Building Explainable Graph Neural Network by Sparse Learning for the Drug-Protein Binding Prediction [0.03%]
基于稀疏学习的可解释图神经网络构建用于药物-蛋白质结合预测
Yang Wang,Zanyu Shi,Pathum Weerawarna et al.
Yang Wang et al.
Explainable Graph Neural Networks have been developed and applied to drug-protein binding prediction to identify the key chemical structures in a drug that have active interactions with the target proteins. However, the key structures ident...
Filtering for Highly Variable Genes and High-Quality Spots Improves Phylogenetic Analysis of Cancer Spatial Transcriptomics Visium Data [0.03%]
过滤高变基因和高质量spots可改善癌症空间转录组学Visium数据的系统发育分析
Alexandra Sasha Gavryushkina,Holly R Pinkney,Sarah D Diermeier et al.
Alexandra Sasha Gavryushkina et al.
Phylogenetic relationship of cells within tumors can help us to understand how cancer develops in space and time and identify driver mutations and other evolutionary events that enable cancer growth and spread. Numerous studies have reconst...
Petr Ryšavý,Filip Železný
Petr Ryšavý
The Monge-Elkan distance is a straightforward yet popular distance measure used to estimate the mutual similarity of two sets of objects. It was initially proposed in the field of databases, and it found broad usage in other fields. Nowaday...
Sing-Hoi Sze
Sing-Hoi Sze
One popular approach to taxonomy classification in the microbiome utilizes 16S ribosomal RNA sequences. The main challenge is that 16S rRNA sequences could be almost identical in closely related species, and it is difficult to distinguish t...
Using Partition Information Entropy to Computationally Rank Order Critical Subreactions in a Petri Net Model of a Biochemical Signaling Network [0.03%]
基于Partition信息熵算法的生化信号网络关键Petri子反应模型排序算法研究
Janet B Jones-Oliveira,Hans-Joseph B Oliveira,Joseph S Oliveira et al.
Janet B Jones-Oliveira et al.
Improved computational methods to analyze the mathematical structure and function of biochemical networks are needed when the biomolecular connectivity is known but when a complete set of the equilibrium and rate constants may not be availa...
A Spatial-Correlated Multitask Linear Mixed-Effects Model for Imaging Genetics [0.03%]
一种用于影像遗传学的基于空间关联的多任务线性混合效应模型
Zhibin Pu,Shufei Ge
Zhibin Pu
Imaging genetics aims to uncover the hidden relationship between imaging quantitative traits (QTs) and genetic markers [e.g., single nucleotide polymorphism (SNP)] and brings valuable insights into the pathogenesis of complex diseases, such...
Yuyang Tao,Shufei Ge
Yuyang Tao
The Mapper algorithm is an essential tool for visualizing complex, high-dimensional data in topological data analysis and has been widely used in biomedical research. It outputs a combinatorial graph whose structure encodes the shape of the...
Aaron Hong,Christina Boucher
Aaron Hong
The growing volume of genomic data, driven by advances in sequencing technologies, demands efficient data compression solutions. Traditional algorithms, such as Lempel-Ziv77 (LZ77), have been fundamental in offering lossless compression, ye...
Combined Topological Data Analysis and Geometric Deep Learning Reveal Niches by the Quantification of Protein Binding Pockets [0.03%]
结合拓扑数据分析和几何深度学习通过量化蛋白质结合口袋来揭示生态位
Peiran Jiang,Jose Lugo-Martinez
Peiran Jiang
Protein pockets are essential for many proteins to carry out their functions. Locating and measuring protein pockets, as well as studying the anatomy of pockets, helps us further understand protein function. Most research studies focus on l...