Novel genes involved in vascular dysfunction of the middle temporal gyrus in Alzheimer’s disease: transcriptomics combined with machine learning analysis by Meiling Wang, Aojie He, Yubing Kang, Zhaojun Wang, Yahui He, Kahleong Lim, Chengwu Zhang, Li Lu

“…CellChat analysis of intercellular communication in the middle temporal gyrus showed that the number of cell interactions in this region was decreased in Alzheimer’s disease patients, with altered intercellular communication of endothelial cells and pericytes being the most prominent. Differentially expressed genes were also identified. Using the CellChat results, AUCell evaluation of the pathway activity of specific cells showed that the obvious changes in vascular function in the middle temporal gyrus in Alzheimer’s disease were directly related to changes in the vascular endothelial growth factor (VEGF)A–VEGF receptor (VEGFR) 2 pathway. …”
Get full text

An Interpretable Machine Learning Model Based on Inflammatory–Nutritional Biomarkers for Predicting Metachronous Liver Metastases After Colorectal Cancer Surgery by Hao Zhu, Danyang Shen, Xiaojie Gan, Ding Sun

“…Feature selection was performed using Boruta and Lasso algorithms, identifying nine core prognostic factors through variable intersection. …”
Get full text

Multiparameter diagnostic model using S100A9, CCL5 and blood biomarkers for nasopharyngeal carcinoma by Lu Long, Ya Tao, Wenze Yu, Qizhuo Hou, Yunlai Liang, Kangkang Huang, Huidan Luo, Bin Yi

“…NPC prediction models were developed using four machine-learning algorithms, and their performance was evaluated with ROC curves. …”
Get full text

Constructing a predictive model for acute mastitis in lactating women based on machine learning by Liujing Zhu, Zuyan Huang, Yan Chen, Guangqiu Li, Liwen Liu

“…Prediction models were established using four different ML algorithms. Through analysis, when comparing the four distinct ML models on the test set, the MLP model performed optimally across various evaluation metrics, including the highest area under the receiver operating characteristic (ROC) curve (AUROC) (0.898), sensitivity (0.820), test specificity (0.863), and F1 score (0.849), with an accuracy of 0.840. …”
Get full text

Comprehensive analysis reveals cholesterol metabolism-related signature for predicting prognosis and guiding individualized treatment of glioma by Dengfeng Lu, Fei Wang, Yayi Yang, Aojie Duan, Yubo Ren, Yun Feng, Haiying Teng, Zhouqing Chen, Xiaoou Sun, Zhong Wang

“…Finally, the drug sensitivity of gliomas in different risk groups was evaluated using the oncoPredict algorithm, and potentially sensitive chemotherapeutic and molecular-targeted drugs were identified. …”
Get full text

Recognition of pivotal immune genes NR1H4 and IL4R as diagnostic biomarkers in distinguishing ovarian clear cell cancer from high-grade serous cancer by Yumin Ke, Meili Liang, Zhimei Zhou, Yajing Xie, Li Huang, Liying Sheng, Yueli Wang, Xinyan Zhou, Zhuna Wu

“…The diagnostic performance of these candidate genes was evaluated using receiver operating characteristic (ROC) curves. …”
Get full text

Recombinant purified buffalo leukemia inhibitory factor plays an inhibitory role in cell growth. by Syed Azmal Ali, Dhruba Malakar, Jai Kumar Kaushik, Ashok Kumar Mohanty, Sudarshan Kumar

Get full text

Large Language Models and Artificial Neural Networks for Assessing 1-Year Mortality in Patients With Myocardial Infarction: Analysis From the Medical Information Mart for Intensive... by Boqun Shi, Liangguo Chen, Shuo Pang, Yue Wang, Shen Wang, Fadong Li, Wenxin Zhao, Pengrong Guo, Leli Zhang, Chu Fan, Yi Zou, Xiaofan Wu

“…An artificial neural network (ANN) algorithm derived from the SWEDEHEART (Swedish Web System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies) registry, termed SWEDEHEART-AI, can predict patient prognosis following acute myocardial infarction (AMI). …”
Get full text

Metabolic pathway activation and immune microenvironment features in non-small cell lung cancer: insights from single-cell transcriptomics by Yanru Liu, Yanru Liu, Yanru Liu, Hanmin Liu, Hanmin Liu, Ying Xiong, Ying Xiong

“…Given that lung cancer is a leading cause of cancer-related deaths globally and NSCLC accounts for the majority of lung cancer cases, understanding the relationship between TME and metabolic pathways in NSCLC is crucial for developing new treatment strategies.MethodsFinally, machine learning algorithms were employed to construct a risk signature with strong predictive power across multiple independent cohorts. …”
Get full text

Identification of cellular senescence-associated genes for predicting the diagnosis, prognosis and immunotherapy response in lung adenocarcinoma via a 113-combination machine learn... by Ting Ge, Guixin He, Qian Cui, Shuangcui Wang, Zekun Wang, Yingying Xie, Yuanyuan Tian, Juyue Zhou, Jianchun Yu, Jinmin Hu, Wentao Li

