sequencingmodel

Sequencing model is a class of computational models designed to capture dependencies in sequential data, enabling understanding, prediction, generation, or analysis of sequences. The term is used across fields such as natural language processing, time-series analysis, and genomics.

Common families include stochastic models such as Markov chains and hidden Markov models, which use probabilistic transitions between states and emissions. Neural sequence models—such as recurrent neural networks, long short-term memory networks, gated recurrent units, and transformer-based architectures—address longer-range dependencies and complex patterns. For structured labeling, conditional random fields are used, while autoregressive models are common for generation tasks. The choice of model depends on the nature of the sequence, the available data, and the required outputs.

In genomics, sequencing models analyze DNA or RNA sequences to identify genes, predict regulatory elements, or infer variants. In language tasks, they predict the next token or generate text, and in time-series domains they forecast future values or classify sequence patterns. Training methods range from maximum likelihood and expectation–maximization for latent or probabilistic models to gradient-based optimization for neural networks, with techniques such as backpropagation through time and transformer training.

Evaluation metrics depend on the task: perplexity or cross-entropy for modeling, accuracy for labeling or classification, BLEU or ROUGE for generation tasks, and domain-specific measures like variant-calling accuracy in genomics. Challenges include managing long-range dependencies, data sparsity and imbalance, interpretability, and substantial computational requirements.