Events

Feb 3 - Feb 9, 2019

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  Large-scale Text and Data Integration for Cancer Genomics Research

  Prof. D, Rebholz-Schuhmann (University Cologne, Germany​)​

  Feb 4, 13:00 - 14:00

  B19 L3 H2

  text mining data mining semantic technologies genomics cancer research

  Abstract This talk will give an overview of data and text mining in the life science domain, but will also point out the use of semantic technologies for other domains. Such solutions cover the integration of heterogeneous data sources based on Semantic Web standards (e.g., TCGA, Cosmic, others), the development of smart technologies (e.g., stream processing), graph and literature analytics, and open data initiatives. Application domains are biomedical solutions in cancer genomics and targeted projects together with industrial partners exploiting Semantic Web technologies in general. The

Jan 20 - Jan 26, 2019

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  Biomedical Signal Processing using the Squared Eigenfunctions of the Schrödinger Operator and Machine Learning

  Abderrazak Chahid, Ph.D. Student, Electrical and Computer Engineering

  Jan 24, 13:00 - 15:00

  B1 B4 B4214

  Signal processing machine learning image processing biomedicine

  Abstract The health of a human body is monitored through several physiological measurements such as the heart rate, the blood pressure, the oxygen saturation levels, brain activity, etc. These measurements are taken at predefined points in the body and recorded as temporal signals or colorful images. During the diagnosis phase, physicians analyze these records visually (sometimes it is not visual with the progress in medicine, better to say: sometimes visually) to take treatment decisions. These records are usually contaminated with noise. The origin of this noise may be diverse. For instance

Dec 16 - Dec 22, 2018

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  Network Engineering as Distinct Academic Discipline Beyond the TEE Movement

  Professor Tarek Al-bawab, Jackson State University

  Dec 19, 14:00 - 16:00

  B1 B3 R3119

  Network Engineering communications

  Abstract: Network Engineering as Distinct Academic Discipline Beyond the TEE Movement Network Engineering is now recognized as distinct education discipline in the USA and in many other countries. This development came about with ABET’s1 2015 (current) Criteria for Electrical, Computer, Communications, Telecommunication(s) and Similarly Named Engineering Programs. Few academic programs focused on this area of study in the past. More programs are expected to emerge now with progress in this field and with ABET recognition of it. Such programs have a great potential to prepare a new generation

Dec 2 - Dec 8, 2018

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  Molecular Basis for p85 Dimerization and Allosteric Ligand Recognition

  Dec 6, 13:00 - 15:00

  B3 L5 R5220

  Abstract The phosphatidylinositol-3-kinase α (PI3Kα) is a heterodimeric enzyme that is composed of a p85α regulatory subunit and a p110α catalytic subunit. PI3Kα plays a critical role in cell survival, growth and differentiation, and is the most frequently mutated pathway in human cancers. The PI3Kα pathway is also targeted by many viruses, such as the human immunodeficiency virus (HIV-1), the herpes simplex virus 1 (HSV-1) or the influenza A virus, to create favorable conditions for viral replication. The regulatory p85α stabilizes the catalytic p110α but keeps it in an inhibited state

Nov 25 - Dec 1, 2018

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  Exploring Protein-Protein Interactions with MEGADOCK: Parallelization, Application, and Open Database

  Masahito Ohue Department of Computer Science, School of Computing, Tokyo Institute of Technology, Japan

  Nov 27, 10:00 - 11:00

  B3 L5 R5220

  Abstract Protein-protein interaction (PPI) plays a core role in cellular functions. In recent years, PPI prediction methods based on protein docking have been developed and have been applied for large-scale PPI network prediction based on tertiary structures. We have developed an ultra-high-throughput PPI prediction system based on rigid-body protein-protein docking and supercomputing, named MEGADOCK[1, 2]. MEGADOCK can perform faster docking based on its improved scoring function[3] and implementation for heterogeneous supercomputers[2, 4-6]. MEGADOCK achieved excellent scalability on
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  AI4GH - 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

