Joint Extraction of Biomedical Events Based on Dynamic Path Planning Strategy and Hybrid Neural Network [0.03%]
基于动态路径规划策略和混合神经网络的生物医学事件联合抽取
Xinyu He,Yujie Tang,Bo Yu et al.
Xinyu He et al.
Biomedical event detection is a pivotal information extraction task in molecular biology and biomedical research, which provides inspiration for the medical search, disease prevention, and new drug development. The existing methods usually ...
AGML: Adaptive Graph-based Multi-label Learning for Prediction of RBP and AS Event Associations During EMT [0.03%]
基于自适应图的多标签学习在EMT过程中预测RBP和AS事件关联性
Yushan Qiu,Wensheng Chen,Wai-Ki Ching et al.
Yushan Qiu et al.
Increasing evidence has indicated that RNA-binding proteins (RBPs) play an essential role in mediating alternative splicing (AS) events during epithelial-mesenchymal transition (EMT). However, due to the substantial cost and complexity of b...
DMAMP: A deep-learning model for detecting antimicrobial peptides and their multi-activities [0.03%]
DMAMP：一种用于检测抗菌肽及其多种活性的深度学习模型
Qiaozhen Meng,Genlang Chen,Shixin Zheng et al.
Qiaozhen Meng et al.
Due to the broad-spectrum and high-efficiency antibacterial activity, antimicrobial peptides (AMPs) and their functions have been studied in the field of drug discovery. Using biological experiments to detect the AMPs and corresponding acti...
Hyb_SEnc: An Antituberculosis Peptide Predictor Based on a Hybrid Feature Vector and Stacked Ensemble Learning [0.03%]
基于混合特征向量和堆叠集成学习的抗结核肽预测模型Hyb_SEnc
Xiuhao Fu,Hao Duan,Xiaofeng Zang et al.
Xiuhao Fu et al.
Tuberculosis has plagued mankind since ancient times, and the struggle between humans and tuberculosis continues. Mycobacterium tuberculosis is the leading cause of tuberculosis, infecting nearly one-third of the world's population. The ris...
KGRLFF: Detecting Drug-Drug Interactions Based on Knowledge Graph Representation Learning and Feature Fusion [0.03%]
基于知识图表示学习和特征融合的药物相互作用检测(KGRLFF)
Xiaoli Lin,Zhuang Yin,Xiaolong Zhang et al.
Xiaoli Lin et al.
Accurate prediction of drug-drug interactions (DDIs) plays an important role in improving the efficiency of drug development and ensuring the safety of combination therapy. Most existing models rely on a single source of information to pred...
HGLA: Biomolecular Interaction Prediction based on Mixed High-Order Graph Convolution with Filter Network via LSTM and Channel Attention [0.03%]
基于LSTM和通道注意力的混合高阶图卷积滤波网络的生物分子相互作用预测(HGLA)
Zhen Zhang,Zhaohong Deng,Ruibo Li et al.
Zhen Zhang et al.
Predicting biomolecular interactions is significant for understanding biological systems. Most existing methods for link prediction are based on graph convolution. Although graph convolution methods are advantageous in extracting structure ...
Diffusing on Two Levels and Optimizing for Multiple Properties: A Novel Approach to Generating Molecules with Desirable Properties [0.03%]
双层扩散和多属性优化：生成具有理想性质分子的新方法
Siyuan Guo,Jihong Guan,Shuigeng Zhou
Siyuan Guo
In the past decade, Artificial Intelligence (AI) driven drug design and discovery has been a hot research topic in the AI area, where an important branch is molecule generation by generative models, from GAN-based models and VAE-based model...
Machine learning-assisted high-throughput screening for Anti-MRSA compounds [0.03%]
基于机器学习的抗MRSA化合物高通量筛选研究
Fadi Shehadeh,LewisOscar Felix,Markos Kalligeros et al.
Fadi Shehadeh et al.
Background: Antimicrobial resistance is a major public health threat, and new agents are needed. Computational approaches have been proposed to reduce the cost and time needed for compound screening. ...
MLRR-ATV: A Robust Manifold Nonnegative LowRank Representation with Adaptive Total-Variation Regularization for scRNA-seq Data Clustering [0.03%]
一种用于scRNA-seq数据聚类的自适应总变差正则化的鲁棒流形非负低秩表示方法（MLRR-ATV）
Gao-Fei Wang,Juan Wang,Shasha Yuan et al.
Gao-Fei Wang et al.
Since genomics was proposed, the exploration of genes has been the focus of research. The emergence of single-cell RNA sequencing (scRNA-seq) technology makes it possible to explore gene expression at the single-cell level. Due to the limit...
Generative Adversarial Network-Based Augmentation with Noval 2-step Authentication for Anti-coronavirus Peptide Prediction [0.03%]
基于生成对抗网络的数据增强技术在抗新型冠状病毒肽预测中的应用研究
Aditya Kumar,Deepak Singh
Aditya Kumar
The virus poses a longstanding and enduring danger to various forms of life. Despite the ongoing endeavors to combat viral diseases, there exists a necessity to explore and develop novel therapeutic options. Antiviral peptides are bioactive...