Events

Jul 16 - Jul 22, 2023

Prioritizing Causative Genomic Variants by Integrating Molecular and Functional Annotations from Multiple Biomedical Ontologies

Azza Althagafi, Ph.D. Student, Computer Science

Jul 20, 13:00 - 17:00

B2 L5 R5220

The dissertation focuses on developing novel computational methods to improve the diagnosis of patients with rare or complex diseases. By systematically relating human phenotypes resulting from gene function loss or change to gene functions and anatomical/cellular locations, the candidate aims to enhance the prediction and prioritization of disease-causing variants. These methods, leveraging graph-based machine learning and biomedical ontologies, demonstrate significant improvements over existing approaches. The presentation will include a systematic evaluation of the methods, demonstrating their ability to compensate for incomplete data and their applications in biomedicine and clinical decision-making. This research contributes to more effective methods for predicting disease-causing variants and advancing precision medicine, offering promising prospects for improved diagnostics and patient care.
Ontology design patterns and methods for integrating phenotype ontologies

Sarah Alghamdi, Ph.D. Student, Computer Science

Jul 20, 09:00 - 10:00

B3 L5 R5209.

bio-ontology phenotype ontologies

Ontologies are widely used in various domains, including biomedical research, to structure information, represent knowledge, and analyze data. Combining ontologies from different domains is crucial for systematic data analysis and comparison of similar domains. This requires ontology composition, integration, and alignment, which involve creating new classes by reusing classes from different domains, aggregating types of ontologies within the same domain, and finding correspondences between ontologies within the same or similar domain.
Towards the Gene Regulation Knowledge Commons

Prof. Jesualdo Tomas Fernandez Breis, University of Murcia, Spain

Jul 19, 11:30 - 13:00

B2 L5 R5220

Knowledge about transcription factor binding and regulation, target genes, cis-regulatory modules and topologically associating domains is not only defined by functional associations like biological processes or diseases, but also has a determinative genome location aspect.

Apr 30 - May 6, 2023

Mar 12 - Mar 18, 2023

Towards Neurosymbolic AI: From Knowledge Infrastructure to Explainable Predictions

Dr. Michel Dumontier, Distinguished Professor, Data Science

Mar 16, 12:00 - 13:00

B2 L5 R5220

data science

Abstract The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These

Feb 5 - Feb 11, 2023

Bio-Hackathon MENA 2023

Bio-Hackathon MENA 2023

Feb 7, 08:00 - Feb 11, 17:00

KAUST Hotel

bioinformatics data mining ontology

Bioinformatics experts, Don’t miss the opportunity to collaborate with researchers and field professionals in the BioHackathonMENA2023 event. BioHackathon events involve a large number of people that meet on-site to discuss ideas and implement projects in a collaborative manner during intensive coding sessions.

Jan 29 - Feb 4, 2023

Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations

Ricardo Henao, Associate Professor (former), Bioengineering

Feb 1, 12:00 - 13:00

B3 L5 R5220

bioinformatics

We propose a structured latent ODE model that explicitly captures system input variations within its latent representation. Building on a static latent variable specification, our model learns (independent) stochastic factors of variation for each input to the system, thus separating the effects of the system inputs in the latent space. This approach provides actionable modeling through the controlled generation of time-series data for novel input combinations (or perturbations). Additionally, we propose a flexible approach for quantifying uncertainties, leveraging a quantile regression formulation.

Dec 4 - Dec 10, 2022

Characterization of Natural Bacterial Pathogens with Genomic and Predictive Modelling

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

Dec 5, 12:00 - 13:00

B3 L5 R5209

machine learning Metagenomics

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.

Nov 27 - Dec 3, 2022

Modelling and Simulation of Biological Systems - 2022-11-28

Gabriel Wittum, Professor, Applied Mathematics and Computational Sciences

Nov 28, 12:00 - 13:00

B2 L5 R5209

applied mathematics bioengineering

Biological systems are distinguished by their enormous complexity and variability. That is why mathematical modelling and computational simulation of those systems is very difficult, in particular thinking of detailed models which are based on first principles. The difficulties start with geometric modelling which needs to extract basic structures from highly complex and variable phenotypes, on the other hand also has to take the statistic variability into account.

Apr 10 - Apr 16, 2022

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

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

Apr 12, 13:00 - 14:00

KAUST

machine learning Computer science artificial intelligence

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.