  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.
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  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

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  AI4GH - 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

  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.
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  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

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  2018 Workshop on Statistics and Data Science

  Nov 12, 08:30 - Nov 15, 17:00

  Auditorium 0215 between Buildings 4 & 5

  data science high dimensional statistics modeling biological processes physical processes

  The Statistics Program at KAUST is proud to host the 2018 Workshop on Statistics and Data Science. This workshop gathers the leading experts on statistical data science to discuss the current needs, challenges and opportunities of modeling massive and high dimensional data, predicting complex biological and physical processes. The workshop will run from November 12-14. Talks and posters presentations will take place in Auditorium 0215 (between Buildings 4 & 5). View Workshop's Agenda.
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  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

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  Everything you always wanted to know about single machine large-scale graph analytics

  Jasmina Malicevic, EPFL

  Nov 8, 12:00 - 13:00

  B9 L2 H1

  EPFL large-scale graph optimization

  Abstract: Graphs are a natural way to capture relations among objects or people. This has led to graph processing systems being used in a wide variety of fields, ranging from biology to social networks, and a large number of such systems have been described in the recent literature. The focus of our recent work is on single machine graph processing systems where the graph is processed from main memory or external storage. We perform a systematic comparison of various techniques proposed to speed up in-memory multicore graph processing. In addition, we take an end- to-end view of execution time
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  AI4GH Seminar Series - Genome-scale Regression Analysis Reveals a Linear Relationship for Promoters and Enhancers After Combinatorial Drug Treatment

  Trisevgeni Rapakoulia, Ph.D., Computer Science

  Nov 7, 12:00 - 13:00

  B2 R5220

  machine learning bioinformatics Drug Combinations drug effects cancer

  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.
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  Prediction of active and inactive chemical compounds from high-throughput assays

  Elaf Jameel Islam, M.S., Computer Science

  Nov 6, 10:15 - 11:45

  B5 L5 R5209

  machine learning

  Abstract This study considers chemical compounds that can exert their activity by interacting with a target protein or other molecular receptor. Our aim is to develop machine learning models that can predict if a chemical compound will be active in a particular test/assay. We will use data from assays that are present in the PubChem knowledge base, specifically in its segment called BioAssays which reports the results of many high-throughput screening experiments. PubChem BioAssays is a valuable resource that contains information from a large number of experiments. In one assay, sometimes many
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  Spatio-Temporal Data Analysis by Transformed Gaussian Processes

  Yuan Yan, PhD Student of Prof. Marc Genton, KAUST

  Nov 5, 17:15 - 18:15

  B2 L5 R5220

  spatio-temporal data analysis statistics

  In the analysis of spatio-temporal data, statistical inference based on the assumption that the data follow a Gaussian distribution is ubiquitous due to its many attractive properties. However, data collected from different fields of science, such as meteorology, astronomy, biology and medicine, rarely meet the assumption of Gaussianity. One option is to apply a monotonic transformation to the data such that the transformed data have a distribution that is close to Gaussian. In this thesis, we focus on a flexible two-parameter family of transformations, the Tukey g-and-h (TGH) transformation
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  Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

  Sara Althubaiti, Ph.D. Student (former), 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
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  Electrocatalytic and Photoelectrochemical Conversion of Energy and Fuels and Storing Solar Energy in Solar Flow Batteries

  Professor Song Jin, University of Wisconsin-Madison

  Nov 4, 14:00 - 15:00

  B2 L5 R5220

  solar energy photoelectrochemical electrocatalytic

  Due to the intermittent nature of most renewable energy sources (such as solar and wind), practical large scale renewable energy deployment and utilization demand both efficient energy conversion and large scale energy storage or alternative usage. Earth-abundant but highly active electrocatalysts need to be developed to enable efficient and sustainable production of energy using electrocatalytic and photoelectrochemical (PEC) water splitting. We report nanostructures of earth-abundant electrocatalysts, such as exfoliated nanosheets of MoS2, WS2, MoSe2, for the hydrogen evolution reaction (HER

