Center. I was in charge of a text-mining

Ahmed Abbas

Ahmed Abbas obtained his Ph.D. degree under the supervision of Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Currently, Ahmed is an Assistant Professor at Ain Shams University, Egypt. Research Interests Ahmed's research interests included Bioinformatics, NMR protein structure determination. Selected Publications 1. Carlo Cannistraci, Ahmed Abbas, and Xin Gao. (2015). “Median modified Wiener filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra”. Scientific Reports (Nature

Abeer Almutari

M.S. Student, Computer Science
Abeer Almutari obtained her Master's degree in Computer Science under the supervision of Professor Robert Hoehndorf at Bio-Ontology Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Abeer's research interests included bioinformatics, machine learning and building new automated models using artificial intelligence. Education ​B.Sc. Electrical & Computer Engineering, King Abdul-Aziz University, Jeddah, Saudi Arabia, 2016

Abdullah Khamis

Abdullah Khamis graduated with a Ph.D. degree in Computer Science from KAUST under the supervision of Professor Vladimir Bajic and Associate Professor Xin Gao. He now works as the Director of Data Science in the Center for Strategic Studies (CSS) of the Royal Court of Saudi Arabia. He leads the Data Science team in developing several machine learning based predictive models such as sentiment analysis and prediction. Research Interest Abdullah's research interests were in bioinformatics and machine learning.

Abdullah Muhammad Kamal

Visiting Student, Computer Science
Abdullah Muhammad-Kamal is a visiting student from the University of Warwick, United Kingdom at (BORG) Bio-Ontology Research Group of Professor Robert Hoehndorf at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Currently on his placement year, interning with the Bio-Ontology Research Group at KAUST, he will be graduating with his Bachelor's degree in Computer and Management Sciences in the summer of 2021. Research Interests ​Abdullah's research interests include Bioinformatics, Machine Learning, and Data Science.