Current and future directions in network biology
arXiv:2309.08478 · doi:10.1093/bioadv/vbae099
Abstract
Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These challenges stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology and highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on the future directions of network biology. Additionally, we offer insights into scientific communities, educational initiatives, and the importance of fostering diversity within the field. This paper establishes a roadmap for an immediate and long-term vision for network biology.
52 pages, 6 figures, 1 table
References in corpus (49)
- Modularity and community structure in networks
- Community detection in graphs
- T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction
- Multilayer Networks
- A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
- ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context
- Community Structure in Time-Dependent, Multiscale, and Multiplex Networks
- E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
- Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods
- Networks beyond pairwise interactions: structure and dynamics
- Modeling polypharmacy side effects with graph convolutional networks
- Unifying Large Language Models and Knowledge Graphs: A Roadmap
- BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining
- Mathematical Formulation of Multi-Layer Networks
- Biological network comparison using graphlet degree distribution
- Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities
- Predicting multicellular function through multi-layer tissue networks
- Graph Self-Supervised Learning: A Survey
- Graph Transformer Networks
- Dynamic Graph Convolutional Networks
- An expanded evaluation of protein function prediction methods shows an improvement in accuracy
- Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations
- Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting
- Graph Representation Learning in Biomedicine
- Representing higher-order dependencies in networks
- Epidemic spreading on complex networks with community structures
- Network Enhancement: a general method to denoise weighted biological networks
- Learning Module Networks
- General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian
- Causal Network Models of SARS-CoV-2 Expression and Aging to Identify Candidates for Drug Repurposing
- wTO: an R package for computing weighted topological overlap and consensus networks with an integrated visualization tool
- MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach
- Uncertainty Aware Semi-Supervised Learning on Graph Data
- BeWith: A Between-Within Method to Discover Relationships between Cancer Modules via Integrated Analysis of Mutual Exclusivity, Co-occurrence and Functional Interactions
- Semi-Supervised Hierarchical Graph Classification
- Neural Algorithmic Reasoning
- Comparing multiple networks using the Co-expression Differential Network Analysis (CoDiNA)
- Long Range Graph Benchmark
- Uncovering the nutritional landscape of food
- Unveiling new disease, pathway, and gene associations via multi-scale neural networks
- Temporal Graph Benchmark for Machine Learning on Temporal Graphs
- Graph Denoising Diffusion for Inverse Protein Folding
- Pairwise versus multiple network alignment
- Data-driven network alignment
- Uncertainty Quantification over Graph with Conformalized Graph Neural Networks
- NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics
- 3D molecule generation by denoising voxel grids
- AVIDa-hIL6: A Large-Scale VHH Dataset Produced from an Immunized Alpaca for Predicting Antigen-Antibody Interactions
- Full-Atom Protein Pocket Design via Iterative Refinement