Titlebook: Bioinformatics Research and Applications; 20th International S Wei Peng,Zhipeng Cai,Pavel Skums Conference proceedings 2024 The Editor(s) (

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Predicting Drug-Target Affinity Using Protein Pocket and Graph Convolution Network,a vital role in DTA due to its direct interaction with drug. With the emergence of numerous computational methods, deep learning holds great promise in this field. Currently, most deep learning methods for DTA prediction are based on sequence or two-dimensional graph data, overlooking the structural

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,RFIR: A Lightweight Network for?Retinal Fundus Image Restoration, obtain high-quality retinal images. Low resolution or poor quality significantly hinders medical diagnosis, adversely affecting clinical or downstream tasks. Furthermore, medical datasets lack the vast quantity of data characteristic of natural images. Transformers, with their numerous parameters,

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,Gaussian Beltrami-Klein Model for?Protein Sequence Classification: A Hyperbolic Approach,ical processes. Several machine-learning approaches have been proposed to address the problems in the area. However, conventional machine learning approaches encounter limitations in capturing the intricate relationships and hierarchical structures inherent in genomic sequences due to the high-dimen

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,Contrastive Masked Graph Autoencoders for?Spatial Transcriptomics Data Analysis,vel. This technique enables the acquisition of gene expression profiles for each spot, constructing a spatial gene expression map. While numerous methods have been developed to integrate expression profiles and spatial information for spatial domain detection, accurate identification remains a chall

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,Spatial Gene Expression Prediction from?Histology Images with?STco,on within complex biological systems. However, the widespread application of spatial transcriptome technology in large-scale studies is hindered by its high cost and complexity. An economical alternative involves utilizing artificial intelligence to predict gene expression information from entire sl