arXiv subjects

Marek Mutwil

Publications and source records attributed to Marek Mutwil.

4 recordsLinked to original sources

arXiv2024

The gene function prediction challenge: large language models and knowledge graphs to the rescue

Elucidating gene function is one of the ultimate goals of plant science. Despite this, only ~15% of all genes in the model plant Arabidopsis thaliana have comprehensively experimentally verified functions. While bioinformatical gene function prediction approaches can guide biologists in their experimental efforts, neither the performance of the gene function prediction methods nor the number of experimental characterisation of genes has increased dramatically in recent years. In this review, we will discuss the status quo and the trajectory of gene function elucidation and outline the recent advances in gene function prediction approaches. We will then discuss how recent artificial intelligence advances in large language models and knowledge graphs can be leveraged to accelerate gene function predictions and keep us updated with scientific literature.

q-bio.MN↗

arXiv2024

Harnessing Big Data and Artificial Intelligence to Study Plant Stress

Life finds a way. For sessile organisms like plants, the need to adapt to changes in the environment is even more poignant. For humanity, the need to develop crops that can grow in diverse environments and feed our growing population is an existential one. The advent of the genomics era enabled the generation of high-throughput data and computational methods that serve as powerful hypothesis-generating tools to understand the genomic and gene functional basis of stress resilience. Today, the proliferation of artificial intelligence (AI) allows scientists to rapidly screen through high-throughput datasets to uncover elusive patterns and correlations, enabling us to create more performant models for prediction and hypothesis generation in plant biology. This review aims to provide an overview of the availability of large-scale data in plant stress research and discuss the application of AI tools on these large-scale datasets in a bid to develop more stress-resilient plants.

q-bio.QM↗

arXiv2024

Plant sesquiterpene lactones

Sesquiterpene lactones (STLs) are a prominent group of plant secondary metabolites predominantly found in the Asteraceae family and have multiple ecological roles and medicinal applications. This review describes the ecological significance of STLs, highlighting their roles in plant defense mechanisms against herbivory and as phytotoxins, alongside their function as environmental signaling molecules. We also cover the substantial role of STLs in medicine and their mode of action in health and disease. We discuss the biosynthetic pathways and the various modifications that make STLs one of the most diverse groups of metabolites. Finally, we discuss methods in identifying and predicting STL biosynthesis pathways.

q-bio.QM↗

arXiv2024

Large Language Models in Plant Biology

Large Language Models (LLMs), such as ChatGPT, have taken the world by storm and have passed certain forms of the Turing test. However, LLMs are not limited to human language and analyze sequential data, such as DNA, protein, and gene expression. The resulting foundation models can be repurposed to identify the complex patterns within the data, resulting in powerful, multi-purpose prediction tools able to explain cellular systems. This review outlines the different types of LLMs and showcases their recent uses in biology. Since LLMs have not yet been embraced by the plant community, we also cover how these models can be deployed for the plant kingdom.

q-bio.GN↗