Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review by Suli Li, Yihang Chu, Ying Wang, Yantong Wang, Shipeng Hu, Xiangye Wu, Xinwei Qi

“…This suggests that state-of-the-art ML-based algorithms for distinguishing melanoma from benign nevi may be ready for clinical use. …”
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Spatial and temporal distribution patterns and factors influencing hepatitis B in China: a geo-epidemiological study by Kang Fang, Na Cheng, Chuang Nie, Wentao Song, Yunkang Zhao, Jie Pan, Qi Yin, Jiwei Zheng, Qinglin Chen, Tianxin Xiang

“…Spatial autocorrelation analysis and spatiotemporal scanning were used to analyze the spatiotemporal distribution characteristics. The random forest algorithm was used to screen the potential influencing factors. …”
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Multimodal marvels of deep learning in medical diagnosis using image, speech, and text: A comprehensive review of COVID-19 detection by Md Shofiqul Islam, Khondokar Fida Hasan, Hasibul Hossain Shajeeb, Humayan Kabir Rana, Md. Saifur Rahman, Md. Munirul Hasan, AKM Azad, Ibrahim Abdullah, Mohammad Ali Moni

“…We explore the architecture of deep learning models, emphasising their data-specific structures and underlying algorithms. …”
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Plasma FGF2 and YAP1 as novel biomarkers for MCI in the elderly: analysis via bioinformatics and clinical study by Yejing Zhao, Yejing Zhao, Xiang Wang, Jie Zhang, Yanyan Zhao, Yi Li, Ji Shen, Ying Yuan, Jing Li

“…To address this gap, datasets GSE29378 and GSE12685 were selected to screen differentially expressed genes (DEGs), and hub genes were identified by different algorithms. …”
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Exhaled volatile organic compounds as novel biomarkers for early detection of COPD, asthma, and PRISm: a cross-sectional study by Jiaxin Tian, Qiurui Zhang, Minhua Peng, Leixin Guo, Qianqian Zhao, Wei Lin, Sitong Chen, Xuefei Liu, Simin Xie, Wenxin Wu, Yijie Li, Junqi Wang, Jin Cao, Ping Wang, Min Zhou

“…Subsequently, classification models were established by machine learning algorithms, based on these VOC markers along with baseline characteristics. …”
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Keypoint Detection Based on Curvature Grouping and Adaptive Sampling by Bifu Li, Yu Cheng, Weitong Li

“…In the keypoint detection algorithm, the farthest point sampling methods and random sampling methods are usually used to select candidate points, then keypoints are screened out from the neighborhood of the candidate points. …”
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Efficient evidence selection for systematic reviews in traditional Chinese medicine by Yizhen Li, Zhe Huang, Zhongzhi Luan, Shujing Xu, Yunan Zhang, Lin Wu, Darong Wu, Dongran Han, Yixing Liu

“…Methods We integrated an established deep learning model (Evi-BERT combined rule-based method) with Boolean logic algorithms and an expanded retrieval strategy to automatically and accurately select potential evidence with minimal human intervention. …”
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CNN-Based Medical Ultrasound Image Quality Assessment by Siyuan Zhang, Yifan Wang, Jiayao Jiang, Jingxian Dong, Weiwei Yi, Wenguang Hou

“…At last, some tests are taken to evaluate the IQA models. They show that the CNN-based IQA is feasible and effective.…”
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Advances in the application of machine learning technology in the field of environmental health by ZHENG Yu, LI Cheng, HU Guiping, JIA Guang

“…However, the application of ML technology in the field of environmental health still faces challenges such as data quality, model interpretability, and interdisciplinary cooperation. …”
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Machine learning-based ultrasound radiomics for predicting risk of recurrence in breast cancer by Wei Fan, Hao Cui, Xiaoxue Liu, Xudong Zhang, Xinran Fang, Junjia Wang, Zihao Qin, Xiuhua Yang, Jiawei Tian, Lei Zhang

“…Subsequently, radiomics models were constructed with eight machine learning algorithms. …”
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Immune Evasion Mechanism Mediated by ITPRIPL1 and Its Prognostic Implications in Glioma by Zou Xiaoyun, Ye Wenhao, Wu Huan, Yang Yuanyuan, Liu Changqing, Wen Hebao, Ma Caiyun

