Estimation of Canopy Chlorophyll Content of Apple Trees Based on UAV Multispectral Remote Sensing Images by Juxia Wang, Yu Zhang, Fei Han, Zhenpeng Shi, Fu Zhao, Fengzi Zhang, Weizheng Pan, Zhiyong Zhang, Qingliang Cui

“…The estimation models for the SPAD values in different growth stages were, respectively, established through five machine learning algorithms: multiple linear regression (MLR), partial least squares regression (PLSR), support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost). …”
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Fundus camera-based precision monitoring of blood vitamin A level for Wagyu cattle using deep learning by Nanding Li, Naoshi Kondo, Yuichi Ogawa, Keiichiro Shiraga, Mizuki Shibasaki, Daniele Pinna, Moriyuki Fukushima, Shinichi Nagaoka, Tateshi Fujiura, Xuehong De, Tetsuhito Suzuki

“…This study developed a handheld camera system capable of capturing cattle fundus images and predicting vitamin A levels in real time using deep learning. 4000 fundus images from 50 Japanese Black cattle were used to train and test the prediction algorithms, and the model achieved an average 87%, 83%, and 80% accuracy for three levels of vitamin A deficiency classification (particularly 87% for severe level), demonstrating the effectiveness of camera system in vitamin A deficiency prediction, especially for screening and early warning. …”
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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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Toxoplasmosis seroprevalence in rheumatoid arthritis patients: A systematic review and meta-analysis. by Zahra Hosseininejad, Mehdi Sharif, Shahabeddin Sarvi, Afsaneh Amouei, Seyed Abdollah Hosseini, Tooran Nayeri Chegeni, Davood Anvari, Reza Saberi, Shaban Gohardehi, Azadeh Mizani, Mitra Sadeghi, Ahmad Daryani

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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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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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The Future of Minimally Invasive GI and Capsule Diagnostics (REFLECT), October 2024 by Lea Østergaard Hansen, Alexandra Agache, Anastasios Koulaouzidis

“…The symposium also highlighted the significance of predictive models for patient selection and developments in panenteric CE. …”
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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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A low-cost platform for automated cervical cytology: addressing health and socioeconomic challenges in low-resource settings by José Ocampo-López-Escalera, Héctor Ochoa-Díaz-López, Xariss M. Sánchez-Chino, César A. Irecta-Nájera, Saúl D. Tobar-Alas, Martha Rosete-Aguilar

“…This disease is preventable and curable if detected in early stages, making regular screening critically important. Cervical cytology, the most widely used screening method, has proven highly effective in reducing cervical cancer incidence and mortality in high income countries. …”
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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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Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods by Bin Zhang, Shengsheng Huang, Chenxing Zhou, Jichong Zhu, Tianyou Chen, Sitan Feng, Chengqian Huang, Zequn Wang, Shaofeng Wu, Chong Liu, Xinli Zhan

“…The intersections of the variables screened by the aforementioned algorithms were utilized to construct a nomogram model for predicting AHD in patients. …”
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Breast lesion classification via colorized mammograms and transfer learning in a novel CAD framework by Abbas Ali Hussein, Morteza Valizadeh, Mehdi Chehel Amirani, Sedighe Mirbolouk

“…As a consequence of this conversion, breast tumors with anomalies become more visible, which allows us to extract more accurate features about them. …”
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Identification of specific metabolic pathways as druggable targets regulating the sensitivity to cyanide poisoning. by Patrick Y Sips, Xu Shi, Gabriel Musso, Anjali K Nath, Yanbin Zhao, Jason Nielson, Jordan Morningstar, Amy E Kelly, Brittney Mikell, Eva Buys, Vikhyat Bebarta, Jared Rutter, V Jo Davisson, Sari Mahon, Matthew Brenner, Gerry R Boss, Randall T Peterson, Robert E Gerszten, Calum A MacRae

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Impact of ITH on PRAD patients and feasibility analysis of the positive correlation gene MYLK2 applied to PRAD treatment by Chuanyu Ma, Chuanyu Ma, Guandu Li, Xiaohan Song, Xiaochen Qi, Tao Jiang

“…GO and KEGG pathway enrichment analyses were performed on these 103 positively correlated differentially expressed genes, and the proportion and type of tumour-infiltrating immune cells were assessed by TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCPCOUNTER, XCELL and EPIC algorithms in patients. In addition, we calculated the relevance of immunotherapy and predicted various drugs that might be used for treatment and evaluated the predictive power of survival models under multiple machine learning algorithms through the training set TCGA-PRAD versus the validation set PRAD-FR cohort. …”
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Focusing on scRNA-seq-Derived T Cell-Associated Genes to Identify Prognostic Signature and Immune Microenvironment Status in Low-Grade Glioma by Jiayu Wen, Qiaoyi Huang, Jiuxiu Yao, Wei Wei, Zehui Li, Huiqin Zhang, Surui Chang, Hui Pei, Yu Cao, Hao Li

“…In addition, bulk RNA data of 975 LGG samples were collected for model construction. Algorithms such as TIMER, CIBERSORT, QUANTISEQ, MCPCOUTER, XCELL, and EPIC were used to depict the tumor microenvironment landscape. …”
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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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Serum Lipid Biomarkers for the Diagnosis and Monitoring of Neuromyelitis Optica Spectrum Disorder: Towards Improved Clinical Management by Li R, Wang J, Wang J, Xie W, Song P, Zhang J, Xu Y, Tian D, Wu L, Wang C

“…Subsequently, we validated the candidate biomarkers in the retrospective cohort and developed diagnostic models using a random forest algorithm. The association between these lipid biomarkers and disease activity was further evaluated in longitudinal analysis.Results: Our analysis identified a panel of serum lipid-related biomarkers that demonstrated significant differences between NMOSD patients and controls. …”
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