Prediction of Insulin Resistance in Nondiabetic Population Using LightGBM and Cohort Validation of Its Clinical Value: Cross-Sectional and Retrospective Cohort Study by Ting Peng, Rujia Miao, Hao Xiong, Yanhui Lin, Duzhen Fan, Jiayi Ren, Jiangang Wang, Yuan Li, Jianwen Chen

“…In the test group, all AUC were also greater than 0.80. The LightGBM model showed the best IR prediction performance with an accuracy of 0.7542, sensitivity of 0.6639, specificity of 0.7642, F1 ConclusionBy leveraging low-cost laboratory indicators and questionnaire data, the LightGBM model effectively predicts IR status in nondiabetic individuals, aiding in large-scale IR screening and diabetes prevention, and it may potentially become an efficient and practical tool for insulin sensitivity assessment in these settings.…”
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Potential Metabolic Markers in the Tongue Coating of Chronic Gastritis Patients for Distinguishing Between Cold Dampness Pattern and Damp Heat Pattern in Traditional Chinese Medici... by Yuan S, Zhang R, Zhu Z, Zhou X, Zhang H, Li X, Hao Y

“…We applied metabolomics to identify differential metabolites distinguishing these patterns.Methods: In this study, the first principal component was analyzed by the OPLS-DA model. The model quality was evaluated by 7-fold cross-validation, and the model validity was evaluated based on R²Y (interpretability of categorical variable Y) and Q² (predictability of the model), and the permutation test was used for further verification. …”
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Multi‐Omic Analysis Reveals a Lipid Metabolism Gene Signature and Predicts Prognosis and Chemotherapy Response in Thyroid Carcinoma by Yuqin Tu, Yanchen Chen, Linlong Mo, Guiling Yan, Jingling Xie, Xinyao Ji, Shu Chen, Changchun Niu, Pu Liao

“…The immune landscape was evaluated using the CIBERSORT algorithm, and chemotherapeutic response was predicted utilizing the “pRRophetic” R package. …”
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Tuberculosis Lesion Segmentation Improvement in X-Ray Images Using Contextual Background Label by Sahasat Khumang, Supaporn Kansomkeat, Wiwatana Tanomkiat, Sathit Intajag

“…To detect PTB at an early stage by screening chest X-Ray (CXR) images for tuberculosis (TB) lesions, we propose a semantic segmentation scheme that uses a deep learning algorithm. …”
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Collaborative Optimization Planning Method for Distribution Network Considering “Hydropower, Photovoltaic, Storage, and Charging” by Jinlin Liao, Jia Lin, Guilian Wu, Sudan Lai

“…The power output curve of a typical day is obtained using the K-means clustering algorithm and the hierarchical analysis method. The non-dominated sorting genetic algorithms II (NSGA-II) with elite strategy is used to solve the multi-objective model to obtain the Pareto solution set. …”
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Machine learning analysis of pharmaceutical cocrystals solubility parameters in enhancing the drug properties for advanced pharmaceutical manufacturing by Tareq Nafea Alharby, Bader Huwaimel

“…This comparative evaluation offers valuable perspectives on selecting models for similar regression assignments, stressing the significance of choosing the right algorithm according to particular output demands. …”
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Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease by Hsin-Yueh Liang, Hsin-Yueh Liang, Kai-Cheng Hsu, Kai-Cheng Hsu, Kai-Cheng Hsu, Shang-Yu Chien, Chen-Yu Yeh, Ting-Hsuan Sun, Meng-Hsuan Liu, Kee Koon Ng

“…The principal predictive feature variables were sex, maximum heart rate, and ST/HR index. Our model generated results within one minute after completing ExECG.ConclusionThe multimodal AI algorithm, leveraging deep learning techniques, efficiently and accurately identifies patients with significant CAD using ExECG data, aiding clinical screening in both symptomatic and asymptomatic patients. …”
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Exploring timely and safe discharge from ICU: a comparative study of machine learning predictions and clinical practices by Chao Ping Wu, Rachel Benish Shirley, Alex Milinovich, Kaiyin Liu, Eduardo Mireles-Cabodevila, Hassan Khouli, Abhijit Duggal, Anirban Bhattacharyya

“…It also highlights ML models can serve as a promising screening tool to enhance ICU discharge, presenting a pathway toward more efficient and reliable critical care decision-making.…”
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Thyroid nodule classification in ultrasound imaging using deep transfer learning by Yan Xu, Mingmin Xu, Zhe Geng, Jie Liu, Bin Meng

“…In this study, we investigate the predictive efficacy of distinguishing between benign and malignant thyroid nodules by employing traditional machine learning algorithms and a deep transfer learning model, aiming to advance the diagnostic paradigm in this field. …”
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Combinations of multimodal neuroimaging biomarkers and cognitive test scores to identify patients with cognitive impairment by Yuriko Nakaoku, Soshiro Ogata, Kiyotaka Nemoto, Chikage Kakuta, Eri Kiyoshige, Kanako Teramoto, Kiyomasa Nakatsuka, Gantsetseg Ganbaatar, Masafumi Ihara, Kunihiro Nishimura

