Integrating Large Language Models for Genetic Variant Classification

The classification of genetic variants, particularly Variants of Uncertain Significance (VUS), poses a significant challenge in clinical genetics and precision medicine. Large Language Models (LLMs) have emerged as transformative tools in this realm. These models can uncover intricate patterns and predictive insights that traditional methods might miss, thus enhancing the predictive accuracy of genetic variant pathogenicity. This study investigates the integration of state-of-the-art LLMs, including GPN-MSA, ESM1b, and AlphaMissense, which leverage DNA and protein sequence data alongside structural insights to form a comprehensive analytical framework for variant classification. Our approach evaluates these integrated models using the well-annotated ProteinGym and ClinVar datasets, setting new benchmarks in classification performance. The models were rigorously tested on a set of challenging variants, demonstrating substantial improvements over existing state-of-the-art tools, especially in handling ambiguous and clinically uncertain variants. The results of this research underline the efficacy of combining multiple modeling approaches to significantly refine the accuracy and reliability of genetic variant classification systems. These findings support the deployment of these advanced computational models in clinical environments, where they can significantly enhance the diagnostic processes for genetic disorders, ultimately pushing the boundaries of personalized medicine by offering more detailed and actionable genetic insights.

Escaping the Forest: Sparse Interpretable Neural Networks for Tabular Data

Tabular datasets are widely used in scientific disciplines such as biology. While these disciplines have already adopted AI methods to enhance their findings and analysis, they mainly use tree-based methods due to their interpretability. At the same time, artificial neural networks have been shown to offer superior flexibility and depth for rich and complex non-tabular problems, but they are falling behind tree-based models for tabular data in terms of performance and interpretability. Although sparsity has been shown to improve the interpretability and performance of ANN models for complex non-tabular datasets, enforcing sparsity structurally and formatively for tabular data before training the model, remains an open question. To address this question, we establish a method that infuses sparsity in neural networks by utilising attention mechanisms to capture the features' importance in tabular datasets. We show that our models, Sparse TABular NET or sTAB-Net with attention mechanisms, are more effective than tree-based models, reaching the state-of-the-art on biological datasets. They further permit the extraction of insights from these datasets and achieve better performance than post-hoc methods like SHAP.

SwiftDossier: Tailored Automatic Dossier for Drug Discovery with LLMs and Agents

The advancement of artificial intelligence algorithms has expanded their application to several fields such as the biomedical domain. Artificial intelligence systems, including Large Language Models (LLMs), can be particularly advantageous in drug discovery, which is a very long and expensive process. However, LLMs by themselves lack in-depth knowledge about specific domains and can generate factually incorrect information. Moreover, they are not able to perform more complex actions that imply the usage of external tools. Our work is focused on these two issues. Firstly, we show how the implementation of an advanced RAG system can help the LLM to generate more accurate answers to drug-discovery-related questions. The results show that the answers generated by the LLM with the RAG system surpass in quality the answers produced by the model without RAG. Secondly, we show how to create an automatic target dossier using LLMs and incorporating them with external tools that they can use to execute more intricate tasks to gather data such as accessing databases and executing code. The result is a production-ready target dossier containing the acquired information summarized into a PDF and a PowerPoint presentation.