RedCDR: Dual Relation Distillation for Cancer Drug Response Prediction [0.03%]
红CDR：癌症药物反应预测的双关系蒸馏方法
Muhao Xu,Zhenfeng Zhu,Yawei Zhao et al.
Muhao Xu et al.
Based on multi-omics data and drug information, predicting the response of cancer cell lines to drugs is a crucial area of research in modern oncology, as it can promote the development of personalized treatments. Despite the promising perf...
Collaborative Structure-Preserved Missing Data Imputation for Single-Cell RNA-Seq Clustering [0.03%]
基于协同结构保护的单细胞RNA序列数据缺失值填补方法研究
Hang Gao,Wenjun Shen,Rui Li et al.
Hang Gao et al.
Clustering of the single-cell RNA-seq (scRNA-seq) transcriptome profiles is able to identify cell types, which is beneficial to improve the understanding of disease progression. However, in practice, the single-cell expression data often co...
Molecular Design Based on Integer Programming and Splitting Data Sets by Hyperplanes [0.03%]
基于整数规划的分子设计及利用划分超平面拆分子结构集
Jianshen Zhu,Naveed Ahmed Azam,Kazuya Haraguchi et al.
Jianshen Zhu et al.
A novel framework for designing the molecular structure of chemical compounds with a desired chemical property has recently been proposed. The framework infers a desired chemical graph by solving a mixed integer linear program (MILP) that s...
Drug-Target Binding Affinity Prediction in a Continuous Latent Space Using Variational Autoencoders [0.03%]
基于变分自编码器的连续潜在空间中药物-靶点结合亲和力预测
Lingling Zhao,Yan Zhu,Naifeng Wen et al.
Lingling Zhao et al.
Accurate prediction of Drug-Target binding Affinity (DTA) is a daunting yet pivotal task in the sphere of drug discovery. Over the years, a plethora of deep learning-based DTA models have emerged, rendering promising results in predicting t...
DLP: Duplex Link Prediction Via Subspace Segmentation for Predicting drug-MiRNA Associations [0.03%]
基于子空间分割的双向链预测方法用于预测药物与MiRNA相互作用
Kai Zheng,Guihua Duan,Qichang Zhao et al.
Kai Zheng et al.
The arduous and costly journey of drug discovery is increasingly intersecting with computational approaches, which promise to accelerate the analysis of bioassays and biomedical literature. The critical role of microRNAs (miRNAs) in disease...
Graph Representation Learning Based on Specific Subgraphs for Biomedical Interaction Prediction [0.03%]
基于特定子图的图形表示学习在生物医学相互作用预测中的应用
Huaxin Pang,Shikui Wei,Zhuoran Du et al.
Huaxin Pang et al.
Discovering the novel associations of biomedical entities is of great significance and can facilitate not only the identification of network biomarkers of disease but also the search for putative drug targets. Graph representation learning ...
Bayesian Lookahead Perturbation Policy for Inference of Regulatory Networks [0.03%]
基因调控网络推理的贝叶斯前瞻扰动策略
Mohammad Alali,Mahdi Imani
Mohammad Alali
The complexity, scale, and uncertainty in regulatory networks (e.g., gene regulatory networks and microbial networks) regularly pose a huge uncertainty in their models. These uncertainties often cannot be entirely reduced using limited and ...
Geometry-Augmented Molecular Representation Learning for Property Prediction [0.03%]
基于几何信息的分子表示学习用于分子性质预测
Yanan Zhang,Xiangzhi Bai
Yanan Zhang
Accurate molecular representation plays a crucial role in expediting the process of drug discovery. Graph neural networks (GNNs) have demonstrated robust capabilities in molecular representation learning, adept at capturing structural and s...
Analysis of Cancer-associated Mutations of POLB using Machine Learning and Bioinformatics [0.03%]
基于机器学习和生物信息学的POLB癌相关突变分析
Razan Alkhanbouli,Amira Al-Aamri,Maher Maalouf et al.
Razan Alkhanbouli et al.
DNA damage is a critical factor in the onset and progression of cancer. When DNA is damaged, the number of genetic mutations increases, making it necessary to activate DNA repair mechanisms. A crucial factor in the base excision repair proc...
A Network Enhancement Method to Identify Spurious Drug-Drug Interactions [0.03%]
一种用于识别假阳性的药物相互作用的网络优化方法
Huan Wang,Ziwen Cui,Yinguang Yang et al.
Huan Wang et al.
As medical safety and drug regulation gain heightened attention, the detection of spurious drug-drug interactions (DDI) has become key in healthcare. Although current research using graph neural networks (GNNs) to predict DDI has shown impr...