Large Language Models in Bioinformatics: A Survey

In recent years, significant progress has been made in the field of protein function prediction with the development of various machine-learning approaches. However, most existing methods formulate the task as a multi-classification problem, i.e. assigning predefined labels to proteins. In this work, we propose a novel approach, Prot2Text, which predicts a protein's function in a free text style, moving beyond the conventional binary or categorical classifications. By combining Graph Neural Networks(GNNs) and Large Language Models(LLMs), in an encoder-decoder framework, our model effectively integrates diverse data types including protein sequence, structure, and textual annotation and description. This multimodal approach allows for a holistic representation of proteins' functions, enabling the generation of detailed and accurate functional descriptions. To evaluate our model, we extracted a multimodal protein dataset from SwissProt, and demonstrate empirically the effectiveness of Pr …

Gene essentiality, the necessity of a specific gene for the survival of an organism, is crucial to our understanding of cellular processes and identifying drug targets. Experimental determination of gene essentiality requires large growth screens that are time-consuming and expensive, motivating the development of in-silico approaches. Existing methods predominantly utilise flux balance analysis (FBA), a constraint-based optimisation algorithm; however, they are fundamentally limited by the necessity of a predefined cellular objective function. This requirement introduces an element of observer bias, as the objective function often reflects the researcher's assumptions rather than the cell's biological goals. Here, we present FluxGAT, a graph neural network (GNN) model capable of predicting gene essentiality directly from graphical representations of flux sampling data. Flux sampling removes the need for objective functions, thereby eliminating observer bias. FluxGAT leverages the uniq …

Graph classification is essential for understanding complex biological systems, where molecular structures and interactions are naturally represented as graphs. Traditional graph neural networks (GNNs) perform well on static tasks but struggle in dynamic settings due to catastrophic forgetting. We present Perturbed and Sparsified Continual Graph Learning (PSCGL), a robust and efficient continual graph learning framework for graph data classification, specifically targeting biological datasets. We introduce a perturbed sampling strategy to identify critical data points that contribute to model learning and a motif-based graph sparsification technique to reduce storage needs while maintaining performance. Additionally, our PSCGL framework inherently defends against graph backdoor attacks, which is crucial for applications in sensitive biological contexts. Extensive experiments on biological datasets demonstrate that PSCGL not only retains knowledge across tasks but also enhances the effi …

In recent years, tasks of machine learning ranging from image processing & audio/video analysis to natural language understanding have been transformed by deep learning. The data content in all these scenarios are expressed via Euclidean space. However, a considerable amount of application data is structured in non-Euclidean space and is expressed as graphs, e.g. dealing with complicated interactions & object interdependencies. Modelling physical systems, learning molecular signatures, identifying protein interactions and predicting diseases involve utilising a model that can adapt from graph data. Graph neural networks (GNNs), specified as artificial-neural models, employ message transmission between graph nodes to represent graph dependencies and are primarily used in the non-Euclidean domain. Variants of GNN like Graph Recurrent Networks (GRN), Graph Auto Encoder (GAE), Graph Convolution Networks (GCN), Graph Adversarial Methods & Graph Reinforcement learning have exhibited breakthr …

The recent development of high-throughput sequencing creates a large collection of multi-omics data, which enables researchers to better investigate cancer molecular profiles and cancer taxonomy based on molecular subtypes. Integrating multi-omics data has been proven to be effective for building more precise classification models. Current multi-omics integrative models mainly use early fusion by concatenation or late fusion based on deep neural networks. Due to the nature of biological systems, graphs are a better representation of bio-medical data. Although few graph neural network (GNN) based multi-omics integrative methods have been proposed, they suffer from three common disadvantages. One is most of them use only one type of connection, either inter-omics or intra-omic connection; second, they only consider one kind of GNN layer, either graph convolution network (GCN) or graph attention network (GAT); and third, most of these methods lack testing on a more complex cancer classifi …