Machine learning-driven multi-omics analysis identifies a prognostic gene signature associated with programmed cell death and metabolism in hepatocellular carcinoma by Xiang Li, Donghao Yin, Jiahao Geng, Yanyu Xu, Zijing Xu, Xuemeng Yang, Quanwei Li, Zimeng Shang, Zhiyun Yang, Zhong Xu, Jiabo Wang, Enxiang Zhang, Xinhua Song

“…Based on prognosis-related DEGs, patients and cells were stratified into high- and low-expression groups using corresponding computational algorithms. The intersecting DEGs from both datasets were analyzed using univariate Cox regression, and a prognostic risk score model was constructed through machine learning algorithms. …”
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Classification of <i>Verticillium dahliae</i> Vegetative Compatibility Groups (VCGs) with Machine Learning and Hyperspectral Imagery by Sudha GC Upadhaya, Chongyuan Zhang, Sindhuja Sankaran, Timothy Paulitz, David Wheeler

“…The study documented the spectral profiles of <i>V. dahliae</i>’s isolates and identified specific spectral features that can effectively differentiate among the VCGs. Multiple machine learning algorithms, including random forest and artificial neural networks (ANNs), were trained and evaluated on previously unseen isolates. …”
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Establishment of an MRI-based radiomics model for distinguishing between intramedullary spinal cord tumor and tumefactive demyelinating lesion by Zifeng Zhang, Ning Li, Yuhang Qian, Huilin Cheng

“…Abstract Objective Differentiating intramedullary spinal cord tumor (IMSCT) from spinal cord tumefactive demyelinating lesion (scTDL) remains challenging with standard diagnostic approaches. …”
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Comment on “Odontogenic Tumors: A Challenge for Clinical Diagnosis and an Opportunity for AI Innovation” by Hinpetch Daungsupawong, Viroj Wiwanitkit

“…Additionally, a more thorough exploration of the current limitations in diagnosing these tumors would have provided a more comprehensive understanding of the issue.Moving forward, future research should focus on developing AI algorithms that can accurately differentiate between different types of odontogenic tumors based on their unique characteristics. …”
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Identification of immune and major depressive disorder-related diagnostic markers for early nonalcoholic fatty liver disease by WGCNA and machine learning by Yuyun Jia, Yanping Cao, Qin Yin, Xueqian Li, Xiu Wen

“…A predictive model for SS/NASH was developed by evaluating nine machine-learning algorithms with 10-fold cross-validation on the datasets.ResultsFourteen genes strongly linked to both the immune system and the two conditions were identified. …”
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The diagnostic and prognostic value of C1orf174 in colorectal cancer by Elham Nazari, Ghazaleh Khalili-Tanha, Ghazaleh Pourali, Fatemeh Khojasteh-Leylakoohi, Hanieh Azari, Mohammad Dashtiahangar, Hamid Fiuji, Zahra Yousefli, Alireza Asadnia, Mina Maftooh, Hamed Akbarzade, Mohammadreza Nassiri, Seyed Mahdi Hassanian, Gordon A Ferns, Godefridus J Peters, Elisa Giovannetti, Jyotsna Batra, Majid Khazaei, Amir Avan

“…RNA and microRNA sequencing were analyzed using bioinformatics and machine learning algorithms to identify differentially expressed genes (DEGs), followed by validation in CRC patients. …”
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A national survey on how to improve the integration of traditional Chinese medicine and artificial intelligence: Attitudes and perceptions from medical staff by Yinger Gu, Xinyin Hu, Hye Won Lee, Zheng Yao, Tianyi Zhou, Nv Xia, Pingchun Yang, Jinglu Guo, Haifeng Huang, Lisi Wang, Wei Wang, Cheng Wang, Qiaoping Zhao, Lingling Lou, Wenjie Wu, Ke Ren, Guomei You, Longlong Fan, Jue Zhou, Fangfang Wang, Xiaoteng Chen, Fan Qu

“…The top three concerns about the potential risks associated with the integration of TCM and AI were the misinterpretation of cultural contexts, flexibility in dialectical treatment, and simplification of traditional TCM experience by algorithms. The top three most promising applications were the intelligent syndrome differentiation system (54.6 %), the TCM four diagnostic instruments (49.1 %), and the acupuncture and Tui Na robot (47.8 %). …”
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Analysis of shared pathogenic mechanisms and drug targets in myocardial infarction and gastric cancer based on transcriptomics and machine learning by Junyang Ma, Junyang Ma, Shufu Hou, Xinxin Gu, Peng Guo, Jiankang Zhu

“…The random forest and Lasso algorithms were used to identify genes with diagnostic value, leading to nomogram construction. …”
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Targeted urinary metabolomics combined with machine learning to identify biomarkers related to central carbon metabolism for IBD by Miao-Lin Lei, Guan-Wei Bi, Xiao-Lin Yin, Xiao-Lin Yin, Yue Wang, Yue Wang, Yue Wang, Zi-Ru Sun, Zi-Ru Sun, Zi-Ru Sun, Xin-rui Guo, Hui-peng Zhang, Xiao-han Zhao, Feng Li, Feng Li, Yan-Bo Yu

