machine learning

Hayssam Dahrouj

Senior Research Scientist at KAUST (King Abdullah University of Science and Technology)

Karen Sanchez

Postdoctoral Research Fellow
Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Hani A. Al Majed

M.S. Student
Electrical and Computer Engineering Master's student at KAUST under the supervision of Professor Bernard Ghanem, with expertise in machine learning workflow automation, physics-informed neural operators, and AI for Science, particularly in chemistry applications. Passionate about leveraging AI to solve complex scientific problems.

Ahmad Sait

M.S. Student, Computer Science
Ahmad Sait is a Master's student in the Computer Science program at KAUST. Before joining KAUST, Ahmad obtained his Bachelor's degree in Computer Science from King Abdulaziz University, Saudi Arabia. Ahmad is also a KAUST Academy graduate from the AI track, having been under the supervision of Prof. Naeemullah Khan. The Academy provided him with seven opportunities to assist in teaching students enrolled in subsequent cohorts.

Saleh AlJazzar

Research Fellow at King Abdullah University of Science and Technology (KAUST) .

Muhammad Esmat

Visiting student at, King Abdullah University of Science and Technology (KAUST).

Yasser Jaber

Visiting Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahsan Mehmood

Intern (former), Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

Abdulkarem Badiea Alshaibani

Visiting Student at the Information Science Lab at the King Abdullah University of Science and Technology (KAUST).

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

Bio2Vec Kickoff Meeting

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Paris France

​​Research Scientist Senay Kafkas will be attending the Bio2Vec kickoff meeting in Paris, France.

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.

AI4GH Seminar Series - Vector Representation of Biological Entities Based on Ontologies and Their Annotations

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B2 R5220

Biological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a biological entity with a set of phenomena within the domain.

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 - 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.

Semantic Prioritization of Novel Causative Genomic Variants in Mendelian and Oligogenic Diseases

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B9 R4223

Abstract Recent advances in Next Generation Sequencing (NGS) technologies have facilitated the generation of massive amounts of genomic data which in turn is bringing the promise that personalized medicine will soon become widely available. As a result, there is an increasing pressure to develop computational tools to analyze and interpret genomic data. In this dissertation, we present a systematic approach for interrogating patients' genomes to identify candidate causal genomic variants of Mendelian and oligogenic diseases. To achieve that, we leverage the use of biomedical data available

WOMoCoE 2019 | Keynote Presentation-Evaluating ontology modules from the perspective of machine learning @JOWO 2019

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Graz Austria

KAUST Assistant professor Robert Hoehndorf will be giving a keynote presentation at the 4th International Workshop on Ontology Modularity, and Evolution @ JOWO 2019, Graz, Austria.

CBRC Dual Seminar - Analysis of COVID-19 Severity - DeepSVP computational method

Sakhaa Al-Saedi, PhD Student; Azza Althagafi, PhD Student

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KAUST

Sakhaa Al-Saedi: We conduct a systematic genetic analysis of risk variants related to increasing the severity of COVID-19. It leads to a better understanding of its genetic basis and identifies the host genes to be targeted to tackle the COVID-19 pandemic and reduce its death toll. Azza Althagafi: We developed DeepSVP, a computational method to prioritize structural variants involved in genetic diseases by combining genomic and gene functions information. DeepSVP significantly improves the success rate of finding causative variants in several benchmarks and can identify novel pathogenic structural variants in consanguineous families.

Characterization of Natural Bacterial Pathogens with Genomic and Predictive Modelling

Dr. Danesh Moradigaravand, Infectious Disease Epidemiology lab, BESE, KAUST

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B3 L5 R5209

In this talk, I will first present how the application of phylogenetic and phylodynamic methods to whole genome sequencing data of multidrug resistant bacterial pathogens provided an in-depth understanding of the epidemiology and evolution of these strains on epidemiological time scales. I will then discuss the characterization of the genomic repertoire of bacterial traits using a combination of machine learning, whole genome sequencing and large-scale phenotypic assays. I will then present the leverage of predictive modelling to predict bacterial features, e.g. antimicrobial resistance, growth, and horizontal gene transfer, from genomic biomarkers. I will finally discuss how large-scale phenotypic assays enabled us to identity genes underlying morphogenesis and biofilm formation.

Professors discuss big data and human health in latest Sci-Café

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​Professors Takashi Gojobori, Xin Gao and Robert Hoehndorf spoke about big data and human health at the Sci-Café event held on February 27. Their talk, which was moderated by James Calvin, Vice President for Academic Affairs, aimed to answer the question, "Can Big Data Solve My Health Problems?" This event was open to all members of the KAUST community.

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.

Mona Alshahrani defends her thesis proposal

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Mona Alshahrani, a Ph.D. candidate under the supervision of Assistant Professor Robert Hoehndorf, defended her thesis proposal on October 10, 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.

Database to support infectious disease research

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Linking disease pathogens to clinical signs and symptoms through a database could support research into the molecular mechanisms of infectious diseases.

MS Thesis Defense: Rare variant collapsing analysis on UK Biobank by Yang Liu

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Yang Liu, M.S./Ph.D. candidate in the Bio-Ontology research group under the supervision of Professor Robert Hoehndorf successfully defended her thesis "Rare variant collapsing analysis on UK Biobank".

