data mining

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

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H2 B9

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

Contributions to In Silico Genome Annotation

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B3 L5 R5209

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

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

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B3 L5 R5209

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.

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

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B3 L5 R5220

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

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

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B3 L5 R5209

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

Large-scale Text and Data Integration for Cancer Genomics Research

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

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B19 L3 H2

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

Deep Predictive Models for Mining Electronic Health Records

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B1 L4 R4214

There is an increasing growth in the amount of electronic health records (EHRs) being collected by healthcare facilities. Data mining techniques hold great potential to systematically use such data for identifying not only inefficiencies but also best practices that improve care and reduce costs. However, due to the complexity of EHR data, directly applying traditional machine learning techniques may yield unsatisfactory predictive performance. Recent advances in deep learning-based methods provide unprecedented ability to predict patients’ future health status, but they still suffer from the sparsity issue of EHR data.

On Knowledge Graph and Causality for Recommender Systems

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B1 L4 R4214

Knowledge Graph (KG) is a large-scale semantic network consisting of entities/concepts as well as the semantic relationships among them, which could be considered as a concise version of Semantic Web. Recently KG is emerging as a hot topic of knowledge discovery and management under artificial intelligence, facilitating semantic computing. Causal relation is a reflection of user behaviours with backend intention, which is related another emerging hot topic – recommendation interpretability. This talk will cover the recent research progresses in these two areas and highlight some open research challenges in recommender systems.

Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining

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KAUST

Dynamic programming is an efficient technique to solve optimization problems. It is based on decomposing the initial problem into simpler ones and solving these sub-problems beginning from the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects. We developed extensions of dynamic programming which allow us (i) to describe the set of objects under consideration, (ii) to perform a multi-stage optimization of objects relative to different criteria, (iii) to count the number of optimal objects,(iv) to find the set of Pareto optimal points for the bi-criteria optimization problem, and (v) to study the relationships between two criteria.

To Encourage or to Restrict: the Label Dependency in Multi-Label Learning

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KAUST

Multi-label learning addresses the problem that one instance can be associated with multiple labels simultaneously. More or less, these labels are usually dependent on each other in different ways. Understanding and exploiting the Label Dependency (LD) is well-accepted as the key to build high-performance multi-label classifiers, i.e., classifiers having abilities including but not limited to generalizing well on clean data and being robust under evasion attack.

Student life: studying and working at the Computational Bioscience Research Center (KAUST)

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For Ph.D. students Arturo Magana Mora, Haitham Ashoor and Olaa Motwalli, April 2017 was a particularly nerve-wracking month. After years of hard work and sleepless nights, they finally defended their thesis, bringing them one step closer to graduation.

Sarah Alghamdi defends her master's thesis

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Sarah Alghamdi successfully defended her master's thesis on April 10, 2018. Her project, entitled "Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Ageing and Longevity in Inbred Mouse Strains", was well-received by a panel of professors that included Professor Vladimir Bajic, Associate Professor Xin Gao and Assistant Professor Robert Hoehndorf.

New Book "Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining" of Professor Moshkov and His Former Students is Published by Springer

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A new book - "Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining" by Hassan AbouEisha, Talha Amin, Igor Chikalov, Shahid Hussain, and Mikhail Moshkov - was recently published by Springer.

KAUST prospective student: Xiaochuan Gou

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Xiaochuan Gou, 27, from Dalian, China, will join KAUST in the fall of 2020. He will join KAUST as a Ph.D. candidate and member of the Machine Intelligence and kNowledge Engineering research group under the supervision of Professor Xiangliang Zhang.

Zhuo Yang

Ph.D. Student, Computer Science
Zhuo Yang is a Ph.D. student in Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). Research Interests Zhuo's research interests include machine learning and data mining. Education Profile ​B. E., Computer Science, Nankai University, China, 2014. M. E., Computer Science, Nankai University, China, ​2017.

