human health

AI4GH Seminar Series - Machine Learning with Biological Knowledge Graphs

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B2 R5220

Abstract Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval, and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. We develop novel methods for feature learning on biological knowledge graphs and apply them to the prediction of edges in the knowledge graph representing problems of finding candidate genes of diseases, or drugs

Professors discuss big data and human health in latest Sci-Café

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​Professors Takashi Gojobori, Xin Gao and Robert Hoehndorf spoke about big data and human health at the Sci-Café event held on February 27. Their talk, which was moderated by James Calvin, Vice President for Academic Affairs, aimed to answer the question, "Can Big Data Solve My Health Problems?" This event was open to all members of the KAUST community.

Computer models combat malaria

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A new malaria metabolic model may uncover better ways to treat a highly deadly disease.

Database to support infectious disease research

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Linking disease pathogens to clinical signs and symptoms through a database could support research into the molecular mechanisms of infectious diseases.

LD Motif Finder locates ancient hidden protein patterns

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A machine learning method has identified highly elusive amino acid sequences involved in cell morphogenesis and adhesion and in diseases like cancer.

Algorithm turns cancer gene discovery on its head

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A method for finding genes that spur tumor growth takes advantage of machine learning algorithms to sift through reams of molecular data collected from studies of cancer cell lines, mouse models and human patients.

Sensing a heart attack before it strikes

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Nanomaterial-based electronic device monitors a key heart health biomarker.

Studying COVID-19’s envelope protein

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A likeness between genes of the SARS and COVID-19 viruses could inform research into potential treatments.

Deep learning in the emergency department

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Harnessing the power of deep learning leads to better predictions of patient admissions and flow in emergency departments.

CovMT: Tracking virus mutations across the world

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An interactive platform helps users visualize where SARS-CoV-2 mutations start, how wide they spread and how infectious they are.

Coronavirus testing made quick and easy

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New diagnostic technique could help contain the spread of COVID-19 and other viral outbreaks.

Viruses as communication molecules

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Electrical and computer engineers take on complex modeling questions that can further our understanding of virus spread in small spaces.

Airborne antennas to address electro-magnetic fields exposure concerns

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Movable airborne antennas receiving cellphone signals could reduce EMF exposure while offering higher data transmission speed and using less power.

Finding true protein hotspots in cancer research

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Novel statistical methods help tell the difference between "false positives" and true detections of protein domain hotspots that could be linked to cancer.

Getting to the bottom of deadly weather

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Cold still causes far more deaths than heat in India. A study of the correlation between temperature and mortality in the Indian city of Pune has found that cold, rather than heat, is by far the bigger killer.

Can machine learning help predict disease spread?

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Machine learning techniques can provide accurate forecasting of the spread of viruses during pandemics. Under the supervision of Ying Sun and Fouzi Harrou, Yasminah Alali developed an approach that removes human bias and assumptions, predicting pandemic evolution more accurately.

New database to "SpUR" on cancer research

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Xin Gao's team developed an interactive web portal where cancer scientists can interrogate how RNA splicing in noncoding parts of genes fuels the growth of different types of tumors.

AI helps diagnose post-COVID lung problems

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A new AI diagnostic tool developed by KAUST scientists allows doctors to visualize lung damage caused by COVID-19 in more detail.

Extreme analysis of seizures

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CEMSE researchers Matheus Guerrero, Raphaël Huser and Hernando Ombao developed a new approach for analyzing seizures in epilepsy patients by applying extreme value theory to real EEG seizure data.

Bone formation comes down to the nanowire

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Jasmeen Merzaban, Jürgen Kosel and colleagues from their labs developed nanotechnology that accelerates the transition of stem cells into bone and could advance regenerative medicine.