“…Ninety‐eight machine learning algorithm combinations were screened to identify the optimal predictive model. …”
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Case-control study combined with machine learning techniques to identify key genetic variations in GSK3B that affect susceptibility to diabetic kidney diseases by Jinfang Song, Yi Xu, Liu Xu, Tingting Yang, Ya Chen, Changjiang Ying, Qian Lu, Tao Wang, Xiaoxing Yin

“…On the other hand, the expression levels and kinase activity of GSK3β in exosomes differed significantly between patients with different genotypes of the GSK3B, suggesting that the effect of GSK3B gene polymorphisms on GSK3β expression and activity may be an important mechanism leading to individual differences in susceptibility to DKD. XG Boost algorithm model identified rs60393216 and rs1488766 as important biomarkers for clinical early warning of DKD.…”
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Identification and validation of endoplasmic reticulum stress-related diagnostic biomarkers for type 1 diabetic cardiomyopathy based on bioinformatics and machine learning by Qiao Tang, Yanwei Ji, Zhongyuan Xia, Yuxi Zhang, Chong Dong, Chong Dong, Qian Sun, Shaoqing Lei

“…The ERDEGs diagnostic model was developed based on a combination of LASSO and Random Forest approaches, and the diagnostic performance was evaluated by the area under the receiver operating characteristic curve (ROC-AUC) and validated against external datasets. …”
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Integrative multi-omics analysis reveals the role of toll-like receptor signaling in pancreatic cancer by Jie Peng, Jiaao Sun, Youfeng Yu, Qihang Yuan, Yong Zhang

“…In the process of building prognostic models, we screened 33 core genes related to the prognosis of pancreatic cancer, and combined a series of machine learning algorithms to build the prognosis model of pancreatic cancer. …”
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Effect of miR-200c on inducing autophagy and apoptosis of HT22 cells from mouse hippocampal neurons via regulating PRDM1 protein: a bioinformatics analysis by W. Wu, J. Fu, Q. Liu, Q. Wang, S. Gao, X. Deng, C. Shen

“…The Support Vector Machine (SVM) algorithm in the Weka software was used to process, model, and screen the available miRNA data. …”
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Application of Elastic networks and Bayesian networks to explore influencing factors associated with arthritis in middle-aged and older adults in the Chinese community by Tao Zhong, Tianlun Li, Jiapei Hu, Jiayi Hu, Li Jin, Yuxuan Xie, Bin Ma, Bin Ma, Dailun Hu

“…First, Elastic networks (ENs) were used to screen for features closely associated with arthritis, and we subsequently incorporated these features into the construction of the BNs model. …”
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Unlocking the bottleneck in forward genetics using whole-genome sequencing and identity by descent to isolate causative mutations. by Katherine R Bull, Andrew J Rimmer, Owen M Siggs, Lisa A Miosge, Carla M Roots, Anselm Enders, Edward M Bertram, Tanya L Crockford, Belinda Whittle, Paul K Potter, Michelle M Simon, Ann-Marie Mallon, Steve D M Brown, Bruce Beutler, Christopher C Goodnow, Gerton Lunter, Richard J Cornall

“…Forward genetics screens with N-ethyl-N-nitrosourea (ENU) provide a powerful way to illuminate gene function and generate mouse models of human disease; however, the identification of causative mutations remains a limiting step. …”
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Utilising AI technique to identify depression risk among doctoral students by Changhong Teng, Chunmei Yang, Qiushi Liu

“…Based on the data from the 2019 Nature Global Doctoral Student Survey, we first screened 13 highly relevant features from a total of 37 features potentially related to the risk of depression among doctoral students by Random Forest algorithm. …”
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3D Reconstruction of the Human Colon From Capsule Endoscope Video by Pal Anders Floor, Ivar Farup, Marius Pedersen

“…As capsule endoscope images contain distortion and artifacts non-ideal for many 3D reconstruction algorithms, the problem is challenging. However, recent developments of virtual graphics-based models of the human gastrointestinal system, where distortion and artifacts can be enabled or disabled, make it possible to “dissect” the problem. …”
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AI-based classification of anticancer drugs reveals nucleolar condensation as a predictor of immunogenicity by Giulia Cerrato, Peng Liu, Liwei Zhao, Adriana Petrazzuolo, Juliette Humeau, Sophie Theresa Schmid, Mahmoud Abdellatif, Allan Sauvat, Guido Kroemer

“…Conclusions We developed AI-based algorithms for predicting CON-inducing drugs based on molecular descriptors and their validation using automated micrographs analysis, offering a new approach for screening ICD inducers with minimized adverse effects in cancer therapy.…”
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