Events

Feb 18 - Feb 25, 2018

• 

  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

  Abstract 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. This event focuses on, but not limited to, computational analysis and numerical modeling the conference will cater to researchers of all schools interested in BEAR facilities. Such as the use of the high-performance computing (HPC) system, BlueBEAR, cloud storage or data visualization.​

Feb 11 - Feb 25, 2018

• 

  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 11, 2018

• 

  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 17, 2017

• 

  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 10, 2017

• 

  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 12, 2017

• 

  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

May 21 - May 28, 2017

• 

  PCCFD - Predictive Complex Computational Fluid Dynamics

  David Keyes, Senior Associate to the President, King Abdullah University of Science and Technology

  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 21, 2017

• 

  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.

Apr 16 - Apr 23, 2017

• 

  Breaking the Boundaries: from Structure to Algorithms

  Vadim Lozin, Professor, University of Warwick, UK

  Apr 17, 14:00 - 15:00

  KAUST

  maximum independent set line graphs boundary classes of graphs

  Abstract Finding a maximum independent set in a graph is an NP-hard problem. However, restricted to the class of line graphs this problem becomes polynomial-time solvable due to the celebrated matching algorithm of Jack Edmonds. What makes the problem easy in the class of line graphs and what other restrictions can lead to an efficient solution? To answer these questions, we employ the notion of boundary classes of graphs. In this talk, we shed some light on the structure of the boundary separating difficult instances of the problem from polynomially solvable ones and analyze algorithmic tools

Apr 9 - Apr 16, 2017

• 

  Computational Methods for ChIP-seq Data Analysis and Applications

  Haitham M. Ashoor, Ph.D., Computer Science

  Apr 10, 16:00 - 17:30

  B3 L5 5209

  computation techniques machine learning bioinformatics data analysis

  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
• 

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

  Arturo Magana Mora, Ph.D., Computer Science

  Apr 10, 13:00 - 15:00

  B3 L5 R5209

  machine learning data mining biology genetics bioinformatics

  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

Apr 2 - Apr 16, 2017

• 

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

  Olaa A. Motwalli, Ph.D., Computer Science

  Apr 9, 16:00 - 17:00

  B3 L5 R5209

  machine learning bioinformatics graph mining genomics

  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

Nov 13 - Nov 20, 2016

• 

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

  Othman Soufan, Ph.D., Computer Science

  Nov 16, 14:00 - 15:00

  H2 B9

  machine learning data mining Computational biology biomedical applications Chemical compounds visualization

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