large language models

Large language model-assisted design of electrospun bioplastics

Automatic translation of natural language requirements into CTL specifications using Large Language Models: A multi-approach evaluation

Conversational AI for inventory analytics: Leveraging large language models in supply chain management

Large language models in mechanical design of mechatronic systems: A review

PathSymphony: Harmonizing symbolic planning and Large Language Models for curriculum-guided mathematical reasoning

LKG-STNet: A large language model-assisted knowledge graph-guided spatiotemporal network for aero-engine remaining useful life prediction

Large language model-assisted evolutionary order dispatching approach for multiple agile earth observing satellites scheduling problem

Failure-mode-aware uncertainty intervention routing for large language models

LLMs Leak Training Data Beyond Verbatim Memorization: Extraction via Membership Decoding

The Membership Decoding method is a plug-and-play replacement for standard decoding that requires only black-box token probabilities, and a new token-level membership inference method is proposed by leveraging likelihood from reference models, shifting the generation from the original token distribution to the member token distribution.

How can public concerns shape eVTOL policy design? A knowledge graph-enhanced large language model (LLM) framework

A hybrid framework of large language models and transformer networks for multimodal stock movement prediction

LncPNdeep: A long non-coding RNA classifier based on large language model with peptide and nucleotide embedding.

Accurate classification of long non-coding RNAs (lncRNAs) is essential for transcriptome annotation and understanding gene regulation. Existing computational methods predominantly rely on nucleotide sequence features, frequently overlooking biologically relevant peptide signals encoded within lncRNAs. To overcome this limitation, we developed LncPNdeep, an integrative deep learning framework that combines nucleotide and peptide embeddings extracted via masked language models, specifically utilizing contextual representations from BigBird, Longformer, and ProtTrans. By fusing both features in a concatenated neural architecture, LncPNdeep robustly captures complex sequence relationships and improves discrimination between lncRNAs and coding RNAs. Benchmarking on the human transcriptome achieved state-of-the-art performance with 97.1% accuracy, surpassing established lncRNA classification tools and baseline machine learning models. LncPNdeep also demonstrated superior generalization ability across cross-species datasets, maintaining consistently high accuracy and F1 scores. Permutation analysis highlighted the pivotal role of peptide embeddings, especially Average Peptide Embedding, in model performance, while t-SNE visualizations confirmed that integrating multiple embeddings markedly enhances the separation of lncRNAs from coding RNAs. These results position LncPNdeep as a versatile and powerful tool for transcriptomic research, facilitating lncRNA discovery, biomarker identification, and comparative genomics. The model and instructions are freely available at https://github.com/yatoka233/LncPNdeep.