MS Thesis Defense: Description Logic EL++Embeddings with Intersectional Closure by Xi Peng

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Xi Peng MS Student under the supervision of Professor Robert Hoehndorf successfully defended his thesis "Description Logic EL++Embeddings with Intersectional Closure"

BORG team win at the KAUST-NVIDIA GPU Hackathon 2022

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Congratulations to Senay Kafkas, Maxat Kulmanov and Fernando Zhapa Camacho of the Bio-Ontology Research Group (BORG) on winning at the KAUST-NVIDIA GPU Hackathon 2022. The 8th edition of the Hackathon on Accelerating Scientific Applications using Graphical Processing Units (GPUs) was held at King Abdullah University of Science and Technology (KAUST), twelve teams of students and researchers enjoyed the opportunity to accelerate their codes in GPU’s under the guidance of expert mentors of NVIDIA, Codee, KAUST Supercomputing Lab (KSL) and KAUST core labs.

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

Xi Peng

Ph.D. Student, Computer Science
Xi Peng likes pop music and doing sports. He graduated from SUSTech in 2021 and planning to continue his academic career at KAUST under the supervision of Professor Robert Hoehndorf. "He says KAUST has the best environment to do research. Life in KAUST is also comfortable. I believe I can achieve a lot in KAUST". Research Interest applications of machine learning and logic integration in biology Education Profile B.Sc, Computer Science, Southern University of Science and Technology, Shenzhen, China, 2017-2021 M.Sc, Computer Science, King Abdullah University of Science and Technology, Thuwal

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.

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

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

Sakhaa Alsaedi

Ph.D. Student, Computer Science
​Sakhaa Al-Saedi completed her bachelor's degree in Computer Science in 2016 at Taibah University. She is the founder of the Medvation startup company, inventing educational kits that teach children concepts of robotics and Machine Learning (ML) in fun ways. She worked as a product developer at the Namma Al-Munawara company, Madinah, before starting her master's degree at KAUST in 2017. She is currently working with the Bio-Ontology Research Group (BORG) of Professor Robert Hoehndorf on human genome sequencing to evaluate the impact of Saudi-specific allele frequencies on variant calling

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.

Olga Mashkova

Ph.D. Student, Computer Science
PhD student in Computer Science. Research interests: machine learning, bioinformatics, mathematics, knowledge representation and reasoning, deep learning, algorithms.

Nurislam Tursynbek

Visiting Student, Bio-Ontology Research Group
Nurislam Tursynbek was a visiting student at Professor Robert Hoehndorf's Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). Research Interests Nurislam's research interests included machine learning and computational biology.

Mona Alshahrani

Mona Alshahrani obtained her Ph.D. degree in Computer Science under the supervision of Professor Robert Hoehndorf at the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). Research Interests Machine Learning, human bioinformatics, data integration. The combination of neuro-symbolic methods over ontologies and semantic RDF graphs with statistical methods. Their applications in data analysis and features representations in several biomedical applications such as phenotypes prediction, drug repurposing, and others. I'm also interested in network

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

Joydeep Medhi

Visiting Student, Bio-Ontology Research Group
Joydeep Medhi was a visiting student from the Indian Institute of Technology, Delhi at Professor Robert Hoehndorf's Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). During his three months internship at BORG he worked on "Machine Learning", Classification of causative disease variant(s) with mutations using Machine Learning Techniques. Successfully implemented and explored different Deep Neural Network architectures utilizing large genome sequencing data, interaction networks and ontologies and performed statistical tests for feature selection

Jay Morgan

Visiting Student, Computer Science
Jay Morgan was a visiting student at (BORG) Bio-Ontology Research Group of Professor Robert Hoehndorf at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. ​Research Interests Jay's research is focused on the verification of machine learning (ML) models, in particular, deep learning. Reasoning as to the decision process of ML is becoming increasingly important especially those that are included in safety-critical systems. Therefore, verifying the properties of the model against requirements as well as interrogating the model itself could provide a great benefit.​​ His

Imane Boudellioua

Imane Boudellioua obtained her Ph.D. degree in Computer Science under the supervision of Professor Robert Hoehndorf at the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). Imene has a Master's degree in Computer Science from KAUST. During Master's her thesis supervisor was Professor Basem Shihada. Thesis title: " A Vehicular Guidance Wireless Sensor Actuator Network." Research Interests Imane Boudellioua's research interests include the application of machine learning and data mining algorithms for functional annotation of various biological

Azza Althagafi

Ph.D. Student, Computer Science
Azza Althagafi is 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. Professional Profile ​2016 to present Graduate Student, KAUST, Thuwal, Saudi Arabia. 2015 - 2016: Teaching Assistant, Taif University, Taif, Saudi Arabia. 2014 – 2015: Volunteer Teaching Assistant, Umm Al-Qura University, Makkah, Saudi Arabia. 2013 – 2016: Assistant Trainer, Mawhiba, The King Abdulaziz and His Companions' Foundation for Giftedness and Creativity, Makkah, Saudi Arabia. Scientific and

Ashraf M. Kibriya

Research Interests ​My research interests include the application of Big Data and Machine Learning techniques to real-world problems in various domains, including biology and biomedicine. In particular, I'm interested in Deep Learning, Text Mining, Network Analysis and Graph Mining. Professional Profile ​2018-Present: Postdoctoral Fellow, KAUST, Thuwal, Saudi Arabia 2011-2018: Doctoral Fellow, KU Leuven, Belgium 2010-2011: Pre-doctoral fellow, KU Leuven, Belgium 2007-2009: Database developer / Datamining specialist, Datamine Ltd., New Zealand 2002-2005: Research Programmer, Machine Learning

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 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.