Kun–Qi birth cohort (KQBC) study for the incidence, aetiology and risk factors of chronic kidney disease in Chinese children: a cohort profile by Yan Ma, Xingdong Chen, Jie Wei, Li Guo, Jian Huang, Li Xie, Lei Chen, Ping Lu, Wei Sun, Wei Shi, Feng Ding, Liping Zhang, Yue Zhang, Linlin Zhang, Rui Song, Yi Huang, Jun Shen, Hong Xu, Zhigang Chen, Qian Shen, Ying Ma, Yiwen Zhang, Hongxia Li, Yihui Zhai, Biao Xu, Xian Zhou, Jun Sun, Bing Wu, Rui Su, Xiaoyan Yu, Jianhong Zhu, Yihong Huang, Ling Gong, Nana Wu, Jingjing Sun, Tianyi Liu, He Yuan, Yiwen Shen, Jiaojiao Liu, Xia Han, Juanjuan Wu, Yinghua Sun, Suxiang Xu, Qinyi Zhang, Na Xie, Jianfeng Ji, Weizhi Mo, Yuanxin Liu, Xinghua Shi, Wanshan Qiu, Jiapeng Mi, Juhua Yan, Minghui Zhu, Pei Feng, Wenqian Sheng, Mingqin Tai, Chunqin Zhou, Qian Jin, Chengyu Gu, Xiujuan Huang, Qinfang Qian, Yixin Cao, Haochen Shen, Ziwen Xue, Yucui Ma, Juan Tian, Yingdan Zhang, Weiyuan Zhuang, Kaiqun Yang, Xiaohui Shen, Yanwei Gu, Beibei Shi, Chenhong Pan, Hongmei Zhou, Xiuchen Zhang, Meihua Tang, Kaixuan Gu, Hongping Liu, Yuanxian Li

“…We aim to develop multimodal risk prediction models by integrating genomics and artificial intelligence algorithms. …”
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Identification of hub genes for the diagnosis associated with heart failure using multiple cell death patterns by Hua‐jing Yuan, Hui Yu, Yi‐ding Yu, Xiu‐juan Liu, Wen‐wen Liu, Yi‐tao Xue, Yan Li

“…Bioinformatics and machine learning algorithms were utilized to screen the HF key genes and PCD‐related HF hub genes, and an HF diagnostic model was constructed on this. …”
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Research on implementation of interventions in tuberculosis control in low- and middle-income countries: a systematic review. by Frank Cobelens, Sanne van Kampen, Eleanor Ochodo, Rifat Atun, Christian Lienhardt

“…Evaluations of diagnostic and screening algorithms were more frequent (n = 19) but geographically clustered and mainly of non-comparative design. …”
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ERBB3-related gene PBX1 is associated with prognosis in patients with HER2-positive breast cancer by Shufen Mo, Haiming Zhong, Weiping Dai, Yuanyuan Li, Bin Qi, Taidong Li, Yongguang Cai

“…Utilizing three distinct machine learning algorithms, we identified three signature genes-PBX1, IGHM, and CXCL13-that exhibited significant diagnostic value within the diagnostic model. …”
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Problems and perspectives of family doctors training on the undergraduate stage by Yu. M. Kolesnik, V. D. Syvolap, N. S. Mikhaylovskaya, T.O. Kulinich

“…For working on practical part of family doctors basic skills it is planned to organize educational and training center at the family ambulatory, and its equipment with the necessary visual means, phantoms, models, simulators, diagnostic, medical apparatus and instruments. …”
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Advancement of artificial intelligence based treatment strategy in type 2 diabetes: A critical update by Aniruddha Sen, Palani Selvam Mohanraj, Vijaya Laxmi, Sumel Ashique, Rajalakshimi Vasudevan, Afaf Aldahish, Anupriya Velu, Arani Das, Iman Ehsan, Anas Islam, Sabina Yasmin, Mohammad Yousuf Ansari

