Drug repurposing for non-small cell lung cancer by predicting drug response using pathway-level graph convolutional network [0.03%]
通过基于路径的图卷积网络预测药物反应从而实现非小细胞肺癌的老药新用
I T Anjusha,K A Abdul Nazeer,N Saleena
I T Anjusha
Drug repurposing is the process of identifying new clinical indications for an existing drug. Some of the recent studies utilized drug response prediction models to identify drugs that can be repurposed. By representing cell-line features a...
SS-DTI: A deep learning method integrating semantic and structural information for drug-target interaction prediction [0.03%]
SS-DTI:一种整合语义和结构信息预测药物-靶点相互作用的深度学习方法
Yujie Chun,Huaihu Li,Shunfang Wang
Yujie Chun
Drug-target interaction (DTI) prediction is pivotal in drug discovery and repurposing, providing a more efficient alternative to traditional wet-lab experiments by saving time and resources and expediting the identification of potential tar...
Exploring relationship between hypercholesterolemia and instability of atherosclerotic plaque - An approach based on a matrix population model [0.03%]
高胆固醇与动脉粥样硬化斑块不稳定性之间关系的探究——基于矩阵种群模型的方法
Mateusz Twardawa,Kaja Gutowska,Piotr Formanowicz
Mateusz Twardawa
Background: Cardiovascular diseases have long been studied to identify their causal factors and counteract them effectively. Atherosclerosis, an inflammatory process of the blood vessel wall, is a common cardiovascular disease. Among the ma...
Li-Ping Wu,Li Yong,Xiang Cheng et al.
Li-Ping Wu et al.
Compound identification in small molecule research relies on comparing experimental mass spectra with mass spectral databases. However, unequal data lengths often lead to inefficient and inaccurate retrieval. Moreover, the similarity calcul...
ASAP-DTA: Predicting drug-target binding affinity with adaptive structure aware networks [0.03%]
ASAP-DTA：使用自适应结构感知网络预测药物靶点结合亲合力
Weibin Ding,Shaohua Jiang,Ting Xu et al.
Weibin Ding et al.
The prediction of drug-target affinity (DTA) is crucial for efficiently identifying potential targets for drug repurposing, thereby reducing resource wastage. In this paper, we propose a novel graph-based deep learning model for DTA that le...
Chao Li,Xiaoran Huang,Xiao Luo et al.
Chao Li et al.
Gene regulatory networks (GRNs) reveal the regulatory interactions among genes and provide a visual tool to explain biological processes. However, how to identify direct relations among genes from gene expression data in the case of high-di...
SAKit: An all-in-one analysis pipeline for identifying novel proteins resulting from variant events at both large and small scales [0.03%]
SAKit：一个用于识别大规模和小规模变异事件产生的新型蛋白质的全方位分析管线
Yan Li,Boran Wang,Zengding Wu et al.
Yan Li et al.
Background: Genetic mutations that cause the inactivation or aberrant activation of essential proteins may trigger alterations or even dysfunctions in cellular signaling pathways, culminating in the development of precancerous lesions and c...
Molecular dynamics simulations of ribosome-binding sites in theophylline-responsive riboswitch associated with improving the gene expression regulation in chloroplasts [0.03%]
分子动力学模拟揭示了与叶绿体基因表达调控优化相关的咖啡因响应型核糖开关的结合位点构象变化
Rahim Berahmand,Masoumeh Emadpour,Mokhtar Jalali Javaran et al.
Rahim Berahmand et al.
The existence of an efficient inducible transgene expression system is a valuable tool in recombinant protein production. The synthetic theophylline-responsive riboswitch (theo.RS) can be replaced in the 5[Formula: see text] untranslated re...
Improving drug-target interaction prediction through dual-modality fusion with InteractNet [0.03%]
通过InteractNet融合双模态改善药物-靶点相互作用预测能力
Baozhong Zhu,Runhua Zhang,Tengsheng Jiang et al.
Baozhong Zhu et al.
In the drug discovery process, accurate prediction of drug-target interactions is crucial to accelerate the development of new drugs. However, existing methods still face many challenges in dealing with complex biomolecular interactions. To...