---
title: Transformer Architecture
type: method
tags: [deep-learning, attention-mechanism, neural-networks]
introduced_by: "[[Vaswani2017-Attention-Is-All-You-Need]]"
applications: [protein-structure, sequence-analysis, nlp]
---

# [[Transformer Architecture]]

## Overview

Self-attention mechanism for sequence [[Processing]]. [[Core]] of modern [[Deep Learning]] for biological sequences.

## [[Key Components]]

- **Self-Attention**: Relates positions in sequence to compute representation
- **[[Multi]]-[[Head Attention]]**: Parallel attention layers for different [[Features]]
- **[[Positional Encoding]]**: Injects sequence order information

## [[Biological Applications]]

### Protein Structure Prediction
Used in [[Jumper2021-AlphaFold]] for MSA processing. Attention learns evolutionary couplings.

### Sequence Motif Discovery
Applied to DNA/RNA sequences for regulatory element finding.

## [[Implementation Notes]]

```python
# PyTorch implementation in experiments
import torch.nn as nn
class ProteinTransformer(nn.Module):
    ...
```

## [[Related Methods]]

- [[Convolutional Neural Networks]] - Earlier approach
- [[Recurrent Neural Networks]] - Sequential processing
- [[Graph Neural Networks]] - Structure-aware [[Learning]]
