An approach for proteins and their encoding genes synonyms integration based on protein ontology [0.03%]
基于蛋白质本体的蛋白质及其编码基因同义词整合的方法研究
Xiaohong Wang,Xiaoli Jing,Fangkun Dou et al.
Xiaohong Wang et al.
Background: Biological research is generating high volumes of data distributed across various sources. The inconsistent naming of proteins and their encoding genes brings great challenges to protein data integration: prot...
HMCDA: a novel method based on the heterogeneous graph neural network and metapath for circRNA-disease associations prediction [0.03%]
基于异构图神经网络和元路径的circRNA-疾病关联预测的新方法
Shiyang Liang,Siwei Liu,Junliang Song et al.
Shiyang Liang et al.
Circular RNA (CircRNA) is a type of non-coding RNAs in which both ends are covalently linked. Researchers have demonstrated that many circRNAs can act as biomarkers of diseases. However, traditional experimental methods for circRNA-disease ...
Prediction of diabetes disease using an ensemble of machine learning multi-classifier models [0.03%]
基于机器学习的多分类器模型集成预测糖尿病病症
Karlo Abnoosian,Rahman Farnoosh,Mohammad Hassan Behzadi
Karlo Abnoosian
Background and objective: Diabetes is a life-threatening chronic disease with a growing global prevalence, necessitating early diagnosis and treatment to prevent severe complications. Machine learning has emerged as a pro...
Rinmaker: a fast, versatile and reliable tool to determine residue interaction networks in proteins [0.03%]
Rinmaker：一种快速、灵活且可靠的鉴定蛋白质中残基相互作用网络的工具
Alvise Spanò,Lorenzo Fanton,Davide Pizzolato et al.
Alvise Spanò et al.
Background: Residue Interaction Networks (RINs) map the crystallographic description of a protein into a graph, where amino acids are represented as nodes and non-covalent bonds as edges. Determination and visualization o...
Prediction of the effects of small molecules on the gut microbiome using machine learning method integrating with optimal molecular features [0.03%]
基于最优分子特征的机器学习方法预测小分子对肠道微生物组的影响
Binyou Wang,Jianmin Guo,Xiaofeng Liu et al.
Binyou Wang et al.
Background: The human gut microbiome (HGM), consisting of trillions of microorganisms, is crucial to human health. Adverse drug use is one of the most important causes of HGM disorder. Thus, it is necessary to identify dr...
SubMDTA: drug target affinity prediction based on substructure extraction and multi-scale features [0.03%]
基于子结构提取和多尺度特征的药物靶点亲和力预测方法(SubMDTA)
Shourun Pan,Leiming Xia,Lei Xu et al.
Shourun Pan et al.
Background: Drug-target affinity (DTA) prediction is a critical step in the field of drug discovery. In recent years, deep learning-based methods have emerged for DTA prediction. In order to solve the problem of fusion of...
IHCP: interpretable hepatitis C prediction system based on black-box machine learning models [0.03%]
基于黑盒机器学习模型的可解释性丙型肝炎预测系统(IHCP)
Yongxian Fan,Xiqian Lu,Guicong Sun
Yongxian Fan
Background: Hepatitis C is a prevalent disease that poses a high risk to the human liver. Early diagnosis of hepatitis C is crucial for treatment and prognosis. Therefore, developing an effective medical decision system i...
Automatically transferring supervised targets method for segmenting lung lesion regions with CT imaging [0.03%]
基于CT影像的肺部病灶区域分割的监督目标自动迁移方法
Peng Du,Xiaofeng Niu,Xukun Li et al.
Peng Du et al.
Background: To present an approach that autonomously identifies and selects a self-selective optimal target for the purpose of enhancing learning efficiency to segment infected regions of the lung from chest computed tomo...
Empirical methods for the validation of time-to-event mathematical models taking into account uncertainty and variability: application to EGFR + lung adenocarcinoma [0.03%]
基于不确定性和变异性的时间事件型数学模型实证验证方法：在EGFR阳性肺腺癌中的应用
Evgueni Jacob,Angélique Perrillat-Mercerot,Jean-Louis Palgen et al.
Evgueni Jacob et al.
Background: Over the past several decades, metrics have been defined to assess the quality of various types of models and to compare their performance depending on their capacity to explain the variance found in real-life...
Tychele N Turner
Tychele N Turner
Background: The study of de novo variation is important for assessing biological characteristics of new variation and for studies related to human phenotypes. Software programs exist to call de novo variants and programs ...