AI and Omics Converge to Decode the Gene Regulatory Logic of the Brain

The human brain runs on a gene regulatory landscape of staggering complexity, orchestrated across thousands of cell types and every stage of development. A new review published in Brain Informatics argues that artificial intelligence has become indispensable for making sense of it. Written by Aranyak Goswami, Rushikesh R. Lagad and Shakil Rafi, the paper synthesizes how computational frameworks in neuro-omics have evolved from simple descriptive statistics into causal graph neural networks and large-scale foundation models, and it maps where these tools are already reshaping our understanding of Alzheimer’s disease, schizophrenia and epilepsy.

The authors frame the problem as an informatics challenge of scale. Single-cell multi-omics and spatial transcriptomics now generate datasets of unprecedented dimensionality, but the so-called curse of dimensionality and inherent biological noise overwhelm traditional statistical approaches, which often fail to capture the non-linear dependencies linking epigenetic modifications, transcriptional output and emergent neural phenotypes. High-throughput omics and neuroimaging each provide high-resolution snapshots of brain biology, yet bridging the gap between molecular dynamics and macro-scale brain architecture remains one of the field’s hardest problems.

To organize a crowded methodological landscape, the review proposes viewing integrative pipelines as a series of complementary functional layers rather than isolated techniques. The first layer is representation learning, in which variational autoencoders and manifold-learning methods such as UMAP and t-SNE compress high-dimensional, noisy data into compact latent embeddings while preserving biological structure. The second is cross-modal fusion and alignment, which uses multimodal transformers or canonical correlation analysis to integrate heterogeneous data types, including genomics, epigenomics and imaging, into shared latent spaces.

The third and fourth layers move from description toward mechanism. Network and causal inference employs graph neural networks and Bayesian frameworks to infer the regulatory edges connecting genes, cell types and macroscopic brain regions. Dynamics and interpretability then applies temporal models, such as hidden Markov models and trajectory inference, to capture how these systems evolve over time, before explainable AI translates model predictions into mechanistic hypotheses that can be tested in the laboratory or clinic. The authors describe this as a coherent discovery pipeline that carries a signal from raw molecular data all the way to a prioritized hypothesis.

Within this framework, the review identifies three successive eras of increasing explanatory power. A statistical era established descriptive co-expression modules through weighted gene co-expression network analysis and regularized regression methods such as LASSO and ElasticNet, providing a baseline for gene-module discovery. A deep learning era introduced non-linear representation learning through variational autoencoders, the single-cell model scVI and graph neural networks, enabling single-cell resolution and multimodal fusion. The field now operates at what the authors call the causal and foundation era, characterized by causal graph neural networks, multimodal transformers and large-scale foundation models such as scGPT and Geneformer that support cross-scale prediction and mechanistic interpretation.

Methodological choice, the review stresses, is driven by the constraints of each data modality. Bulk omics, with larger sample sizes but lower resolution, favors regularized statistical models and classical machine learning. Single-cell omics, with its cellular heterogeneity and technical dropouts, demands non-linear embedding techniques. Spatial omics, which preserves the physical coordinates of tissue, requires spatially aware models such as graph convolutional networks that treat tissue geometry as an essential feature of regulatory context. Hybrid strategies frequently outperform any single approach: pairing a variational autoencoder with a graph neural network enables latent-space regulatory inference, while combining multimodal fusion with explainable AI supports biomarker discovery in clinical cohorts. Small sample sizes favor prior-driven Bayesian models, and longitudinal datasets call for temporal modeling.

The most technically thorny section concerns cross-scale integration, the attempt to link molecules to brain networks. A voxel in a functional MRI scan typically aggregates thousands of cells, masking the cell-type-specific contributions that molecular data reveal. Temporal discordance compounds the problem: gene expression dynamics unfold on a very different time scale than blood-oxygen-level-dependent signals. The authors suggest that neural ordinary differential equations, which model continuous temporal evolution of biological layers, may help bridge the gap, with interpretable biomarker selection converting predictions into testable hypotheses. Three emerging routes are highlighted: hierarchical modeling from genes to circuits to functional hubs, multi-resolution graph neural networks that jointly model molecular and connectivity graphs, and cross-modal attention architectures that weigh molecular markers against macroscopic imaging phenotypes.

