ESGC-MDA: Identifying miRNA-disease associations using enhanced Simple Graph Convolutional Networks [0.03%]
基于增强型Simple图卷积网络的miRNA-疾病关联识别
Xuehua Bi,Chunyang Jiang,Cheng Yan et al.
Xuehua Bi et al.
MiRNAs play an important role in the occurrence and development of human disease. Identifying potential miRNA-disease associations is valuable for disease diagnosis and treatment. Therefore, it is urgent to develop efficient computational m...
MLW-BFECF: a multi-weighted dynamic cascade forest based on bilinear feature extraction for predicting the stage of Kidney Renal Clear Cell Carcinoma on multi-modal gene data [0.03%]
基于双线性特征提取的多模式基因数据下用于预测肾脏透明细胞癌分期的多重权重动态级联森林方法(MLW-BFECF)
Liye Jia,Liancheng Jiang,Junhong Yue et al.
Liye Jia et al.
The stage prediction of kidney renal clear cell carcinoma (KIRC) is important for the diagnosis, personalized treatment, and prognosis of patients. Many prediction methods have been proposed, but most of them are based on unimodal gene data...
An End-to-end Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction [0.03%]
一种融合知识图谱的端到端图神经网络蛋白质相互作用预测方法
Jie Yang,Yapeng Li,Guoyin Wang et al.
Jie Yang et al.
Protein-protein interactions (PPIs) are essential to understanding cellular mechanisms, signaling networks, disease processes, and drug development, as they represent the physical contacts and functional associations between proteins. Recen...
Generative Biomedical Event Extraction with Constrained Decoding Strategy [0.03%]
基于约束解码的生成式生物医学事件抽取模型
Fangfang Su,Chong Teng,Fei Li et al.
Fangfang Su et al.
Currently, biomedical event extraction has received considerable attention in various fields, including natural language processing, bioinformatics, and computational biomedicine. This has led to the emergence of numerous machine learning a...
A comprehensive evaluation framework for benchmarking multi-objective feature selection in omics-based biomarker discovery [0.03%]
组学生物标志物发现中多目标特征选择基准测试的全面评估框架
Luca Cattelani,Arindam Ghosh,Teemu Rintala et al.
Luca Cattelani et al.
Machine learning algorithms have been extensively used for accurate classification of cancer subtypes driven by gene expression-based biomarkers. However, biomarker models combining multiple gene expression signatures are often not reproduc...
GrapHiC: An integrative graph based approach for imputing missing Hi-C reads [0.03%]
一种基于图的集成方法填补高通量染色质交互测序数据中的缺失值
Ghulam Murtaza,Justin Wagner,Justin M Zook et al.
Ghulam Murtaza et al.
Hi-C experiments allow researchers to study and understand the 3D genome organization and its regulatory function. Unfortunately, sequencing costs and technical constraints severely restrict access to high-quality Hi-C data for many cell ty...
De Novo Drug Design by Multi-Objective Path Consistency Learning with Beam A∗ Search [0.03%]
基于束搜索的多目标路径一致性学习的新药设计方法
Dengwei Zhao,Jingyuan Zhou,Shikui Tu et al.
Dengwei Zhao et al.
Generating high-quality and drug-like molecules from scratch within the expansive chemical space presents a significant challenge in the field of drug discovery. In prior research, value-based reinforcement learning algorithms have been emp...
A knowledge graph-based method for drug-drug interaction prediction with contrastive learning [0.03%]
基于知识图的药物间相互作用预测对比学习方法
Jian Zhong,Haochen Zhao,Qichang Zhao et al.
Jian Zhong et al.
Precisely predicting Drug-Drug Interactions (DDIs) carries the potential to elevate the quality and safety of drug therapies, protecting the well-being of patients, and providing essential guidance and decision support at every stage of the...
Orientation Determination of Cryo-EM Projection Images Using Reliable Common Lines and Spherical Embeddings [0.03%]
基于可靠的公共线条和球面嵌入确定Cryo-EM投影图像的方向性
Xiangwen Wang,Qiaoying Jin,Li Zou et al.
Xiangwen Wang et al.
Three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is a critical technique for recovering and studying the fine 3D structure of proteins and other biological macromolecules, where the primary issue i...
Improving Molecule Generation and Drug Discovery with a Knowledge-enhanced Generative Model [0.03%]
利用增强生成模型改进分子生成和药物发现
Aditya Malusare,Vaneet Aggarwal
Aditya Malusare
Recent advancements in generative models have established state-of-the-art benchmarks in the generation of molecules and novel drug candidates. Despite these successes, a significant gap persists between generative models and the utilizatio...