Attention-Based Multi-Modal Integration

Each omics layer first undergoes intra-modality self-attention to capture internal dependencies before cross-modal fusion. For example, a DNA sequence encoder uses self-attention to identify distal regulatory elements within a 100kb window, while a parallel RNA-seq encoder captures co-expression modules. This ensures that each modality's internal structure is fully resolved before inter-modal comparison, preventing premature fusion that could dilute modality-specific signals.

The cross-attention mechanism projects one modality as the query and another as keys and values, enabling selective information retrieval. In a gene expression prediction task, RNA-seq embeddings query DNA methylation keys to assess promoter silencing. The attention weights dynamically scale based on contextual relevance—methylation at CpG islands receives higher weight than distal regions when predicting transcription, implementing a biological prior through learned attention patterns.

Gating mechanisms control the flow of information from each modality encoder, allowing the model to suppress noisy or irrelevant inputs. A sigmoid gate computes a per-modality weight between 0 and 1 based on the current prediction context:

• Tumor classification: Somatic mutation data receives gate values near 1.0
• Same model, normal tissue: Mutation gates drop to near 0.0, prioritizing expression data This prevents the model from being misled by modalities that carry no signal for the specific inference task.

Attention operates at multiple biological resolutions simultaneously:

• Token-level: Individual nucleotide or amino acid positions
• Gene-level: Aggregated per-gene embeddings from variant and expression data
• Pathway-level: Attention over predefined gene sets from Reactome or KEGG This hierarchical architecture mirrors biological organization, allowing the model to attend to fine-grained sequence features while also considering systems-level pathway activity when making phenotype predictions.

The attention mechanism learns to dynamically reweight modalities based on the specific prediction task encoded in a context vector. For drug response prediction:

• Targeted therapy: Attention heavily weights kinase mutation status and protein structure features
• Chemotherapy: Attention shifts toward DNA repair pathway expression and copy number variation This task-conditioned attention eliminates the need for separate models per prediction context, enabling a single architecture to handle diverse downstream tasks through learned routing.

During training, entire omics layers are randomly zeroed out with probability p=0.2-0.4, forcing the attention mechanism to learn redundant representations across modalities. This regularization technique ensures the model gracefully handles missing clinical assays at inference time—if proteomic data is unavailable, the attention weights automatically redistribute to transcriptomic and epigenomic features without requiring imputation. The result is a deployment-robust model that functions with partial input profiles.

The preprocessing strategy of converting raw biological data from different sources into discrete or continuous tokens using modality-specific encoders before feeding them into a unified multi-modal transformer. Key considerations include:

• DNA sequences: k-mer tokenization or byte-pair encoding
• RNA expression: log-normalized count vectors or binned expression levels
• Epigenomic tracks: continuous-valued signal windows
• Protein abundance: normalized intensity values

Each modality requires its own embedding projection to map into a shared dimensional space before attention-based fusion can occur.

A self-supervised training paradigm that pulls paired omics profiles together in the latent space while pushing unpaired profiles apart. Applied to single-cell data, this technique aligns RNA expression with protein abundance measurements from the same cell (CITE-seq). The InfoNCE loss maximizes mutual information between modalities, creating a joint embedding space where cross-modal retrieval becomes possible. This approach excels at learning robust representations even when modalities exhibit different statistical properties and noise distributions.

A neural gating mechanism that controls information flow from distinct modality-specific encoders, allowing the model to dynamically suppress noisy or irrelevant omics inputs. The gate computes a sigmoid-activated weight for each modality based on learned relevance criteria:

• Low-quality samples: automatically down-weighted
• Missing assays: gated to zero without breaking the forward pass
• Task-specific relevance: methylation data suppressed for tasks where it provides no signal

This provides robustness against real-world clinical data heterogeneity where not all assays are equally reliable.

A regularization technique where entire data modalities are randomly zeroed out during training to force the model to learn robust representations that handle missing clinical assays. Key benefits include:

• Missing modality resilience: model learns to infer from partial inputs
• Redundancy exploitation: prevents over-reliance on any single omics layer
• Clinical deployment readiness: real-world patient data often lacks complete multi-omic profiles

During inference, the model can operate with any subset of modalities, making it practical for heterogeneous healthcare datasets.

An architecture that explicitly models multi-modal interactions by computing the outer product between modality-specific embeddings, capturing high-order multiplicative correlations across omics layers. Unlike simple attention-based fusion that models additive interactions, tensor fusion captures multiplicative cross-modal relationships:

• Gene expression × methylation interactions
• RNA abundance × protein level synergies
• Epigenomic state × transcriptomic output dependencies

The resulting tensor is flattened and passed through subsequent layers, though dimensionality explosion requires careful regularization for high-dimensional genomic inputs.