Oct 28 - Nov 3, 2018

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

  Arwa Bin Raies, Ph.D., Computer Science

  Nov 1, 11:00 - 13:00

  B2 L5 R5220

  artificial intelligence High Performance Computing machine learning bioinformatics

  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
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  VSIM: Visualization and Simulation of Genomes for Premarital Testing

  Azza Althagafi, Ph.D. Student (former), Computer Science

  Nov 1, 08:00 - 10:00

  B3 L5 R5209

  bioinformatics machine learning artificial intelligence genomics

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

  Azza Althagafi, Ph.D. Student (former), Computer Science

  Oct 31, 12:00 - 13:00

  B2 R5220

  bioinformatics machine learning artificial intelligence genomics

  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.

Oct 21 - Oct 27, 2018

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  Towards rational design of biosynthesis pathways

  Meshari Alazmi, Ph.D., Computer Science

  Oct 24, 17:00 - 18:30

  B3 R5209

  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.
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  AI4GH Seminar Series - Machine Learning with Biological Knowledge Graphs

  Mona Alshahrani, Ph.D. (former), 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
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  Hihooi: Fusing Workload Scalability with Strong Consistency for Transactional Databases

  Michael Sirivianos, Assistant Professor of Computer Engineering, Cyprus University of Technology

  Oct 22, 11:45 - 13:00

  B9 H1

  With the advent of the Internet and Internet-connected devices, modern business applications can experience rapid increases as well as variability in transactional workloads. Database replication has been employed to scale performance and improve availability of relational databases but past approaches have suffered from various issues including limited scalability, performance versus consistency tradeoffs, and requirements for database or application modifications. This talk presents Hihooi, a replication-based middleware system that is able to achieve workload scalability, strong consistency

Oct 14 - Oct 20, 2018

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  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.
• 

  Solution structure and high order numerical methods for time-dependent problems

  Dr. Hailiang Liu, Professor of Mathematics, Iowa State University (ISU)

  Oct 15, 16:00 - 17:00

  B1 L3 R3422

  The course discusses the solution structure and high order structure-preserving numerical methods for several time-dependent problems, modeled by partial differential equations balanced with diffusion, convection, and various interactions. The main topic focuses on the model structure and the construction of stable, accurate approximate algorithms for these problems, and the interplay between analytical theory and computational aspects of such algorithms with real applications. Here is a list of topics to be discussed: (1) Optimization, entropy and differential equations (2) Critical threshold

Oct 7 - Oct 13, 2018

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  EE Seminar: AMMONO-GaN: ultimate substrate solution for energy saving GaN power electronics

  Dr. Robert Dwiliński (University of Warsaw, Poland)

  Oct 10, 15:00 - 16:00

  B3 L5 R5220

  photonics

  Bulk gallium nitride (GaN) crystals are regarded as the most promising candidates for substrates for optoelectronic, high power and high frequency electronic devices. After dacedes of extensive research on a numer of methods incluging HNPS, HVPE and Flux - ammonothermal (AMMONO) method of bulk GaN growth offers the best combination of crystal quality and size, within foreseen scalability able to meet huge demands of lighting and power electronic markets. Developed as analogy of hydrothermal method commonly used for multi-ton production of synthetic quartz, AMMONO method currently provides 2-inch crystals of excellent structural properties and wide spectrum of electrical parameters.
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  AI4GH Seminar Series | Predicting Protein Function and Phenotype Associations

  Maxat Kulmanov, Research Scientist (former), 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

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  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

Sep 16 - Sep 22, 2018

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  CS Graduate Seminar by visitor Professor Vadim Lozin