“…At the same time, the rapidly increasing role of AI in diabetes care is woven into the story, mainly targeting how insulin therapy can be modified and personalized through algorithms and predictive modelling. It leaves a deep review of their pre-existing synergies, which helps understand how collaborative opportunities will unlock the future of T2DM care. …”
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Fragmenstein: predicting protein–ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding–based methodology by Matteo P. Ferla, Rubén Sánchez-García, Rachael E. Skyner, Stefan Gahbauer, Jenny C. Taylor, Frank von Delft, Brian D. Marsden, Charlotte M. Deane

“…We show that an algorithmic approach (Fragmenstein) that ‘stitches’ the ligand atoms from this structural information together can provide more accurate and reliable predictions for protein–ligand complex conformation than general methods such as pharmacophore-constrained docking. …”
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Multi-Target Mechanism of Compound Qingdai Capsule for Treatment of Psoriasis: Multi-Omics Analysis and Experimental Verification by Qiao Y, Li C, Chen C, Wu P, Yang Y, Xie M, Liu N, Gu J

“…CQC ingredients-targets network was constructed using these ingredients and their targets. Screening of CQC anti-psoriasis core targets using machine learning algorithm. …”
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Systematic elucidation of the effective constituents and potential mechanisms of Scrophulariae Radix against neoplasm based on LC-MS, network pharmacology, and molecular docking ap... by Shu-jie Yu, Xiao-bin Kong, Xin Jin, Meng-yi Shan, Gang Cheng, Pei-lu Wang, Wen-long Li, Pei-yuan Zhao, Yun-jie Sheng, Bing-qian He, Qi Shi, Hua-qiang Li, Qi-ming Zhao, Lu-ping Qin, Lu-ping Qin, Xiong-yu Meng, Xiong-yu Meng

“…As a result, the material–liquid ratio was significantly reduced from 100 g/mL to 32 g/mL, and the extraction efficiency was 1.332%, which was close to the predicted value of 1.346% in the response surface method, indicating that the algorithm model had a good fit. Next, a total of 738 compounds, including 161 terpenoids, 144 phenolic acids, 51 alkaloids, 24 flavonoids, 34 saccharides, 32 lignans and coumarins, 45 amino acids and derivatives, 23 organic acids, 134 lipids, 22 nucleotides and derivatives, and 59 other ingredients, were characterized from Scrophulariae Radix based on the accurate precursor and product ions, retention time, standards, fragmentation patterns, and previous publications. …”
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Transmitted drug resistance in the CFAR network of integrated clinical systems cohort: prevalence and effects on pre-therapy CD4 and viral load. by Art F Y Poon, Jeannette L Aldous, W Christopher Mathews, Mari Kitahata, James S Kahn, Michael S Saag, Benigno Rodríguez, Stephen L Boswell, Simon D W Frost, Richard H Haubrich

“…Aggregate effects of mutations by drug class were estimated by fitting linear models of pVL and CD4 on weighted sums over TDR mutations according to the Stanford HIV Database algorithm. …”
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Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.) by Zhu Yang, Zhu Yang, Wenjie Kan, Wenjie Kan, Ziqi Wang, Caiguo Tang, Yuan Cheng, Yuan Cheng, Dacheng Wang, Dacheng Wang, Yameng Gao, Lifang Wu, Lifang Wu

“…Additionally, the least absolute shrinkage and selection operator (LASSO) regression algorithm in machine learning was used to screen transcription factors such as bHLH, WRKY, and MYB that influenced the expression of TaAkPHY genes. …”
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Identification of three T cell-related genes as diagnostic and prognostic biomarkers for triple-negative breast cancer and exploration of potential mechanisms by Zhi-Chuan He, Zheng-Zheng Song, Zhe Wu, Peng-Fei Lin, Xin-Xing Wang

“…Differentially expressed genes (DEGs) between TNBC and other BRCA subtypes were intersected with T cell-related genes to identify candidate biomarkers. Machine learning algorithms were used to screen for key hub genes, which were then used to construct a logistic regression (LR) model. …”
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A Deep Learning Method for Pneumoconiosis Staging on Chest X-Ray Under Label Noise by Wenjian Sun, Dongsheng Wu, Jiang Shen, Yang Luo, Hao Wang, Li Min, Chunbo Luo

