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Long Short-Term Memory (LSTM)

Definition
AI-generated

Long short-term memory (LSTM) is a gated recurrent neural network cell that maintains a separate cell state and uses input, forget, and output gates to propagate information across many time steps, reducing vanishing-gradient problems in long sequences.

Topics
LLM and Agents
Related
Deep Learning Recurrent Neural Network (RNN) Transformer

Why it matters in GWAS

LSTMs model ordered biological sequences (e.g. RNA or epigenomic tracks) and longitudinal phenotypes in EHR-linked studies; they remain a baseline alongside CNNs and transformers for sequence tasks in genomics.

Example usage

"We compared an LSTM over phased haplotype encodings to a linear PRS for predicting drug response trajectories."

Deep Learning Recurrent Neural Network (RNN) 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.
← Long-Read Sequencing Loss Function →

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