  Vadim Lozin, Professor, University of Warwick, UK

  Sep 17, 14:00 - 15:00

  KAUST

  Abstract In this talk, we introduce a new graph parameter under the name functionality. We show that functionality generalizes simultaneously several other graph parameters, such as degeneracy or clique-width, by proving that bounded degeneracy or bounded clique-width imply bounded functionality. Moreover, we show that this generalization is proper by revealing classes of graphs of unbounded degeneracy and clique-width, where functionality is bounded by a constant. This includes permutation graphs and unit interval graphs. We also observe that bounded functionality implies bounded VC-dimension

Jul 29 - Aug 4, 2018

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  Similarity Algorithms by Anas Ismail

  Aug 2, 09:00 - 11:00

  B3 R5209

  Abstract Here we provide two similarity algorithms each for a specific type of data. First, we provide a new algorithm to calculate the Gromov hyperbolicity constant which is a measure of how similar a metric is to a tree metric. We also provide a new algorithm determining how similar two spatial trajectories are.

Jul 22 - Jul 28, 2018

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  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.
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  AI in Cancer Precision Medicine

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

  B3 R5220

  About The workshop on " AI in Cancer Precision Medicine" is jointly organized by researchers from the CBRC, the University of Birmingham, and the University of Cambridge, as part of the KAUST-sponsored Center Partnership program. At the workshop, 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. Speakers Dr. Paul Schofield, Dr. Adeeb Noor, Dr. Andreas Karwath. Agenda The workshop is open to all on Wednesday the 25th of July

Apr 22 - Apr 28, 2018

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  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 15 - Apr 21, 2018

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  Neural Inductive Matrix Factorization for Predicting Disease-Gene Associations

  Siqing Hou, M.S., Computer Science

  Apr 18, 10:00 - 11:30

  B3 R5208

  bioinformatics machine learning Disease-Gene Associations

  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.

Apr 8 - Apr 14, 2018

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

  Sarah Alghamdi, Ph.D. Student (former), 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 25 - Mar 31, 2018

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  Big Data in Biodiversity and Health

  Mar 26, 09:30 - Mar 28, 13:30

  B3 L5 5209

  About ​We are witnessing today an enormous increase in the volume and complexity of data across a variety of domains, including bioscience. Extracting useful information from such data is challenging. Although many approaches have already been developed, efficient analysis of big data in bioscience domain is far from satisfactory. Biodiversity and health are prominently characterized by a high volume of data with great complexity of information contained, which lead to various approaches to data analyses. The goal of this workshop is to present a selection of efforts currently being made at

Mar 18 - Mar 24, 2018

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  Computational and Statistical Interface to Big Data

  Xin Gao, Professor, Computer Science

  Mar 19, 08:00 - Mar 21, 17:00

  B9 L2 H2

  We are now in the fourth paradigm of science: Data Science. The massive amount of structured and unstructured data has posed new challenges and opportunities to the fields of computer science and statistics. Traditional computational and statistical methods for data storage, curation, sharing, querying, updating, visualization, analysis, and privacy have been shown to fail in the big data scenario due to the unprecedented volume, velocity, variety, veracity and value of the big data. This conference will bring together a number of prominent researchers in Computer Science and Statistics with common interests and active research in big data, as well as the researchers at KAUST who regularly generate or face big data, such as those in bioscience and red sea research.

Mar 11 - Mar 17, 2018

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  Symbolic AI in Computational Biology

  Robert Hoehndorf, Associate Professor, Computer Science

  Mar 12, 12:00 - 13:00

  B9 H1 R2322

  About 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 combine symbolic and statistical Artificial Intelligence approaches for the analysis of biological and biomedical
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  Causality-based new drug development: Some successful cases and a new challenge

  Naoyuki Kamatani, MD, PhD

  Mar 11, 12:00 - 13:00

  B2 L5 R5209

  artificial intelligence biomedicine drug development

  Abstract The success rate of new drug development is extremely low. It is even lower when the new drug has a novel mechanism of action. From my experience, I propose that the success rate can be dramatically increased by predicting the effects of a drug in humans based on the causality-confirmed data. It is dangerous to develop new drugs based on the data in which causality is not confirmed. In biology, there are three different types of relationships in which the causality is confirmed, i.e. the relationships between parent and child, between gene and phenotype and between intervention and

