Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response by Sha-Zhou Li, Hai-Ying Sun, Yuan Tian, Liu-Qing Zhou, Tao Zhou

“…Therefore, the identification of reliable biomarker is crucial to enhance the accuracy of screening and treatment strategies for HNSCC.MethodTo develop and identify a machine learning-derived prognostic model (MLDPM) for HNSCC, ten machine learning algorithms, namely CoxBoost, elastic network (Enet), generalized boosted regression modeling (GBM), Lasso, Ridge, partial least squares regression for Cox (plsRcox), random survival forest (RSF), stepwise Cox, supervised principal components (SuperPC), and survival support vector machine (survival-SVM), along with 81 algorithm combinations were utilized. …”
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A phase separation-related gene signature for prognosis prediction and immunotherapy response evaluation in gastric cancer with targeted natural compound discovery by Yanjuan Jia, Yuanyuan Ma, Zhenhao Li, Wenze Zhang, Rukun Lu, Wanxia Wang, Chaojun Wei, Chunyan Wei, Yonghong Li, Xiaoling Gao, Tao Qu

“…Immune checkpoint inhibitor (ICI) response between PS-related high- and low-risk groups was evaluated using TIDE algorithm scores. Potential therapeutic agents targeting signature genes were screened via Connectivity Map and HERB database analyses, followed by molecular docking validation. …”
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Development of a method for differential diagnosis of iron deficiency anemia and anemia of chronic disease based on demographic data and routine laboratory tests using machine lear... by N. V. Varekha, N. I. Stuklov, K. V. Gordienko, R. R. Gimadiev, O. B. Shchegolev, S. N. Kislaya, E. V. Gubina, A. A. Gurkina

“…The study of machine learning methods, a branch of artificial intelligence science, is relevant for the development of optimal screening strategies, identification of risk groups, and application of less expensive and more accessible laboratory tests to assess the body iron status.   …”
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Hydraulic Pump Fault Diagnosis Method Based on EWT Decomposition Denoising and Deep Learning on Cloud Platform by Wanlu Jiang, Zhenbao Li, Sheng Zhang, Teng Wang, Shuqing Zhang

“…Compared with ensemble empirical mode decomposition (EEMD) and complementary ensemble empirical mode decomposition (CEEMD), the results show that the axial piston pump fault diagnosis algorithm based on EWT and 1D-CNN has higher fault identification accuracy.…”
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A prospective multicenter randomized controlled trial on artificial intelligence assisted colonoscopy for enhanced polyp detection by Dong Kyun Park, Eui Joo Kim, Jong Pil Im, Hyun Lim, Yun Jeong Lim, Jeong-Sik Byeon, Kyoung Oh Kim, Jun-Won Chung, Yoon Jae Kim

“…Abstract Colon polyp detection and removal via colonoscopy are essential for colorectal cancer screening and prevention. This study aimed to develop a colon polyp detection program based on the RetinaNet algorithm and verify its clinical utility. …”
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Proposed Comprehensive Methodology Integrated with Explainable Artificial Intelligence for Prediction of Possible Biomarkers in Metabolomics Panel of Plasma Samples for Breast Canc... by Cemil Colak, Fatma Hilal Yagin, Abdulmohsen Algarni, Ali Algarni, Fahaid Al-Hashem, Luca Paolo Ardigò

“…The SHapley Additive Descriptions (SHAP) analysis evaluated the optimal prediction model for interpretability. <i>Results</i>: The RF algorithm showed improved accuracy (0.963 ± 0.043) and sensitivity (0.977 ± 0.051); however, LightGBM achieved the highest ROC AUC (0.983 ± 0.028). …”
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The application of compressed sensing on tumor mutation burden calculation from overlapped pooling sequencing data by Yue Cui, Yi Qiao, Rongming An, Xuan Pan, Jing Tu

“…Additionally, we performed an assessment of the reconstruction efficiency of both the BP model and the OMP model.…”
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Research on predicting the risk level of coal mine roof accident based on machine learning by Zhao-Yang Guan, Jin-Ling Xie, Shen-Kuang Wu, Chao Liang

“…Finally, KNN, SVM and DT algorithms are used to evaluate the model performance. …”
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Neural network analysis of pharyngeal sounds can detect obstructive upper respiratory disease in brachycephalic dogs. by Andrew McDonald, Anurag Agarwal, Ben Williams, Nai-Chieh Liu, Jane Ladlow

