iAnOxPep: a machine learning model for the identification of anti-oxidative peptides using ensemble learning [0.03%]
基于集成学习的抗氧化肽鉴定的机器学习模型(iAnOxPep)
Mir Tanveerul Hassan,Hilal Tayara,Kil To Chong
Mir Tanveerul Hassan
Due to their safety, high activity, and plentiful sources, antioxidant peptides, particularly those produced from food, are thought to be prospective competitors to synthetic antioxidants in the fight against free radical-mediated illnesses...
Performance Comparison between Deep Neural Network and Machine Learning based Classifiers for Huntington Disease Prediction from Human DNA Sequence [0.03%]
基于深度神经网络和机器学习分类器的亨廷顿舞蹈病人类DNA序列预测性能比较
C Vishnuppriya,G Tamilpavai
C Vishnuppriya
Huntington Disease (HD) is a type of neurodegenerative disorder which causes problems like psychiatric disturbances, movement problem, weight loss and problem in sleep. It needs to be addressed in earlier stage of human life. Nowadays Deep ...
DeepLigType: Predicting Ligand Types of Protein-Ligand Binding Sites Using a Deep Learning Model [0.03%]
基于深度学习模型预测蛋白质-配体结合位点的配体类型
Orhun Vural,Leon Jololian,Lurong Pan
Orhun Vural
The analysis of protein-ligand binding sites plays a crucial role in the initial stages of drug discovery. Accurately predicting the ligand types that are likely to bind to protein-ligand binding sites enables more informed decision making ...
AI-based Computational Methods in Early Drug Discovery and Post Market Drug Assessment: A Survey [0.03%]
基于人工智能的计算方法在早期药物发现和上市后药物评估中的应用：综述
Flora Rajaei,Cristian Minoccheri,Emily Wittrup et al.
Flora Rajaei et al.
Over the past few years, artificial intelligence (AI) has emerged as a transformative force in drug discovery and development (DDD), revolutionizing many aspects of the process. This survey provides a comprehensive review of recent advancem...
Enhancing Single-Cell RNA-seq Data Completeness with a Graph Learning Framework [0.03%]
一种改进单细胞RNA测序数据完整性的图学习框架
Snehalika Lall,Sumanta Ray,Sanghamitra Bandyopadhyay
Snehalika Lall
Single cell RNA sequencing (scRNA-seq) is a powerful tool to capture gene expression snapshots in individual cells. However, a low amount of RNA in the individual cells results in dropout events, which introduce huge zero counts in the sing...
circ2DGNN: circRNA-disease Association Prediction via Transformer-based Graph Neural Network [0.03%]
基于Transformer的图神经网络的circRNA-疾病关联预测(Circ2DGNN)
Keliang Cen,Zheming Xing,Xuan Wang et al.
Keliang Cen et al.
Investigating the associations between circRNA and diseases is vital for comprehending the underlying mechanisms of diseases and formulating effective therapies. Computational prediction methods often rely solely on known circRNA-disease da...
LHPre: Phage Host Prediction with VAE-based Class Imbalance Correction and Lyase Sequence Embedding [0.03%]
基于VAE的类不平衡校正和裂解酶序列嵌入的噬菌体宿主预测模型LHPre
Jia Wang,Zhenjing Yu,Jianqiang Li
Jia Wang
The escalation of antibiotic resistance underscores the need for innovative approaches to combat bacterial infections. Phage therapy has emerged as a promising solution, wherein host determination plays an important role. Phage lysins, char...
Hierarchical hypergraph learning in association-weighted heterogeneous network for miRNA-disease association identification [0.03%]
基于关联加权异构网络的层次双曲图学习的微rna-疾病关联识别方法
Qiao Ning,Yaomiao Zhao,Jun Gao et al.
Qiao Ning et al.
MicroRNAs (miRNAs) play a significant role in cell differentiation, biological development as well as the occurrence and growth of diseases. Although many computational methods contribute to predicting the association between miRNAs and dis...
Detecting Boolean Asymmetric Relationships with a Loop Counting Technique and its Implications for Analyzing Heterogeneity within Gene Expression Datasets [0.03%]
基于循环计算技术的布尔不对称关系检测及其在基因表达数据异质性分析中的应用
Haosheng Zhou,Wei Lin,Sergio R Labra et al.
Haosheng Zhou et al.
Many traditional methods for analyzing gene-gene relationships focus on positive and negative correlations, both of which are a kind of 'symmetric' relationship. Biclustering is one such technique that typically searches for subsets of gene...
Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq [0.03%]
一种同时去除批次效应和标注细胞类型的判别领域适配网络
Qi Zhu,Aizhen Li,Zheng Zhang et al.
Qi Zhu et al.
Machine learning techniques have become increasingly important in analyzing single-cell RNA and identifying cell types, providing valuable insights into cellular development and disease mechanisms. However, the presence of batch effects pos...