“…Finally, MCI identification models were developed using a penalized logistic regression model with an elastic net algorithm.ResultsAmong the 148 participants (mean age, 78.6 ± 5.2 years), 44.6% were identified as having MCI. …”
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Distinguishing novel coronavirus influenza A virus pneumonia with CT radiomics and clinical features by Lianyu Sui, Huan Meng, Jianing Wang, Wei Yang, Lulu Yang, Xudan Chen, Liyong Zhuo, Lihong Xing, Yu Zhang, Jingjing Cui, Xiaoping Yin

“…And then combining these features of the two to construct a combined model. Receiver operating characteristic curve (ROC), calibration curve, and decision curve were performed to evaluate the classification of the radiomics model, clinical model and combined model. …”
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Gas adsorption meets geometric deep learning: points, set and match by Antonios P. Sarikas, Konstantinos Gkagkas, George E. Froudakis

“…Recently, machine learning (ML) pipelines have been established as the go-to method for large scale screening by means of predictive models. These are typically built in a descriptor-based manner, meaning that the structure must be first coarse-grained into a 1D fingerprint before it is fed to the ML algorithm. …”
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Prediction of clinical stages of cervical cancer via machine learning integrated with clinical features and ultrasound-based radiomics by Maochun Zhang, Qing Zhang, Xueying Wang, Xiaoli Peng, Jiao Chen, Hanfeng Yang

“…Prediction models were developed utilizing several ML algorithms by Python based on an integrated dataset of clinical features and ultrasound radiomics. …”
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The Effect of Arctic Sea‐Ice Loss on Extratropical Cyclones by Stephanie Hay, Matthew D. K. Priestley, Hao Yu, Jennifer L. Catto, James A. Screen

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Enhancing glaucoma diagnosis: Generative adversarial networks in synthesized imagery and classification with pretrained MobileNetV2 by I. Govindharaj, D. Santhakumar, K. Pugazharasi, S. Ravichandran, R. Vijaya Prabhu, J. Raja

“…This approach does not only contribute to glaucoma screening but also can also reveal the benefits of the GANs and transfer learning in medical imaging. • A GAN approach to generate high-quality fundus image datasets in an attempt to minimize dataset differences. • Implemented improved Enhanced Level Set Algorithm for Optic Cup segmentation. • Built on top of the pretrained MobileNetV2 to obtain better results of glaucoma classification.…”
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A Cell Component-Related Prognostic Signature for Head and Neck Squamous Cell Carcinoma Based on the Tumor Microenvironment by Siyu Li, Yajun Gu, Junguo Wang, Dengbin Ma, Xiaoyun Qian, Xia Gao

“…In this study, we aimed to develop a cell component-related prognostic model based on TME. We screened cell component enrichments from samples in The Cancer Genome Atlas (TCGA) HNSCC cohort using the xCell algorithm. …”
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Immunoglobulin G N-Glycosylation and Inflammatory Factors: Analysis of Biomarkers for the Diagnosis of Moyamoya Disease by Zan X, Liu C, Wang X, Sun S, Li Z, Zhang W, Sun T, Hao J, Zhang L

“…This research aimed to evaluate the diagnostic efficacy of IgG N-glycosylation for MMD.Methods: Ultra-high-performance liquid chromatography (UPLC) was employed to examine the properties of IgG N-glycans in blood samples from 116 patients with MMD and 126 controls, resulting in the quantitative determination of 24 initial glycan peaks (GP). Through the Lasso algorithm and multivariate logistic regression analysis, we constructed a diagnostic model based on initial glycans and related inflammatory factors to distinguish MMD patients from healthy individuals.Results: After adjusting for potential confounding variables, including age, fasting blood glucose (FBG), total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), neutrophil count (NEUT), and lymphocyte count (LYM), our study demonstrated significant differences in the characteristics of 6 initial glycans and 16 derived glycans between the MMD cohort and the healthy control group. …”
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Predicting cardiotoxicity in drug development: A deep learning approach by Kaifeng Liu, Huizi Cui, Xiangyu Yu, Wannan Li, Weiwei Han

“…This study not only improved the predictive accuracy of cardiotoxicity models but also promoted a more reliable and scientifically interpretable method for drug safety assessment. …”
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An incremental data-driven approach for carbon emission prediction and optimization of heat treatment processes by Qian Yi, Xin Wu, Junkang Zhuo, Congbo Li, Chuanjiang Li, Huajun Cao

“…Using life cycle assessment (LCA) theory, carbon emission sources are accurately analyzed and quantified, and a full life cycle carbon emission model is established. The key process parameters affecting part performance and carbon emission were screened through mechanism analysis, and the incremental data were fused by the Elasticity Weight Consolidation (EWC) algorithm to establish an EWC-BPNN heat treatment carbon emission prediction model. …”
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Detecting schizophrenia, bipolar disorder, psychosis vulnerability and major depressive disorder from 5 minutes of online-collected speech by Julianna Olah, Win Lee Edwin Wong, Atta-ul Raheem Rana Chaudhry, Omar Mena, Sunny X. Tang

“…Linguistic and paralinguistic features were extracted and ensemble learning algorithms (e.g., XGBoost) were used to train models. …”
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