“…The ambiguous properties of small opacities in pneumoconiosis chest radiographs can cause diagnostic drift, which in turn leads to the presence of noisy labels in the datasets collected from hospitals that can negatively impact the generalization of deep learning models. To tackle this issue, we propose COFINE, a novel coarse-to-fine noise-tolerant deep learning method for the staging of pneumoconiosis chest radiographs, which comprises two procedures: coarse screening and fine learning. …”
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A nicotinamide metabolism-related gene signature for predicting immunotherapy response and prognosis in lung adenocarcinoma patients by Meng Wang, Wei Li, Fang Zhou, Zheng Wang, Xiaoteng Jia, Xingpeng Han

“…Four independent prognostic NMRGs (GJB3, CPA3, DKK1, KRT6A) were screened and used to construct a RiskScore model, which exhibited a strong predictive performance. …”
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Machine learning analysis of FOSL2 and RHoBTB1 as central immunological regulators in knee osteoarthritis synovium by Kun Gao, Zhenyu Huang, Zhouwei Liao, Yanfei Wang, Dayu Chen

“…We employed several machine learning algorithms, including least absolute shrinkage and selection operator and support vector machine–recursive feature elimination, to screen for key genes. …”
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A systematic review of neural network applications for groundwater level prediction by Samuel K. Afful, Cyril D. Boateng, Emmanuel Ahene, Jeffrey N. A. Aryee, David D. Wemegah, Solomon S. R. Gidigasu, Akyana Britwum, Marian A. Osei, Jesse Gilbert, Haoulata Touré, Vera Mensah

“…In conclusion, a hybrid metaheuristic algorithm produced more efficient results emphasizing their efficacy. …”
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Integrated multi-omics analysis and machine learning identify G protein-coupled receptor-related signatures for diagnosis and clinical benefits in soft tissue sarcoma by Duo Wang, Duo Wang, Duo Wang, Jihao Tu, Jihao Tu, Jianfeng Liu, Jianfeng Liu, Yuting Piao, Yuting Piao, Yiming Zhao, Yiming Zhao, Ying Xiong, Ying Xiong, Jianing Wang, Jianing Wang, Xiaotian Zheng, Xiaotian Zheng, Bin Liu, Bin Liu

“…We developed a novel machine learning framework that incorporated 12 machine learning algorithms and their 127 combinations to construct a consensus GPRS to screen biomarkers with diagnostic significance and clinical translation, which was assessed by the internal and external validation datasets. …”
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Neutrophil- and Endothelial Cell-Derived Extracellular Microvesicles Are Promising Putative Biomarkers for Breast Cancer Diagnosis by Thayse Batista Moreira, Marina Malheiros Araújo Silvestrini, Ana Luiza de Freitas Magalhães Gomes, Kerstin Kapp Rangel, Álvaro Percínio Costa, Matheus Souza Gomes, Laurence Rodrigues do Amaral, Olindo Assis Martins-Filho, Paulo Guilherme de Oliveira Salles, Letícia Conceição Braga, Andréa Teixeira-Carvalho

“…Machine learning approaches were employed to determine the performance of MVs to identify BC and to propose BC classifier algorithms. <b>Results:</b> Patients with BC had more neutrophil- and endothelial cell-derived MVs than controls before treatment. …”
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Uso de inteligencia artificial para predecir complicaciones en cirugías de columna toracolumbar degenerativa: revisión sistemática by G. Ricciardi, J.I. Cirillo Totera, R. Pons Belmonte, L. Romero Valverde, F. López Muñoz, A. Manríquez Díaz

“…Due to heterogeneity in samples, outcomes of interest, and algorithm evaluation metrics, a meta-analysis was not performed. …”
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[Translated article] Use of artificial intelligence to predict complications in degenerative thoracolumbar spine surgery: A systematic review by G. Ricciardi, J.I. Cirillo Totera, R. Pons Belmonte, L. Romero Valverde, F. López Muñoz, A. Manríquez Díaz

“…In 5 (41.6%) articles, the effectiveness of artificial intelligence predictive models was compared with conventional techniques. …”
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