Thursday, June 30, 2022, 08:30
- 10:30
https://kaust.zoom.us/s/96993442133
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In this dissertation, we combined artificial intelligence and machine/deep learning with chemical and biological properties to develop several computational methods to solve biomedical domain problems, specifically drug repositioning, and demonstrated their efficiencies and capabilities. We developed three network-based DTI prediction methods using machine learning, graph embedding, and graph mining. These methods significantly improved prediction performance, and the best-performing method even reduces the error rate by more than 33% across all datasets compared to the best state-of-the-art method. As it is more insightful to predict continuous values that indicate how tightly the drug binds to a specific target, we conducted a comparison study of current regression-based methods that predict drug-target binding affinities (DTBA). Our methods demonstrated their efficiency and capability by achieving high prediction performance and identifying therapeutic targets for several cancer types. We further conducted a lung cancer case study of findings that support the novel predicted targets.
Prof. Takashi Gojobori, Computational Bioscience Research Center, KAUST
Tuesday, May 24, 2022, 12:00
- 14:00
Building 19, Hall 3
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The Computational Bioscience Research Center (CBRC) will be holding a student poster competition as part of its yearly conference. Core Labs booths will also be featured in this session.
Prof. Takashi Gojobori, Computational Bioscience Research Center, KAUST
Monday, May 23, 2022, 08:00
- 16:30
Building 19, Hall 1
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The Computational Bioscience Research Center (CBRC) is pleased to invite the KAUST community to the KAUST Research Conference on Advances in Metagenomics and its Applications.
Michal Mankowski , Assistant Professor, Erasmus University Rotterdam; Elisa Laiolo, PhD Student, Red Sea Research Center
Tuesday, April 26, 2022, 13:00
- 14:00
Zoom: https://kaust.zoom.us/j/97787963445
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Elisa Laiolo: In her talk, Elisa will provide insights in to the KAUST Metagenomic Analyses Platform (KMAP), which is the first catalogue of the global ocean genome and its partitioning among taxonomical and functional groups, providing insights in its high diversity and functional variety. Michal Mankowski: In his talk, Michal will explain the techniques of using simulation with optimization, in order to design boundaries-free CAS that aims to maintain the prioritization of organ transplant for pediatric, different blood types, and status 1 candidates while minimizing the overall deaths.
Sakhaa Al-Saedi, PhD Student; Azza Althagafi, PhD Student
Tuesday, April 12, 2022, 13:00
- 14:00
https://kaust.zoom.us/j/94042806504
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Sakhaa Al-Saedi: We conduct a systematic genetic analysis of risk variants related to increasing the severity of COVID-19. It leads to a better understanding of its genetic basis and identifies the host genes to be targeted to tackle the COVID-19 pandemic and reduce its death toll. Azza Althagafi: We developed DeepSVP, a computational method to prioritize structural variants involved in genetic diseases by combining genomic and gene functions information. DeepSVP significantly improves the success rate of finding causative variants in several benchmarks and can identify novel pathogenic structural variants in consanguineous families.
Monday, April 11, 2022, 11:00
- 12:00
https://kaust.zoom.us/j/95586142868
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Dr. Katsuhiko Mineta will give a talk on "Population genomics of indigenous inhabitants in Arabian Peninsula". This talk will highlight the research conducted in areas of population genomic profiling of 957 unrelated individuals who self-identify with 28 large tribes in Saudi Arabia. The result of this research disclose a granular map of population structure in Arabia and what implications this will have for future genetic studies into diseases.
Wednesday, March 30, 2022, 17:30
- 19:30
https://kaust.zoom.us/j/95328976703
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.
Wednesday, March 30, 2022, 14:00
- 15:00
https://kaust.zoom.us/j/98007745127
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Knowing metastasis is the primary cause of cancer-related deaths incentivized research to unravel the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications. In this regard, predicting metastasis onset has also been explored using artificial intelligence (AI) approaches that are machine learning (ML), and more recently, deep learning (DL).
Juexiao Zhou, MS Student and Manola Moretti, Research Scientist
Tuesday, March 29, 2022, 14:00
- 15:00
Building 9, Level 2, Room 2322
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Talk 1: PPML-Omics: a Privacy-Preserving federated Machine Learning system protects patients’ privacy from omic data. Talk 2: Peptide biopolymers in medical and environmental applications: an AFM and Raman spectroscopy perspective
Professor Paul Matsudaira, National University of Singapore
Tuesday, February 15, 2022, 16:30
- 17:30
Zoom webinar
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Abstract

In the mammalian intestine, stem cells (ISCs) located in basal crypts, replicate and tran

Prof. Yuxuan Hu, Xidian University, China
Thursday, December 23, 2021, 12:30
- 14:00
https://kaust.zoom.us/s/97004787600
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Signal transduction is the primary mechanism for cell-cell communication and scRNA-seq technology holds great promise for studying this communication at high levels of resolution. Signaling pathways are highly dynamic and cross-talk among them is prevalent. Due to these two features, simply examining expression levels of ligand and receptor genes cannot reliably capture the overall activities of signaling pathways and the interactions among them.
Sahika Inal, Associate Professor, Bioscience, Organic Bioelectronics Laboratory, Computational Bioscience Research Center
Tuesday, September 28, 2021, 15:00
- 16:00
Building 9, Level 2, Hall 2
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In this talk, I will show how these materials are used in organic electrochemical transistors (OECTs) to detect biological species in physiological media. I will introduce two types of OECT based sensors; one that detects Alzheimer’s disease biomarkers with performance exceeding the state-of-the-art,1,2 and the other that detects coronavirus spike proteins at the physical limit.3 Having challenged these sensors with patient samples, I will discuss areas where proof-of-concept organic biosensor platforms may fail. By tackling each of these problems, we improve device performance to a level that marks a considerable step toward biochemical sensing of infectious and noninfectious disease biomarkers. I will highlight how computational methods can aid in sensor development and organic semiconductor research.
Artificial Intelligence Initiative at KAUST
Wednesday, April 28, 2021, 08:30
- 16:30
https://kaust.zoom.us/j/96464686903

The Artificial Intelligence Initiative (AII) at KAUST cordially invites you to attend the KAUST Conference on Artificial Intelligence to be held on April 28-29, 2021. The conference is a two full-day event and will feature the broad AI landscape at KAUST by delving into topics on machine learning, AI theory and foundations, systems, and the many applications of AI in various scientific fields ranging from healthcare and biology to automation and visual computing.

The conference will be a hybrid event with both online streaming (Zoom Webinar) and limited in-person (Auditorium of Building 20) attendance.

Registration for the event is required for both in-person and online participation: Register here.

Registration for participants will remain open until midnight of April 25, 2021.