Researchers at the Rosalind Franklin Institute are using generative AI, including GANs, to augment limited biological datasets, specifically mirtron data from mirtronDB. The synthetic data created mimics real-world samples, facilitating more comprehensive training of machine learning models, leading to improved mirtron identification tools. They also plan to apply Large Language Models (LLMs) to predict unknown patterns in sequence and structure biology problems. Why it matters: This research explores AI techniques to tackle data scarcity in biological research, potentially accelerating discoveries in noncoding RNA and transposable elements.
A KAUST team developed piRNAi, a gene-silencing tool in nematode worms using synthetic RNA sequences interacting with the piRNA pathway. They successfully silenced genes involved in sex determination and other functions, demonstrating multiplexed gene silencing. The gene silencing lasted for varying durations across generations, up to six generations. Why it matters: This expands the molecular toolkit for gene manipulation and offers potential therapeutic applications in humans, given the presence of the same gene-silencing pathway.
MBZUAI researchers collaborated with Carnegie Mellon University and the Broad Institute of MIT and Harvard to develop a new statistical method for analyzing data used for gene regulatory network inference. The method addresses the challenge of distinguishing true zero expression values from dropouts in single-cell RNA sequencing data. This research will be presented at the Twelfth International Conference on Learning Representations (ICLR 2024). Why it matters: Improving gene regulatory network inference can lead to better understanding of disease mechanisms and inform the development of new medicines.
Daisuke Kihara from Purdue University presented a seminar at MBZUAI on using deep learning for biomolecular structure modeling. His lab is developing 3D structure modeling methods, especially for cryo-electron microscopy (cryo-EM) data. They are also working on RNA structure prediction and peptide docking using deep neural networks inspired by AlphaFold2. Why it matters: Applying advanced deep learning techniques to biomolecular structure prediction can accelerate drug discovery and our understanding of molecular functions.
The paper introduces Guided Deep List, a tool for automating the generation of epidemiological line lists from open source reports. The tool uses distributed vector representations and dependency parsing to extract tabular data on disease outbreaks. It was evaluated on MERS outbreak data in Saudi Arabia, demonstrating improved accuracy over baseline methods and enabling epidemiological inferences.
KAUST professors Samir Hamdan and Nina Fedoroff collaborated on research published in Nucleic Acids Research focusing on microRNA (miRNA) biogenesis in plants. The study examined miRNA production in Arabidopsis thaliana and found that the protein SERRATE (SE) is integral to the processing of pri-miRNA by DCL1. They characterized the interactions of SE with RNA and DCL1, elucidating the mechanism by which SE promotes DCL1 activity. Why it matters: Understanding miRNA biogenesis could help modify crop plants to better tolerate stressful conditions, potentially increasing crop yields and productivity in the region.
KAUST researchers are analyzing the SARS-CoV-2 genome to identify potential targets for treatment and vaccine development. They are using the KAUST Metagenome Analysis Platform (KMAP) and the university's supercomputer to compare and analyze genomic data. The research focuses on identifying key genes for detection and treatment of COVID-19. Why it matters: This research contributes to the global effort to combat the pandemic and highlights KAUST's capabilities in genomic data analysis and computational bioscience.
Pierre Baldi from UC Irvine presented applications of AI to biomedicine, covering molecular-level analysis of circadian rhythms, real-time polyp detection in colonoscopy videos, and prediction of post-operative adverse outcomes. He discussed integrating AI in future AI-driven hospitals. The presentation was likely part of a panel discussion hosted by MBZUAI in collaboration with the Manara Center for Coexistence and Dialogue. Why it matters: This highlights the growing interest in AI applications within the healthcare sector in the UAE, particularly through institutions like MBZUAI.