Xiaochuan Gou

Ph.D. Student, Computer Science
Xiaochuan Gou is a Ph.D. candidate in the KAUST Machine Intelligence and kNowledge Engineering research group (MINE) under the supervision of Professor Xiangliang Zhang. Before joining KAUST, Xiaochuan obtained his master’s degree in Computer Science from National Chiao Tung University and a bachelor’s degree in Spatial Information and Digital Technology from Wuhan University, China, respectively. Research Interests Xiaochuan's research interests include machine learning, data mining, and GIS. He aspires to build new machine learning models to resolve existing problems in public traffic

Vasiliki Kordopati

Vasiliki Kordopati obtained Master degree in Computer Science under the co-supervision of Professor Boon Ooi at Photonics Laboratory and Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) from King Abdullah University of Science and Technology (KAUST). Vasiliki is a co-founder of Oæsis, a King Abdullah University of Science and Technology (KAUST) startup spinout, where she is responsible for product development. A computer engineer by training, she received a bachelor's degree in Computer Engineering from the University of Patras, Greece. Research Interest Vasiliki's

Uchenna Akujuobi

Uchenna Akujuobi obtained his Ph.D. degree in computer science under the supervision of Prof. Xiangliang Zhang leading the MINE group at King Abdullah University of Science and Technology (KAUST). Research interests Uchenna's research interests included Machine learning, data mining, Social Networks, Graph mining, Information Retrieval, Text Mining, Big Data, and Deep Networks. Ph.D. dissertation entitled “Learning from Scholarly Attributed Graphs for Scientific Discovery”​. First employment: Research Scientist, Sony AI​, Tokyo, Japan. He is also the creator of Delve, a dataset retrieval, and

Su Wang

Su Wang obtained a master's degree in Computer Science under the supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory in King Abdullah University of Science and Technology (KAUST). First employment: Didi Chuxing​, Beijing, China, 2016. Research Interests Su's research interests included ​Data mining and machine learning. Education Profile ​​B.Sc., Information Science & Electronic Engineering, Zhejiang University, 2014. M.Sc., Computer Science, King Abdullah University of Science and Technology (KAUST). Awards and Distinctions First Prize

Shichao Pei

Ph.D. Student, Computer Science
Shichao Pei is a Ph.D. student in Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). Research Interests Shichao Pei research interests include machine learning and data mining. Education Profile ​B. E., Software Engineering, Xi'an Jiaotong University​, 2016.

Shuai Zheng

Shuai Zheng obtained his Master's degree in Computer Science under the supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory at King Abdullah University of Science and Technology (KAUST). First employment: Ph.D. student, University of Texas at Arlington​, 2013 Research Interests Shuai's research interests included Data mining, Cloud computing and High Performance Computing. Selected Publications ​Dan Williams, Shuai Zheng, Xiangliang Zhang, Hani Jamjoom, "TideWatch: Fingerprinting the Cyclicality of Big Data Workloads". Accepted by the

Senay Kafkas

Research Scientist, Bio-Ontology Research Group
Research Interests Senay's research interests include biomedical text/data mining, Natural Language Processing (NLP), and Next Generation Sequencing (NGS) data analysis. Scientific and Professional Membership ​International Society of Computational Biology (ISCB) (2010-present). KAUST Affiliations Bio-Ontology Research Group (BORG).​ Computational Bioscience Research Center (CBRC). Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE). Education Profile Ph.D., Computer Engineering, Eastern Mediterranean University, Cyprus, 2012. M.Sc., Computer Engineering, Eastern

Robert Werfelmann

Robert Werfelmann obtained a Master's Degree in Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). He did his thesis in a task of Natural Language Processing where the goal is to predict the native language of the author of a non-native text. During his studies, he has built and improved skills in several topics including algorithm design and analysis, concurrent computing, computer networking, and artificial intelligence fields such as

Qiannan Zhang

Ph.D. Student, Computer Science
Qiannan Zhang is a Ph.D. student in Computer Science program at Machine Intelligence and Knowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). ​​​​​Research interests Qiannan's research interests include Machine learning and data mining​. Education Profile B. E., Communication Engineering, Tianjin University, China, 2010-2014. M. E., Communication Engineering, Tianjin University, China, 2014-2017. Research Intern, KAUST, 2017.

