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EquiRank: Improved protein-protein interface quality estimation using protein language-model-informed equivariant graph neural networks.
Comput Struct Biotechnol J
December 2024
Department of Computer Science, Virginia Tech, Blacksburg, 24061, VA, USA.
Quality estimation of the predicted interaction interface of protein complex structural models is not only important for complex model evaluation and selection but also useful for protein-protein docking. Despite recent progress fueled by symmetry-aware deep learning architectures and pretrained protein language models (pLMs), existing methods for estimating protein complex quality have yet to fully exploit the collective potentials of these advances for accurate estimation of protein-protein interface. Here we present EquiRank, an improved protein-protein interface quality estimation method by leveraging the strength of a symmetry-aware E(3) equivariant deep graph neural network (EGNN) and integrating pLM embeddings from the pretrained ESM-2 model.
View Article and Find Full Text PDFComparing the performance of ChatGPT and ERNIE Bot in answering questions regarding liver cancer interventional radiology in Chinese and English contexts: A comparative study.
Digit Health
January 2025
Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Introduction: This study aims to critically assess the appropriateness and limitations of two prominent large language models (LLMs), enhanced representation through knowledge integration (ERNIE Bot) and chat generative pre-trained transformer (ChatGPT), in answering questions about liver cancer interventional radiology. Through a comparative analysis, the performance of these models will be evaluated based on their responses to questions about transarterial chemoembolization and hepatic arterial infusion chemotherapy in both English and Chinese contexts.
Methods: A total of 38 questions were developed to cover a range of topics related to transarterial chemoembolization (TACE) and hepatic arterial infusion chemotherapy (HAIC), including foundational knowledge, patient education, and treatment and care.
ClinClip: a Multimodal Language Pre-training model integrating EEG data for enhanced English medical listening assessment.
Front Neurosci
January 2025
The Basic Department, The Tourism College of Changchun University, Changchun, China.
Introduction: In the field of medical listening assessments,accurate transcription and effective cognitive load management are critical for enhancing healthcare delivery. Traditional speech recognition systems, while successful in general applications often struggle in medical contexts where the cognitive state of the listener plays a significant role. These conventional methods typically rely on audio-only inputs and lack the ability to account for the listener's cognitive load, leading to reduced accuracy and effectiveness in complex medical environments.
View Article and Find Full Text PDFUnICLAM: Contrastive representation learning with adversarial masking for unified and interpretable Medical Vision Question Answering.
Med Image Anal
January 2025
Sixth Medical Center of Chinese PLA General Hospital, Beijing, 100037, China. Electronic address:
Medical Visual Question Answering aims to assist doctors in decision-making when answering clinical questions regarding radiology images. Nevertheless, current models learn cross-modal representations through residing vision and text encoders in dual separate spaces, which inevitably leads to indirect semantic alignment. In this paper, we propose UnICLAM, a Unified and Interpretable Medical-VQA model through Contrastive Representation Learning with Adversarial Masking.
View Article and Find Full Text PDFData-driven discovery and parameter estimation of mathematical models in biological pattern formation.
PLoS Comput Biol
January 2025
Department of Anatomy and Cell Biology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Fukuoka, Japan.
Mathematical modeling has been utilized to explain biological pattern formation, but the selections of models and parameters have been made empirically. In the present study, we propose a data-driven approach to validate the applicability of mathematical models. Specifically, we developed methods to automatically select the appropriate mathematical models based on the patterns of interest and to estimate the model parameters.
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