Jun 27 - Jul 3, 2021

Artificial Intelligence methods for precision health: Applications to rare disease and cancer research

Robert Hoehndorf, Associate Professor, Computer Science

Jun 27, 10:00 - 11:00

B9 L2 H2

artificial intelligence

Abstract The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will show how to incorporate biological background knowledge in machine learning models for the identification of gene-disease

Apr 5 - Apr 11, 2020

Predicting Single Gene Loss of Function Phenotypes

Maxat Kulmanov, Research Scientist, Bio-Ontology Research Group

Apr 6, 19:30 - 21:30

KAUST

We developed and expanded novel methods for representation learning, predicting protein functions and their loss of function phenotypes. We use deep neural network algorithm and combine them with symbolic inference into neural-symbolic algorithms. Our work significantly improves previously developed methods for predicting protein functions through methodological advances in machine learning, incorporation of broader data types that may be predictive of functions, and improved systems for neural-symbolic integration. The methods we developed are generic and can be applied to other domains in which similar types of structured and unstructured information exist. In future, our methods can be applied to prediction of protein function for metagenomic samples in order to evaluate the potential for discovery of novel proteins of industrial value. Also our methods can be applied to the prediction of loss of function phenotypes in human genetics and incorporate the results in a variant prioritization tool that can be applied to diagnose patients with Mendelian disorders.

Mar 1 - Mar 7, 2020

Knowledge-based methods in life sciences

Robert Hoehndorf, Associate Professor, Computer Science

Mar 3, 10:00 - 11:30

B9 L2 H2 R2325

Computer science bioinformatics knowledge representation

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.

Jan 19 - Jan 25, 2020

KAUST Research Conference Digital Health 2020

Takashi Gojobori, Ibn Sina Distinguished Professor (former), Bioscience

Jan 20, 08:00 - Jan 22, 17:00

B19 L2 H1

digital health artificial intelligence computational science big data analytics Omics

Computational Bioscience Research Center at King Abdullah University of Science and Technology is pleased to announce the KAUST Research Conference on Digital Health 2020.

Dec 1 - Dec 7, 2019

Professor Robert Hoehndorf will be a guest speaker at upcoming Science to Watch

Dec 1, 18:00 - 20:00

B1 R2418

Science to Watch is a science orientated movie series followed by a discussion about the movie as it relates to science.

Nov 3 - Nov 9, 2019

Accelerating biomedical discovery with an Internet of FAIR data and services

Dr. Michel Dumontier, Distinguished Professor of Data Science at Maastricht University, The Netherlands

Nov 4, 12:00 - 13:00

B9 L2 H1 R2322

biomedicine collective knowledge incompatible formats overlapping heterogenous access points computational standards effective representations semantic technologies FAIR automated scientific discovery

In this talk, I will discuss our work to create computational standards, platforms, and methods to wrangle knowledge into simple, but effective representations based on semantic web technologies that are maximally FAIR - Findable, Accessible, Interoperable, and Reuseable - and to further use these for biomedical knowledge discovery. But only with additional crucial developments will this emerging Internet of FAIR data and services enable automated scientific discovery on a global scale.

Sep 22 - Sep 28, 2019

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

Robert Hoehndorf, Associate Professor, Computer Science

Sep 23, 09:00 - 16:00

Graz Austria

machine learning

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.

Feb 24 - Mar 2, 2019

Neuro-symbolic systems in the life sciences

Robert Hoehndorf, Associate Professor, Computer Science

Feb 25, 12:00 - 13:00

B9 H1 R2322

biomedical data data analysis symbolic methods

Abstract The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will describe how to generate knowledge graph embeddings for analysis of biological and biomedical data. Brief Biography Robert

Feb 3 - Feb 9, 2019

Dec 2 - Dec 8, 2018

AI4GH Seminar Series - D3 System: Drug-Drug Interaction Discovery and Demystification

Adeeb Noor, Assistant professor of Bioinformatics, King Abdulaziz University

Dec 2, 12:30 - 13:30

B2 R5220

Contemporary drug-drug interactions (DDIs) research offers a clinically impactful opportunity to identify DDIs prior to their occurrence; however, the clinical utility of current DDI identification systems have, to date, been greatly limited by the inability to accurately and concisely identify their pathway of interaction for the vast lists of identified computationally and clinically valid DDIs.

Nov 25 - Dec 1, 2018

AI4GH Seminar Series - Computational Modeling of Malaria Metabolism Reveals Different Stages and Species Nutrient Preferences and Drug Targets

Alyaa M Mohamed, Ph.D., Bioscience

Nov 25, 12:00 - 13:00

B2 R5220

genome plasmodial infections

Malaria kills nearly one-half million people a year and over 1 billion people are at risk of becoming infected by the parasite. Plasmodial infections are difficult to treat for a myriad of reasons, but the ability of the organism to remain latent in hosts and the complex life cycles greatly contributed to the difficulty in treat malaria.

