[HTML][HTML] Multimodal large language models in health care: applications, challenges, and future outlook
In the complex and multidimensional field of medicine, multimodal data are prevalent and
crucial for informed clinical decisions. Multimodal data span a broad spectrum of data types …
crucial for informed clinical decisions. Multimodal data span a broad spectrum of data types …
Scientific large language models: A survey on biological & chemical domains
Large Language Models (LLMs) have emerged as a transformative power in enhancing
natural language comprehension, representing a significant stride toward artificial general …
natural language comprehension, representing a significant stride toward artificial general …
[HTML][HTML] Large language models in bioinformatics: applications and perspectives
Large language models (LLMs) are a class of artificial intelligence models based on deep
learning, which have great performance in various tasks, especially in natural language …
learning, which have great performance in various tasks, especially in natural language …
The impact of large language models on scientific discovery: a preliminary study using gpt-4
MR AI4Science, MA Quantum - arXiv preprint arXiv:2311.07361, 2023 - arxiv.org
In recent years, groundbreaking advancements in natural language processing have
culminated in the emergence of powerful large language models (LLMs), which have …
culminated in the emergence of powerful large language models (LLMs), which have …
PLPMpro: Enhancing promoter sequence prediction with prompt-learning based pre-trained language model
The promoter region, positioned proximal to the transcription start sites, exerts control over
the initiation of gene transcription by modulating the interaction with RNA polymerase …
the initiation of gene transcription by modulating the interaction with RNA polymerase …
[图书][B] Foundation models for natural language processing: Pre-trained language models integrating media
G Paaß, S Giesselbach - 2023 - library.oapen.org
This open access book provides a comprehensive overview of the state of the art in research
and applications of Foundation Models and is intended for readers familiar with basic …
and applications of Foundation Models and is intended for readers familiar with basic …
GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity
The interaction between T-cell receptors (TCRs) and peptides (epitopes) presented by major
histocompatibility complex molecules (MHC) is fundamental to the immune response …
histocompatibility complex molecules (MHC) is fundamental to the immune response …
Application of Transformers in Cheminformatics
KD Luong, A Singh - Journal of Chemical Information and …, 2024 - ACS Publications
By accelerating time-consuming processes with high efficiency, computing has become an
essential part of many modern chemical pipelines. Machine learning is a class of computing …
essential part of many modern chemical pipelines. Machine learning is a class of computing …
A sparse and wide neural network model for DNA sequences
Accurate modeling of DNA sequences requires capturing distant semantic relationships
between the nucleotide acid bases. Most existing deep neural network models face two …
between the nucleotide acid bases. Most existing deep neural network models face two …
Self-supervised learning for DNA sequences with circular dilated convolutional networks
DNA molecules commonly exhibit wide interactions between the nucleobases. Modeling the
interactions is important for obtaining accurate sequence-based inference. Although many …
interactions is important for obtaining accurate sequence-based inference. Although many …