Events

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

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.

Apr 16 - Apr 22, 2017

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  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 15, 2017

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  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
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  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
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  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 19, 2016

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  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