Abstract

Revoking personal private data is one of the basic human rights, which has already been s

The dissertation focuses on developing novel computational methods to improve the diagnosis of patients with rare or complex diseases. By systematically relating human phenotypes resulting from gene function loss or change to gene functions and anatomical/cellular locations, the candidate aims to enhance the prediction and prioritization of disease-causing variants. These methods, leveraging graph-based machine learning and biomedical ontologies, demonstrate significant improvements over existing approaches. The presentation will include a systematic evaluation of the methods, demonstrating their ability to compensate for incomplete data and their applications in biomedicine and clinical decision-making. This research contributes to more effective methods for predicting disease-causing variants and advancing precision medicine, offering promising prospects for improved diagnostics and patient care.

Ontologies are widely used in various domains, including biomedical research, to structure information, represent knowledge, and analyze data. Combining ontologies from different domains is crucial for systematic data analysis and comparison of similar domains. This requires ontology composition, integration, and alignment, which involve creating new classes by reusing classes from different domains, aggregating types of ontologies within the same domain, and finding correspondences between ontologies within the same or similar domain.

Knowledge about transcription factor binding and regulation, target genes, cis-regulatory modules and topologically associating domains is not only defined by functional associations like biological processes or diseases, but also has a determinative genome location aspect.

In this seminar Prof. Jorge Mazza Rodrigues from the University of California, explains how deforestation in the Amazon rainforest, mainly due to cattle pasture, leads to increased nitrogen fixation and methane emissions. The Amazon Rainforest Microbial Observatory found abundant carbohydrate utilization genes in pastures. Rodrigues studies microbial diversification and its impact on global biogeochemical cycles.

A personal presentation describing the role of computational biology and bioinformatics in addressing state-of-the-art biomedical challenges. First, I will provide an overview of the transition between a Ph.D. in Mathematics to a postdoc in Computational Biology: How did it happen? What were the challenges? Secondly, I will briefly present several current case studies where computational biology (in several flavors) is core to understanding novel biological data related to multi-omic data analysis, spatial profiling, gene therapy, and more.

Abstract

The increased availability of biomedical data, particularly in the public domain, offers

Abstract

The 2016 Global Burden of Disease Study identifies chronic kidney disease (CKD) as the 11

I will present a data science pipeline which starts with Earth observation data arriving in the ground segment of a satellite mission and ends with a complete user application. I will first briefly present all the tools my group has been developing since 2010 for supporting the various stages of the pipeline. Then, I will concentrate on the recently developed system Strabo 2 which can store big geospatial data encoded in RDF and query them using the Open Geospatial Consortium standard GeoSPARQL. Strabo 2 is the only parallel and distributed RDF store available today that can manage terabytes of geospatial data efficiently.

Abstract

In the coming years, expansion of crewed space exploration beyond low Earth orbit, to the

Bioinformatics experts, Don’t miss the opportunity to collaborate with researchers and field professionals in the BioHackathonMENA2023 event. BioHackathon events involve a large number of people that meet on-site to discuss ideas and implement projects in a collaborative manner during intensive coding sessions.

We propose a structured latent ODE model that explicitly captures system input variations within its latent representation. Building on a static latent variable specification, our model learns (independent) stochastic factors of variation for each input to the system, thus separating the effects of the system inputs in the latent space. This approach provides actionable modeling through the controlled generation of time-series data for novel input combinations (or perturbations). Additionally, we propose a flexible approach for quantifying uncertainties, leveraging a quantile regression formulation.

Understanding and managing microbiomes offers promising perspectives for all health issues. The synergistic impact of anthropogenic factors on the inter-linked plant microbiome such as biodiversity loss, pollution, ozone depletion, climate change and changing biogeochemical cycles is less understood. Recent studies indicated a general shift of the plant microbiota characterized by a decrease of evenness and specificity, and an increase of r-strategist and hypermutator prevalence as well as antimicrobial resistance. the microbiome and resistome are interconnected, and should be managed by microbiome management together.

In this talk, I will first present how the application of phylogenetic and phylodynamic methods to whole genome sequencing data of multidrug resistant bacterial pathogens provided an in-depth understanding of the epidemiology and evolution of these strains on epidemiological time scales. I will then discuss the characterization of the genomic repertoire of bacterial traits using a combination of machine learning, whole genome sequencing and large-scale phenotypic assays. I will then present the leverage of predictive modelling to predict bacterial features, e.g. antimicrobial resistance, growth, and horizontal gene transfer, from genomic biomarkers. I will finally discuss how large-scale phenotypic assays enabled us to identity genes underlying morphogenesis and biofilm formation.

Biological systems are distinguished by their enormous complexity and variability. That is why mathematical modelling and computational simulation of those systems is very difficult, in particular thinking of detailed models which are based on first principles. The difficulties start with geometric modelling which needs to extract basic structures from highly complex and variable phenotypes, on the other hand also has to take the statistic variability into account.

The Computational Bioscience Research Center (CBRC) at King Abdullah University of Science and Technology (KAUST) is pleased to host the International Conference on Bioinformatics 2022 (InCoB2022).  This year’s conference theme will be “Accelerating innovation to meet biological challenges: The role of bioinformatics”.

Abstract

In the Anthropocene, plastic pollution is a worldwide concern that must be tackled from d

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.

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.

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.