Skip to content

Inference Concepts

Inference is the process of using a trained DNA language model to generate predictions, analyze sequences, or perform downstream tasks on new DNA data. This document covers the fundamental concepts and methods involved in inference with DNA language models.

What is Inference?

Inference refers to the process of applying a trained model to new, unseen data to make predictions or generate outputs. In the context of DNA language models, inference involves:

  • Sequence Analysis: Analyzing DNA sequences to understand their properties
  • Prediction Generation: Generating predictions about sequence characteristics
  • Feature Extraction: Extracting meaningful representations from DNA sequences
  • Downstream Tasks: Performing specific biological tasks using the model's learned representations

Key Components of Inference

1. Model Loading and Initialization

Before inference can begin, the trained model must be loaded into memory. This involves:

  • Model Restoration: Loading the trained model weights and architecture from storage
  • Memory Allocation: Allocating sufficient memory for the model and input data
  • Device Placement: Placing the model on appropriate computational devices (CPU, GPU, or distributed systems)
  • State Configuration: Setting the model to evaluation mode to disable training-specific behaviors

2. Input Preprocessing

DNA sequences must be properly formatted and tokenized before feeding into the model:

  • Sequence Cleaning: Removing invalid characters and normalizing sequences
  • Tokenization: Converting DNA sequences into model-compatible tokens
  • Padding/Truncation: Ensuring consistent input lengths for batch processing
  • Batch Preparation: Organizing multiple sequences for efficient processing

3. Forward Pass

The core inference step where the model processes the input:

  • Input Processing: Feeding preprocessed sequences through the model
  • Computation: Performing matrix operations and neural network computations
  • Output Generation: Producing raw model outputs (logits, probabilities, or embeddings)
  • Memory Management: Efficiently managing computational resources during processing

4. Output Processing

Transform raw model outputs into meaningful results:

  • Logits Processing: Converting raw scores to probabilities using activation functions
  • Post-processing: Applying task-specific transformations and filtering
  • Result Formatting: Structuring outputs for downstream use and interpretation
  • Confidence Scoring: Assessing the reliability of model predictions