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Recurrent Neural Network (RNN)

Definition
AI-generated

A recurrent neural network (RNN) processes sequential inputs with hidden states updated at each time step, sharing parameters across positions; practical variants such as LSTM and GRU gates mitigate vanishing gradients for long dependencies.

Topics
LLM and Agents
Related
Deep Learning Long Short-Term Memory (LSTM) Neural Network

Why it matters in GWAS

RNNs and their gated variants model RNA sequence, chromatin profiles along the genome, and longitudinal EHR or wearable time series linked to genetic studies. For genomics, sequence order and strand must be defined consistently, and models should be checked for batch and ancestry effects analogous to other predictors.

Example usage

"The longitudinal LDL trajectory model used a gated RNN over clinic visits before testing SNP-by-trajectory association."

Deep Learning Long Short-Term Memory (LSTM) Neural Network RNA-seq Transformer
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

  • Hochreiter S, Schmidhuber J. (1997). Long short-term memory. Neural Comput.
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