Nov 18 - Nov 24, 2018

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

Fatima Zohra Smaili, Ph.D. Student, Computer Science

Nov 18, 12:00 - 13:00

B2 R5220

machine learning data analysis

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.

Nov 11 - Nov 17, 2018

AI4GH Seminar Series - Towards Rational Design of Biosynthesis Pathways

Meshari Alazmi, Ph.D., Computer Science

Nov 11, 12:00 - 13:00

B2 R5220

machine learning bioinformatics structural biology systems biology

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.

Nov 4 - Nov 10, 2018

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

Sara Althubaiti, Ph.D. Student, Computer Science

Nov 5, 10:30 - 12:30

B3 R5209

bioinformatics text mining machine learning Ontologies cancer

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

Oct 28 - Nov 3, 2018

Oct 21 - Oct 27, 2018

AI4GH Seminar Series - Machine Learning with Biological Knowledge Graphs

Mona Alshahrani, Ph.D., Computer Science

Oct 24, 12:00 - 13:00

B2 R5220

bioinformatics data integration machine learning knowledge graphs human health

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

Oct 14 - Oct 20, 2018

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

Oct 17, 12:00 - 13:00

B2 R5220

bioinformatics text mining machine learning cancer Ontologies

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.

Oct 7 - Oct 13, 2018

AI4GH Seminar Series | Predicting Protein Function and Phenotype Associations

Maxat Kulmanov, Research Scientist, Bio-Ontology Research Group

Oct 10, 12:00 - 13:00

B2 R5220

biology biomedicine artificial intelligence knowledge discovery data integration protein function prediction

The amount of available protein sequences is rapidly increasing, mainly as a consequence of the development and application of high throughput sequencing technologies in the life sciences.

Sep 30 - Oct 6, 2018

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

Robert Hoehndorf, Associate Professor, Computer Science

Oct 3, 12:00 - 13:00

B2 R5220

biology biomedicine bioinformatics Ontologies

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

Jul 22 - Jul 28, 2018

AI in Cancer Precision Medicine Workshop

Dr. Paul Schofield

Jul 25, 08:00 - Jul 26, 13:00

B3 R5220

We will investigate how novel AI technologies, including progress in machine learning, knowledge representation and reasoning can be applied to improving diagnosis and treatment of cancer in the era of genomic medicine.

Apr 22 - Apr 28, 2018

Bio2Vec Kickoff Meeting

Apr 25, 08:00 - Apr 26, 17:00

Paris France

big data machine learning Data Analytics

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

Apr 8 - Apr 14, 2018

Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Ageing and Longevity in Inbred Mouse Strains

Sarah Alghamdi, Ph.D. Student, Computer Science

Apr 10, 13:00 - 14:30

B9 R3120

biomedicine Ontologies data analysis semantic analysis computation techniques

Abstract In biomedical research, ontologies are widely used to represent knowledge as well as annotate datasets. Many of the existing ontologies cover a single type of phenomena, such as a process, cell type, gene, pathological entity or anatomical structure. Consequently, it is required to use multiple ontologies to fully characterize the observations in the datasets. Although this allows precise annotation of different aspects of a given dataset, it limits our ability to use the ontologies in data analysis, as the ontologies are usually disconnected and their combination cannot be exploited

Mar 11 - Mar 17, 2018

Feb 25 - Mar 3, 2018

Symbolic AI in Computational Biology: Applications to Disease Gene and Drug Target Identification

Robert Hoehndorf, Associate Professor, Computer Science

Feb 26, 16:30 - 17:30

The University of Cambridge in the United Kingdom

Abstract KAUST Assistant Professor Robert Hoehndorf will give a seminar on " Symbolic AI in Computational Biology: Applications to Disease Gene and Drug Target Identification" at the University of Cambridge in the United Kingdom. More Information The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are

Feb 18 - Feb 24, 2018

Sep 4 - Sep 10, 2016

Bio-ontologies and their role in analyzing personal genome data

Robert Hoehndorf, Associate Professor, Computer Science

Sep 4, 15:45 - 16:45

KAUST

biomedical ontologies data analysis

The aim of the tutorial is to introduce recent advances in using biomedical ontologies for data analysis, in particular for the interpretation of personal genome data and identifying causal variants underlying a phenotype or disease.