Othman Soufan

Research Interests I’m a Ph.D. in Computer Science; currently focusing on developing drug discovery and biomedical applications through data mining and graph mining techniques. I excel in analyzing data, extracting unseen useful patterns and achieving a better informative decision-making process. The result of my work can be found in one filed patent and several peer-reviewed publications in international journals ranked top in the respective domain of specialty. I have a strong business background, both academic and empirical, derived from my education and family business. Being transparent

Mohammad Mohiuddin Azad

Ph.D. Student, Computer Science
Research Interests Study of greedy and dynamic programming algorithms for construction of exact and approximate decision trees, rules and reducts for decision tables with many-valued decisions Analysis and design of classifiers based on decision trees, reducts, and decision rule systems Data mining, Machine Learning Selected Publications Mohammad Azad, Igor Chikalov, Shahid Hussain and Mikhail Moshkov. “Restricted multi-pruning of decision trees”. MLDAS (Machine Learning and Data Analytics Symposium) 2016 organized by QCRI (Qatar Computing Research Institute) and Boeing International, Doha

Mikhail Moshkov

Professor Moshkov specializes in various areas of discrete applied mathematics, theoretical computer science and operations research. He is the author or co-author of more than 200 research publications, including 10 books published by Springer.

Maha Thafar

Ph.D. Student, Computer Science
Research Interests I am a computer scientist focusing on developing novel computational methods using artificial intelligence, machine learning, deep learning, and graph mining techniques to solve biomedical problems. I am developing these methods to be applied to large-scale chemical and biomedical data. Currently, I'm working on compound-protein interactions prediction and binding affinity prediction projects. My objective is to empower women in my country with a flourishing academic background that enables building better careers for themselves. Additionally, to create a research culture

Luke Slater

Ph.D. Student, Computer Science
Luke Slater is working as a Research Fellow in the Institute of Cancer and Genomics with an interest in ontology and ontology accessories, and their combination with other interdisciplinary techniques to gain novel and practically useful insights into information.

Lu Yu

Lu Yu obtained his Ph.D. degree in computer science under the supervision of Prof. Xiangliang Zhang leading the MINE group at King Abdullah University of Science and Technology (KAUST). He mainly worked on learning representation of discrete data structure (e.g. graph, sequence, entity) that intersected with graph theory, optimization, adversarial learning. Before joining KAUST, he worked as a Data Mining Engineer at Alibaba Group. Ph.D. dissertation entitled “Exploring Entity Relationship in Pairwise Ranking: Adaptive Sampler and Beyond”. First employment: Senior Algorithm Engineer, Ant Group

Ibrahim Alabdulmohsin

Ph.D. Student, Computer Science
Ibrahim Alabdulmohsin obtained a Ph.D. degree in Computer Science under the co-supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory and Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Currently, Ibrahim is working with IT Strategy and Technology Planning Department at Saudi Aramco. Pursued his Ph.D. degree at MINE Lab from 2012 - 2017. Ibrahim obtained his M.Sc. degree in Computer Science from Stanford University. Dissertation Title: Learning via Query

Hongyan Bao

Ph.D. Student, Computer Science
Hongyan Bao is a Ph.D. student at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). Research Interests ​​Hongyan's research interests include Data Mining. Selected Publications ​Zaiqiao Meng, Shangsong Liang, Hongyan Bao, and Xiangliang Zhang. Co-Embedding Attributed Networks. Accepted by The Twelfth International Conference on Web Search and Data Mining (WSDM 2019), February ​11-15, 2019, Melbourne, Australia. (Acceptance rate = 84/511 = 16%). Education Profile B

Guoda Chen

Guoda Chen obtained his M.S. degree in Computer Science under the supervision of Prof. David Keyes (HiCMA Research Group) and Prof. Panagiotis Kalnis (Cloud Research Group) at King Abdullah University of Science and Technology (KAUST). Research Interest Guoda's research interests included Data Management and Data Mining.

Guolei Sun

Guolei Sun obtained his MS degree in the Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). Research interests Guolei's research interests include Machine learning and data mining. Education Profile ​B.E., Biomedical Engineering, Huazhong University of Science & Technology​, 2015​. M.Sc., Computer Science, King Abdullah University of Science and Technology (KAUST).

