Great question—let’s dive into whether your current mapping works and what better alternatives exist for preprocessing A/G/C/T data.

Is the 1/2/3/4 mapping suitable for dense NNs? #

Technically, this mapping will work in the sense that your model can process the numeric inputs and learn to make predictions. However, it introduces a critical flaw: it imposes an arbitrary ordinal relationship between the bases that doesn’t exist in reality.

For example, your model might incorrectly learn that "A (1) is closer to G (2) than it is to T (4)" or that "higher numbers correspond to some meaningful hierarchy." Since A, G, C, T are categorical (unordered) values, this artificial ordering can mislead the model into picking up spurious patterns, hurting its performance and interpretability.

Better Preprocessing Methods #

Here are two far more effective approaches tailored to categorical nucleotide data:

1. One-Hot Encoding (Best for Single Base Inputs) #

This is the gold standard for unordered categorical values. Each base gets its own binary feature column:

• A → [1, 0, 0, 0]
• G → [0, 1, 0, 0]
• C → [0, 0, 1, 0]
• T → [0, 0, 0, 1]

This eliminates any false ordinal relationships, and dense neural networks handle sparse binary inputs perfectly. It’s simple to implement—most ML frameworks (like TensorFlow/PyTorch or scikit-learn) have built-in functions to handle this with just a few lines of code.

2. Embedding Layers (Ideal for Sequence Data) #

If you plan to eventually work with longer nucleotide sequences (instead of single bases), an embedding layer is a smarter choice. Instead of one-hot encoding (which creates high-dimensional sparse vectors), embeddings learn low-dimensional dense vectors for each base. These vectors can capture subtle relationships between bases (e.g., how A and T pair, or how certain mutations relate) as the model trains.

Even for single bases, embeddings work, but one-hot is more straightforward and efficient for this specific use case.

Quick Note on Your Data Cleaning #

You mentioned removing elements with multi-letter combinations—if those combinations are meaningful (like mutations or codons), you don’t have to discard them! You can extend the same preprocessing logic: for example, a 3-letter codon could be one-hot encoded into a 64-dimensional vector (4^3 possible combinations) or mapped to an embedding.

内容的提问来源于stack exchange，提问作者RektAngle