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Multilayer Perceptron (MLP)

Definition
AI-generated

A multilayer perceptron (MLP) is a feedforward neural network of fully connected layers with nonlinear activations; it maps a fixed-size input vector to outputs and is often used as a head on top of embeddings from CNNs, RNNs, or transformers.

Topics
LLM and Agents
Synonyms
Feedforward neural network
Related
Feedforward Network (FFN) Backpropagation Deep Learning

Why it matters in GWAS

MLPs appear in polygenic deep models on SNP panels, in variant scoring heads, and as decoders in autoencoders; even shallow MLPs can overfit related individuals or population structure if validation is not carefully structured.

Example usage

"The SNP-based predictor concatenated PCs with dosages and passed them through a three-layer MLP with dropout."

Feedforward Network (FFN) Backpropagation Deep Learning Neural Network Polygenic Risk Score (PRS)
Related-term network

Click any node to open that term page.

Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Hornik K, Stinchcombe M, White H. (1989). Multilayer feedforward networks are universal approximators. Neural Netw.
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