A5055886613- Cardiac Structural Anomalies and Repair
- Cardiovascular Issues in Pregnancy
- Congenital Heart Disease Studies
- Health and Conflict Studies
- RNA and protein synthesis mechanisms
- Innovative Teaching and Learning Methods
- Scientific Computing and Data Management
- Parkinson's Disease Mechanisms and Treatments
- Topic Modeling
- Open Education and E-Learning
- Computational Drug Discovery Methods
- Protein Structure and Dynamics
- Advanced biosensing and bioanalysis techniques
- Machine Learning in Bioinformatics
- Migration, Health and Trauma
- Distributed and Parallel Computing Systems
- CRISPR and Genetic Engineering
- Global Maternal and Child Health
- Enzyme Structure and Function
- Biofuel production and bioconversion
- Innovative Microfluidic and Catalytic Techniques Innovation
University of Cambridge
2023-2025
American University of Beirut
2023
Chelsea and Westminster Hospital
2020
Imperial College London
2020
Abstract Deep learning methods of predicting protein structures have reached an accuracy comparable to that high-resolution experimental methods. It is thus possible generate accurate models the native states hundreds millions proteins. An open question, however, concerns whether these advances can be translated disordered proteins, which should represented as structural ensembles because their heterogeneous and dynamical nature. To address this problem, we introduce AlphaFold-Metainference...
In computational physics, chemistry, and biology, the implementation of new techniques in shared open-source software lowers barriers to entry promotes rapid scientific progress. However, effectively training users presents several challenges. Common methods like direct knowledge transfer in-person workshops are limited reach comprehensiveness. Furthermore, while COVID-19 pandemic highlighted benefits online training, traditional tutorials can quickly become outdated may not cover all...
The high attrition rate in drug discovery pipelines is an especially pressing issue for Parkinson's disease, which no disease-modifying drugs have yet been approved. Numerous clinical trials targeting α-synuclein aggregation failed, at least part due to the challenges identifying potent compounds preclinical investigations. To address this problem, we present a machine learning approach that combines generative modeling and reinforcement identify small molecules perturb kinetics of manner...
This study aims to assess whether the characteristics, management and outcomes of women varied between Syrian Palestinian refugees, migrant other nationalities Lebanese giving birth at a public tertiary centre in Beirut, Lebanon.
Summary The role of the SNCA gene locus in driving Parkinson’s disease (PD) through rare and common genetic variation is well-recognized, but transcriptional diversity vulnerable cell types remains unclear. We performed long-read RNA sequencing human dopaminergic neurons show that annotated transcripts account for only 5% expression. Rather, majority expression (75%) at originates from with alternative 5’ 3’ untranslated regions. Importantly, 10% encoding open reading frames not previously...
Abstract Deep learning methods of predicting protein structures have reached an accuracy comparable to that high-resolution experimental methods. It is thus possible generate accurate models the native states hundreds millions proteins. An open question, however, concerns whether these advances can be translated disordered proteins, which should represented as structural ensembles because their heterogeneous and dynamical nature. To address this problem, we introduce AlphaFold-Metainference...
Molecular Dynamics (MD) simulations provide accurate descriptions of the motions molecular systems, yet their computational demands pose significant challenges in applications biology and materials science. Given success deep learning methods a wide range fields, timely question concerns whether these could be leveraged to improve efficiency MD simulations. To investigate this possibility, we introduce Language Models (MDLMs), enable generation trajectories. In present implementation, an...
In computational physics, chemistry, and biology, the implementation of new techniques in a shared open source software lowers barriers to entry promotes rapid scientific progress. However, effectively training users presents several challenges. Common methods like direct knowledge transfer in-person workshops are limited reach comprehensiveness. Furthermore, while COVID-19 pandemic highlighted benefits online training, traditional tutorials can quickly become outdated may not cover all...