“…Evaluated via nested cross validation, the neural network predicts the presence of clinically significant BOAS with an area under the receiving operating characteristic of 0.85, an operating sensitivity of 71% and a specificity of 86%. The algorithm could enable widespread screening for BOAS to be conducted by both owners and veterinarians, improving treatment and breeding decisions.…”
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Examination of Teacher Candidates’ Intercultural Sensitivity Levels by CART Analysis by Nesrin Hark Söylemez

“…The study was conducted on a voluntary basis. A relational screening model was employed to assess the intercultural sensitivity levels. …”
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The Chaotic Prediction for Aero-Engine Performance Parameters Based on Nonlinear PLS Regression by Chunxiao Zhang, Junjie Yue

“…At the same time, the forecast error is less than that of nonlinear PLS algorithm through bootstrap test screening.…”
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Hyperspectral estimation of chlorophyll content in grapevine based on feature selection and GA-BP by YaFeng Li, XinGang Xu, WenBiao Wu, Yaohui Zhu, LuTao Gao, XiangTai Jiang, Yang Meng, GuiJun Yang, HanYu Xue

“…Comparison of the prediction ability of Random Forest Regression (RFR) algorithm, Support Vector Machine Regression (SVR) model, and Genetic Algorithm-Based Neural Network (GA-BP) on grape LCC based on sensitive features. …”
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Research on Feature Extraction of Performance Degradation for Flexible Material R2R Processing Roller Based on PCA by Yaohua Deng, Huiqiao Zhou, Kexing Yao, Zhiqi Huang, Chengwang Guo

“…The Jacobi iteration method was introduced to derive the algorithm for solving eigenvalue and eigenvector of the covariance matrix. …”
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Permeability Predictions for Tight Sandstone Reservoir Using Explainable Machine Learning and Particle Swarm Optimization by Jing-Jing Liu, Jian-Chao Liu

“…The particle swarm optimization algorithm is then used to optimize the hyperparameters of the XGBoost model. …”
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Module Partition of Mechatronic Products Based on Core Part Hierarchical Clustering and Non-Core Part Association Analysis by Shuai Wang, Yi-Fei Song, Guang-Yu Zou, Jia-Xiang Man

“…Firstly, the core part screening method is used to simplify the structural model of mechatronic products and reduce the difficulty of modeling. …”
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A Dynamic Adaptive Ensemble Learning Framework for Noninvasive Mild Cognitive Impairment Detection: Development and Validation Study by Aoyu Li, Jingwen Li, Yishan Hu, Yan Geng, Yan Qiang, Juanjuan Zhao

“…To address the challenges (eg, the curse of dimensionality and increased model complexity) posed by high-dimensional features, we developed a dynamic adaptive feature selection optimization algorithm to identify the most impactful subset of features for classification performance. …”
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AI-Powered Synthesis of Structured Multimodal Breast Ultrasound Reports Integrating Radiologist Annotations and Deep Learning Analysis by Khadija Azhar, Byoung-Dai Lee, Shi Sub Byon, Kyu Ran Cho, Sung Eun Song

“…Additionally, the deep-learning-based algorithm, utilizing DenseNet-121 as its core model, achieved an overall accuracy of 0.865, precision of 0.868, recall of 0.847, F1-score of 0.856, and area under the receiver operating characteristics of 0.92 in classifying tissue stiffness in breast US shear-wave elastography (SWE-mode) images. …”
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High-accuracy physical property prediction for pure organics via molecular representation learning: bridging data to discovery by Qi Ou, Hongshuai Wang, Minyang Zhuang, Shangqian Chen, Lele Liu, Ning Wang, Zhifeng Gao

“…We employed a 3D transformer-based molecular representation learning algorithm to create the Org-Mol pre-trained model, using 60 million semi-empirically optimized small organic molecule structures. …”
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Is cardiovascular risk profiling from UK Biobank retinal images using explicit deep learning estimates of traditional risk factors equivalent to actual risk measurements? A prospec... by Kohji Nishida, Ryo Kawasaki, Yiming Qian, Liangzhi Li, Yuta Nakashima, Hajime Nagahara

“…This two-stage approach provides human interpretable information between stages, which helps clinicians gain insights into the screening process copiloting with the DL model.…”
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Machine learning and SHAP value interpretation for predicting the response to neoadjuvant chemotherapy and long-term clinical outcomes in Chinese female breast cancer by Quan Yuan, Rongjie Ye, Yao Qian, Hao Yu, Yuexin Zhou, Xiaoqiao Cui, Feng Liu, Ming Niu

“…The Least Absolute Shrinkage and Selection Operator (LASSO) Cox algorithm, combined with XGBoost and Random Forest (RF) models, identified 9 overlapping prognostic features, enhancing the nomogram’s predictive accuracy for overall survival (OS). …”
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