SAE1 May Play a Pro-Carcinogenic Role in Pancreatic Adenocarcinoma: A Comprehensive Study Integrating Multiple Pieces of Evidence [0.03%]
SAE1可能在胰腺导管腺癌中发挥促癌作用：多维证据的综合分析研究
Yi Chen,Tong Wu,Qi Li et al.
Yi Chen et al.
SAE1, a key factor in tumour development, has not been thoroughly examined in pancreatic adenocarcinoma (PAAD), a cancer with high incidence and poor prognosis. We conducted a comprehensive study, integrating mRNA data, immunohistochemistry...
Identification of HIBCH and MGME1 as Mitochondrial Dynamics-Related Biomarkers in Alzheimer's Disease Via Integrated Bioinformatics Analysis [0.03%]
通过综合生物信息学分析识别出阿尔茨海默病线粒体动态相关标志物HIBCH和MGME1
Hailong Li,Fei Feng,Shoupin Xie et al.
Hailong Li et al.
Mitochondrial dynamics (MD) play a crucial role in the genesis of Alzheimer's disease (AD); however, the molecular mechanisms underlying MD dysregulation in AD remain unclear. This study aimed to identify critical molecules of MD that contr...
Improved in Silico Identification of Protein-Protein Interactions Using Deep Learning Approach [0.03%]
基于深度学习方法改进的蛋白质-蛋白质相互作用的计算机识别方法研究
Irfan Khan,Muhammad Arif,Ali Ghulam et al.
Irfan Khan et al.
Protein-protein interactions (PPIs) perform significant functions in many biological activities likewise gene regulation, metabolic pathways and signal transduction. The deregulation of PPIs may cause deadly diseases, such as cancer, autoim...
Predictor-Based Output Feedback Control of Tumour Growth With Positive Input: Application to Antiangiogenic Therapy [0.03%]
基于预测的正输入肿瘤生长输出反馈控制及其在抗血管生成治疗中的应用
Mohamadreza Homayounzade
Mohamadreza Homayounzade
Controlling tumour growth systems presents significant challenges due to the inherent restriction of positive input in biological systems, along with delays in system output and input measurements. Traditional control methods struggle to ad...
scRSSL: Residual semi-supervised learning with deep generative models to automatically identify cell types [0.03%]
基于深度生成模型的残差半监督学习自动识别细胞类型的方法（scRSSL）
Yanru Gao,Hongyu Duan,Fanhao Meng et al.
Yanru Gao et al.
Single-cell sequencing (scRNA-seq) allows researchers to study cellular heterogeneity in individual cells. In single-cell transcriptomics analysis, identifying the cell type of individual cells is a key task. At present, single-cell dataset...
Identification of Eight Histone Methylation Modification Regulators Associated With Breast Cancer Prognosis [0.03%]
鉴定出8种与乳腺癌预后相关的组蛋白甲基化修饰调节因子
Yan-Ni Cao,Xiao-Hui Li,Xing-Jie Chen et al.
Yan-Ni Cao et al.
Histone methylation is an important epigenetic modification process coordinated by histone methyltransferases, histone demethylases and histone methylation reader proteins and plays a key role in the occurrence and development of cancer. Th...
SVM-LncRNAPro: An SVM-Based Method for Predicting Long Noncoding RNA Promoters [0.03%]
基于支持向量机的长链非编码rna启动子预测方法svm-lncrnapro
Guohua Huang,Taigan Xue,Weihong Chen et al.
Guohua Huang et al.
Long non-coding RNAs (lncRNAs) are closely associated with the regulation of gene expression, whose promoters play a crucial role in comprehensively understanding lncRNA regulatory mechanisms, functions and their roles in diseases. Due to l...
Transcriptome Analyses Reveal the Important miRNAs Involved in Immune Response of Gastric Cancer [0.03%]
转录组分析揭示胃癌免疫反应中重要的miRNA
Wen Jin,Jianli Liu,Tingyu Yang et al.
Wen Jin et al.
MicroRNAs (miRNAs) are crucial factors in gene regulation, and their dysregulation plays important roles in the immunity of gastric cancer (GC). However, finding specific and effective miRNA markers is still a great challenge for GC immunot...
TNFR-LSTM: A Deep Intelligent Model for Identification of Tumour Necroses Factor Receptor (TNFR) Activity [0.03%]
TNFR-LSTM：深度智能识别肿瘤坏死因子受体(TNFR)活性模型
Faisal Binzagr,Ansar Naseem,Muhammad Umer Farooq et al.
Faisal Binzagr et al.
Tumour necrosis factors (TNFs) are key players in processes such as inflammation, cancer development, and autoimmune diseases. However, accurately identifying TNFs remains challenging because of their complex interactions with other cytokin...
Investigating the Impact of Antibiotics on Environmental Microbiota Through Machine Learning Models [0.03%]
通过机器学习模型调查抗生素对环境微生物的影响
Yiheng Du,Khandaker Asif Ahmed,Md Rakibul Hasan et al.
Yiheng Du et al.
Antibiotic pollution in the environment can significantly impact soil microorganisms, such as altering the soil microbial community or emerging antibiotic-resistant bacteria. We propose three machine learning (ML) methods to investigate ant...