Fawaz Alsolami

Ph.D. Student, Computer Science
Research Interests Sequential optimization for inhibitory rules relative to length and coverage Analysis and design of classifiers based on inhibitory rules and trees Machine Learning and Data Mining Selected Publications F. Alsolami, T. Amin, M. Moshkov, B. Zielosko: Comparison of heuristics for optimization of association rules. CS&P 2015: 4-11 (2015) F. Alsolami, T. Amin, I. Chikalov, M. Moshkov, B. Zielosko: Dynamic programming approach for construction of association rule systems. CS&P 2015: 12-21 (2015) F. Alsolami, I. Chikalov, M. Moshkov: Comparison of heuristics for inhibitory rule

Ehab Abdelhamid

Ph.D. Student, Computer Science
Ehab Abdelhamid is a Ph.D. student in InfoCloud Research Group (CLOUD) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Panagiotis Kalnis. Research Interests Ehab's research interests include Data mining over very large datasets and Information Retrieval. Currently working on pattern mining in large graphs; The approach currently developed eliminates unnecessary iterations that could not be avoided in previous related work. As a result, improved performance in large graphs. Selected Publications ​Ehab A. Mohamed , Ahmed R.and Samhaa R. , 2009,

Di Wang

Assistant Professor, Computer Science
Professor Wang's research addresses issues and societal concerns arising from machine learning, particularly in the areas of privacy, security, safety, fairness, robustness, interpretability and transparency. He aims to develop provable and practical algorithms to address trustworthiness issues in machine learning.

Chisato Fujii

Chisato Fujii obtained her Master's degree in Computer Science under the supervision of Prof. Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Research Interests Chisato's research interests included Bioinformatics and Computational Biology. Born in Japan, Chisato is a software engineer at a fashion technology startup in Cambridge. She works on the development of 3-dimensional garment rendering on a customizable body shape unique to the shopper's size. How would you describe your work/activities at KAUST? ''I studied a

Basmah Altaf

Basmah Altaf obtained her Ph.D. degree in Computer Science under the supervision of Prof. Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory in King Abdullah University of Science and Technology (KAUST). Research Interests​​ Basmah's research interests include Recommender systems and Data mining​. Education Profile ​B. E., Computer & Information Systems, N.E.D. University of Engineering and Technology, Karachi, Pakistan, 2010. M. S., Computer Science, KAUST, 2013. Awards and Distinctions ​'Gold Medal' for securing '1st Position' among 140 students in Bachelor

Azza Althagafi

Ph.D. Student, Computer Science
Azza Althagafi is a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Professional Profile ​2016 to present Graduate Student, KAUST, Thuwal, Saudi Arabia. 2015 - 2016: Teaching Assistant, Taif University, Taif, Saudi Arabia. 2014 – 2015: Volunteer Teaching Assistant, Umm Al-Qura University, Makkah, Saudi Arabia. 2013 – 2016: Assistant Trainer, Mawhiba, The King Abdulaziz and His Companions' Foundation for Giftedness and Creativity, Makkah, Saudi Arabia. Scientific and

Arturo Magana Mora

​Arturo Magana-Mora obtained his Ph.D. in Computer Science at the King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Vladimir Bajic in 2017 and later joined the National Institute of Advanced Industrial Science and Technology (AIST) in Japan as a postdoctoral research fellow in the Com. Bio Big-Data Open Innovation Lab (CBBD-OIL). Research Interest His research interests include the development of novel machine-learning and data mining techniques to address the complex problems in biology. His research work has resulted in several peer-reviewed

Abdulhakim Qahtan

Abdulhakim Qahtan obtained his Ph.D. degree in Computer Science under the supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory at King Abdullah University of Science and Technology (KAUST). Dissertation Title: Efficient Estimation of Dynamic Density Function with Applications in Streaming Data. First employment: Postdoctoral researcher at QCRI in Qatar. Research Interests Abdulhakim's research interests included Data mining, Statistics​​​ and Applying statistical models in machine learning. Selected Publications Basma Alharbi, Abdulhakim