Feb 25 - Mar 3, 2018

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  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

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  Keynote Speaker | The 8th BEAR PGR Conference & Users Forum 2018

  Robert Hoehndorf, Associate Professor, Computer Science

  Feb 23, 09:00 - 16:30

  The University of Birmingham in the United Kingdom

  High Performance Computing cloud storage data visualisation

  KAUST Assistant Professor Robert Hoehndorf will be a keynote speaker at the 8th BEAR PGR Conference & Users Forum at the University of Birmingham in the United Kingdom.
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  Keynote Presentation - The 8th BEAR PGR Conference & Users Forum 2018

  Feb 23, 09:00 - 16:30

  University of Birmingham United Kingdom

  computational analysis

  KAUST Assistant Professor Robert Hoehndorf will be a keynote speaker at the 8th BEAR PGR Conference & Users Forum​ at the University of Birmingham in the United Kingdom.
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  Fifth KAUST-NVIDIA Workshop on Accelerating Scientific Applications Using GPUs

  Timothy Lanfear , Brent Leback

  Feb 18, 08:00 - Feb 20, 17:00

  B4 B5 A0215

  supercomputing

  The KAUST Supercomputing Laboratory is co-organizing with NVIDIA, a leader in accelerated computing and artificial intelligence, a full-day workshop on accelerating scientific applications using GPUs on Tuesday, February 20th, 2018 in the auditorium between buildings 4 and 5.

Feb 4 - Feb 10, 2018

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  KAUST Research Workshop on Optimization and Big Data

  Peter Richtarik, Professor, Computer Science

  Feb 5, 08:00 - Feb 7, 05:00

  B19 L3 H2

  optimization machine learning Social Network Analysis asynchronous algorithms

  The age of "big data" is here: data of unprecedented sizes is becoming ubiquitous, which brings new challenges and new opportunities. With this comes the need to solve optimization problems of unprecedented sizes.

Dec 10 - Dec 16, 2017

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

  Rawan Olayan, Ph.D., Computer Science

  Dec 11, 10:00 - 12:00

  B3 L5 R5220

  bioinformatics data integration data mining graph mining machine learning

  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

Dec 3 - Dec 9, 2017

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  Big Data Analyses in Evolutionary Biology

  Dec 4, 08:00 - Dec 6, 17:00

  B9 H2

  big data Big data analysis evolutionary biology

  This event is organized by CBRC with financial support from the KAUST Office of Sponsored Research

Nov 5 - Nov 11, 2017

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  Contributions to In Silico Genome Annotation

  Manal Kalkatawi, Ph.D., Computer Science

  Nov 9, 10:00 - 13:00

  B3 L5 R5209

  bioinformatics data mining machine learning Deep learning genomics

  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

Jun 11 - Jun 17, 2017

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  Computational Methods for Large Spatio-temporal Datasets and Functional Data Ranking

  Jun 13, 09:00 - 10:00

  B1 L4 R4102

  computational statistics Environmental Statistics

  In this thesis defense, I will talk about two topics—computational methods for large spatial datasets and functional data ranking. Both are tackling the challenges of big and high-dimensional data.

May 21 - May 27, 2017

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  PCCFD - Predictive Complex Computational Fluid Dynamics

  David Keyes, Professor, Applied Mathematics and Computational Science

  May 22, 08:45 - May 24, 05:00

  B9 L2 H1

  ​CFD algorithms applied mathematics numerical analysis Computer science

  The PCCFD workshop will focus on cutting-edge research in the field of algorithmic development for CFD and multi-scale complex flow simulations.

May 14 - May 20, 2017

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

  Majed Alzahrani, Ph.D., Computer Science

  May 17, 15:00 - 17:00

  B3 L5 R5209

  data mining machine learning Computational biology

  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.