The disease applications show what this pipeline delivers. In Alzheimer’s disease, deep learning applied to single-nucleus RNA sequencing has characterized the transition of homeostatic microglia into disease-associated microglia, and causal graph neural networks have identified master transcription factors, notably SPI1, also known as PU.1, that coordinate the switch toward pro-inflammatory states. AI-driven integration of PET imaging with transcriptomics has further revealed regional vulnerability signatures dictated by synaptic maintenance gene expression, offering a molecular account of why tau pathology strikes some brain regions and not others. In schizophrenia, AI-integrated multi-omics shows that GWAS risk variants are enriched in excitatory neuronal lineages during early mid-gestation, and representation learning that fuses polygenic risk scores with longitudinal fetal brain atlases maps how these variants disrupt synaptic pruning, implicating the MHC region and the complement system in synaptic elimination. In epilepsy, multi-view learning links EEG-derived connectivity features with spatial transcriptomics of resected focal tissue, and hidden Markov and graph-based approaches have pinpointed seizure hubs where disrupted GABAergic interneuron gene expression permits runaway excitation.

The review is candid about the field’s unresolved controversies. Deep learning’s black-box nature hinders translation of high-accuracy diagnostic models into actionable drug targets, prompting emerging mechanistic explainable AI frameworks that enforce biological constraints on hidden layers. Pseudotime analysis, which reconstructs temporal trajectories from static snapshots of different donors, may fail to capture the idiosyncratic variability of individual disease progression, creating an urgent need for longitudinal multi-omic datasets from single cohorts to validate AI-generated clocks of brain aging. Aggressive batch-correction methods such as Harmony and LIGER, while necessary for large-scale integration, risk over-smoothing rare biological signals, a particular hazard in neuropsychiatry where subtle cell-state transitions may be dismissed as technical noise.

Looking forward, the authors call for unsupervised causal discovery algorithms that identify regulatory motifs directly from single-cell multiome data rather than relying on curated databases biased toward well-studied pathways, distinguishing driver master regulators from passenger transcriptional changes. They envision person-specific digital twins built from longitudinal omics and neuroimaging, using physics-informed neural networks to predict how interventions alter the transcriptional landscape of cortical circuits, and federated learning that trains models across institutions without moving sensitive patient data, an approach suited to rare neurological conditions. A dedicated section on the gut-brain axis describes physics-informed models simulating how microbial metabolites such as short-chain fatty acids cross the blood-brain barrier and act as epigenetic modifiers through HDAC inhibition in the prefrontal cortex and hippocampus. The review closes with a call for clinically interpretable decision-support systems in which explainable AI offers clinicians a quantifiable mechanistic path, not merely a risk score, supported by standardized coordinate systems, spatial transcriptomic benchmarks and robust batch-correction strategies that can serve as a common language for biologists, data scientists and clinicians alike.

Subject of Research: Computational and AI methods for integrating neuronal multi-omics data with brain network analysis in neurological disorders

Article Title: Decoding neuronal gene expression: integrative insights from omics and AI

Article References: Goswami, A., Lagad, R. R., & Rafi, S. (2026). Decoding neuronal gene expression: integrative insights from omics and AI. Brain Informatics, 13(1), Article 36. https://doi.org/10.1186/s40708-026-00323-z

Image Credits: AI Generated

DOI: 10.1186/s40708-026-00323-z

Keywords: neuro-omics, gene regulation, artificial intelligence, graph neural networks, Alzheimer's disease, schizophrenia, epilepsy, spatial transcriptomics, single-cell RNA sequencing, explainable AI, foundation models, gut-brain axis