“…Diagnostic models were constructed using six machine learning algorithms, and their performance was evaluated by cross-validated area under the receiver operating characteristic curve (AUC). …”
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Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic... by Peng Lyu, Na Xie, Xu-peng Shao, Shuai Xing, Xiao-yue Wang, Li-yun Duan, Xue Zhao, Jia-min Lu, Rong-fei Liu, Duo Zhang, Wei Lu, Kai-liang Fan

“…Thereafter, the genes were identified utilizing machine-learning algorithms. The receiver operating characteristic curve was employed to assess the discrimination and effectiveness of the hub genes. …”
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Detection of flap malperfusion after microsurgical tissue reconstruction using hyperspectral imaging and machine learning by Marianne Maktabi, Benjamin Huber, Toni Pfeiffer, Torsten Schulz

“…Several supervised classification algorithms were evaluated to differentiate impaired perfusion from healthy tissue via HSI recordings. …”
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Analysis of signals from air conditioner compressors with ordinal patterns and machine learning by Keila Barbosa, Alejandro C Frery, George DC Cavalcanti

“…We analyze the expressiveness of the Ordinal Patterns and identify those variables that best differentiate the two machines. Furthermore, we incorporate machine learning algorithms, such as Artificial Neural Networks, Support Vector Machines, and Decision Trees, to evaluate and validate the effectiveness of Ordinal Patterns as discriminative features. …”
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Comparison of 46 Cytokines in Peripheral Blood Between Patients with Papillary Thyroid Cancer and Healthy Individuals with AI-Driven Analysis to Distinguish Between the Two Groups by Kyung-Jin Bae, Jun-Hyung Bae, Ae-Chin Oh, Chi-Hyun Cho

“…Among the five classification algorithms evaluated, XGBoost demonstrated superior performance in terms of accuracy, precision, sensitivity (recall), specificity, F1-score, and ROC-AUC score. …”
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Screening of endoplasmic reticulum stress characteristic genes and immune infiltration manifestations in chronic obstructive pulmonary disease by ZHANG Shuang, LUO Chenyang, HE Zhiyi

“…Three machine learning algorithms, LASSO, SVM-RFE, and RF, were used to screen the characteristic genes, and their diagnostic performance was verified and evaluated in the GSE10006. …”
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Ferroptosis-related genes in preeclampsia: integrative bioinformatics analysis, experimental validation and drug prediction by Lidan He, Feng Zhan, Xuemei Li, Huijuan Yang, Jianbo Wu

“…Hub genes were selected using RandomForest and LASSO algorithms. Their diagnostic potential was evaluated through ROC analysis. …”
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Assessment of copeptin and obestatin levels in coronary artery disease patients by Saraa Ali, Fatma F. Abdel Hamid, Samia Hussein, Shaimaa Wageeh, Doaa M. Ibrahim

“…They are currently assessed clinically through myocardial enzymes, electrocardiography, risk score algorithms, and angiography. It is important to find a suitable marker to differentiate them from other causes of chest pain. …”
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Identification and validation of the nicotine metabolism-related signature of bladder cancer by bioinformatics and machine learning by Yating Zhan, Min Weng, Yangyang Guo, Dingfeng Lv, Feng Zhao, Zejun Yan, Junhui Jiang, Yanyi Xiao, Lili Yao

“…Integrative machine learning combination based on 10 machine learning algorithms was used for the construction of robust signature. …”
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Bioinformatic identification of signature miRNAs associated with fetoplacental vascular dysfunction in gestational diabetes mellitus by Yulan Lu, Chunhong Liu, Xiaoxia Pang, Xinghong Chen, Chunfang Wang, Huatuo Huang

“…Firstly, differentially expressed miRNA genes (DEMGs) were evaluated using limma package. …”
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The role of mitochondria-related genes in hepatocellular carcinoma prognosis: construction of prognostic models based on machine learning by Fei Gao, Fei Teng, Yuxiang Wan, Qiaoli Zhang, Jinchang Huang

“…Weighted gene co-expression network analysis was subsequently employed to construct co-expression networks and identify key modules associated with HCC progression. We evaluated 113 machine learning algorithms to develop mitochondrial gene-based prognostic models. …”
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Comprehensive analysis of total knee arthroplasty kinematics and functional recovery: Exploring full-body gait deviations in patients with knee osteoarthritis. by Xavier Gasparutto, Alice Bonnefoy-Mazure, Michael Attias, Katia Turcot, Stéphane Armand, Hermès H Miozzari

“…Patients were clustered by applying K-means algorithms on full-body kinematic features of gait before surgery. …”
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