Events

Jul 29 - Aug 4, 2018

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

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 15 - Apr 21, 2018

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

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 25 - Mar 31, 2018

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

Computational and Statistical Interface to Big Data

Xin Gao, Program Chair, 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

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

Feb 4 - Feb 10, 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 16, 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 9, 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 11, 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 27, 2017

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

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

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

Nov 13 - Nov 19, 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