“…Subsequently, we developed a novel machine learning framework that incorporated 12 machine learning algorithms and their 113 combinations to construct a LUAD CS-related signature (LUAD-CSRS), which were assessed in both training and validation cohorts. …”
Get full text

Predicting Treatment Outcomes in Patients with Low Back Pain Using Gene Signature-Based Machine Learning Models by Youzhi Lian, Yinyu Shi, Haibin Shang, Hongsheng Zhan

“…Patients were classified into two groups: those with resolved pain and those with persistent pain. Differentially expressed genes (DEGs) between the two groups were identified through bioinformatic analysis. …”
Get full text

Using Digital Phenotyping to Discriminate Unipolar Depression and Bipolar Disorder: Systematic Review by Rongrong Zhong, XiaoHui Wu, Jun Chen, Yiru Fang

“… BackgroundDifferentiating bipolar disorder (BD) from unipolar depression (UD) is essential, as these conditions differ greatly in their progression and treatment approaches. …”
Get full text

Measuring mortality due to HIV-associated tuberculosis among adults in South Africa: Comparing verbal autopsy, minimally-invasive autopsy, and research data. by Aaron S Karat, Mpho Tlali, Katherine L Fielding, Salome Charalambous, Violet N Chihota, Gavin J Churchyard, Yasmeen Hanifa, Suzanne Johnson, Kerrigan McCarthy, Neil A Martinson, Tanvier Omar, Kathleen Kahn, Daniel Chandramohan, Alison D Grant

“…International Classification of Diseases (ICD) codes do not differentiate between HIV-associated TB and HIV more generally. …”
Get full text

Elucidating the dynamic tumor microenvironment through deep transcriptomic analysis and therapeutic implication of MRE11 expression patterns in hepatocellular carcinoma by Ruiqiu Chen, Chaohui Xiao, Zizheng Wang, Guineng Zeng, Shaoming Song, Gong Zhang, Lin Zhu, Penghui Yang, Rong Liu

“…We also screened for differentially expressed genes and constructed a robust HCC prognosis model using 101 machine-learning algorithms. …”
Get full text

Development and application of an early prediction model for risk of bloodstream infection based on real-world study by Xiefei Hu, Shenshen Zhi, Yang Li, Yuming Cheng, Haiping Fan, Haorong Li, Zihao Meng, Jiaxin Xie, Shu Tang, Wei Li

“…Based on the optimal combination, six machine learning algorithms were used to construct an early BSI risk prediction model. …”
Get full text

Automated Lightweight Model for Asthma Detection Using Respiratory and Cough Sound Signals by Shuting Xu, Ravinesh C. Deo, Oliver Faust, Prabal D. Barua, Jeffrey Soar, Rajendra Acharya

“…<b>Results:</b> The proposed ensemble approach, facilitated by a majority voting approach for model efficacy evaluation, achieved acceptable ACC values of 94.05% and 83.31% for differentiating between asthma and normal cases utilising separate respiratory sounds and cough sounds, respectively. …”
Get full text

Unveiling PANoptosis in Acute Kidney Injury: An Integrative Multi-Dimensional Approach to Identify Key Biomarkers by Wang N, Zhang L, Xu Z, Xu Q, Lu Y, Niu P, Yan L, Wang L, Cao H, Shao F

“…Several machine learning algorithms were employed to determine the optimal feature genes. …”
Get full text

Multi-modal radiomics features to predict overall survival of locally advanced esophageal cancer after definitive chemoradiotherapy by Nuo Yu, Yidong Wan, Lijing Zuo, Ying Cao, Dong Qu, Wenyang Liu, Lei Deng, Tao Zhang, Wenqing Wang, Jianyang Wang, Jima Lv, Zefen Xiao, Qinfu Feng, Zongmei Zhou, Nan Bi, Tianye Niu, Xin Wang

“…The least absolute shrinkage and selection operator (LASSO) for COX algorithm was used to obtain features highly correlated with survival outcomes in the training cohort. …”
Get full text

Two-step consensus clustering approach to immune cell infiltration: An integrated exploration and validation of prognostic and immune implications in sarcomas by Ao-Yu Li, Jie Bu, Hui-Ni Xiao, Zi-Yue Zhao, Jia-Lin Zhang, Bin Yu, Hui Li, Jin-Ping Li, Tao Xiao

“…The tumor immune dysfunction evaluation (TIDE) score was also employed to quantify immunotherapy efficacy between two ImmCI score groups. …”
Get full text

A Convolutional Neural Network Tool for Early Diagnosis and Precision Surgery in Endometriosis-Associated Ovarian Cancer by Christian Macis, Miriam Santoro, Vladislav Zybin, Stella Di Costanzo, Camelia Alexandra Coada, Giulia Dondi, Pierandrea De Iaco, Anna Myriam Perrone, Lidia Strigari

“…Furthermore, the performance of each hybrid model and the majority voting ensemble of the three competing ML models were evaluated using trained and refined hybrid CNN models combined with Support Vector Machine (SVM) algorithms, with the best-performing model selected as the benchmark. …”
Get full text