Identification of a novel immunogenic cell death-related classifier to predict prognosis and optimize precision treatment in hepatocellular carcinoma by Dongjing Zhang, Bingyun Lu, Qianqian Ma, Wen Xu, Qi Zhang, Zhiqi Xiao, Yuanheng Li, Ren Chen, An-jiang Wang

“…A reliable risk model named ICD score was constructed via machine learning algorithms to assess the immunological status, therapeutic responses, and clinical outcomes of individual HCC patients. …”
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PLCG2 and IFNAR1: The Potential Biomarkers Mediated by Immune Infiltration and Osteoclast Differentiation of Ankylosing Spondylitis in the Peripheral Blood by Bo Han, Qiaobo Xie, Weishi Liang, Peng Yin, Xianjun Qu, Yong Hai

“…In the WGCNA, modules of MCODE with different algorithms were used to find 33 key genes that were related to each other in a strong way. …”
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A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study. by Ilaria Amodeo, Giorgio De Nunzio, Genny Raffaeli, Irene Borzani, Alice Griggio, Luana Conte, Francesco Macchini, Valentina Condò, Nicola Persico, Isabella Fabietti, Stefano Ghirardello, Maria Pierro, Benedetta Tafuri, Giuseppe Como, Donato Cascio, Mariarosa Colnaghi, Fabio Mosca, Giacomo Cavallaro

“…Data from different sources will be integrated and analyzed using ML and DL, and forecasting algorithms will be developed for each outcome. Methods of data augmentation and dimensionality reduction (feature selection and extraction) will be employed to increase sample size and avoid overfitting. …”
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Integrating machine learning with mendelian randomization for unveiling causal gene networks in glioblastoma multiforme by Lixin Du, Pan Wang, Xiaoting Qiu, Zhigang Li, Jianlan Ma, Pengfei Chen

“…Methods This study employed a comprehensive analysis approach integrating 113 machine learning algorithms with Mendelian Randomization (MR) analysis to investigate the molecular underpinnings of GBM. …”
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Prediksi Mahasiswa Drop-Out Di Universitas XYZ by Tubagus Ahmad Marzuqi, Evelline Kristiani, Marcel

“…The results indicate that the predictive model using the Random Forest algorithm achieved an accuracy of 99.67%. The Gradient Boosting algorithm yielded an accuracy of 99.21%, while the Decision Tree algorithm achieved 98.67% accuracy. …”
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Evaluating sowing uniformity in hybrid rice using image processing and the OEW-YOLOv8n network by Zehua Li, Zehua Li, Yihui Pan, Xu Ma, Yongjun Lin, Xicheng Wang, Hongwei Li

“…Compared to the advanced object detection algorithms such as Faster-RCNN, SSD, YOLOv4, YOLOv5s YOLOv7-tiny, and YOLOv10s, the mAP of the new network increased by 5.2%, 7.8%, 4.9%, 2.8% 2.9%, and 3.3%, respectively. …”
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Rapid detection of carbapenem-resistant Escherichia coli and carbapenem-resistant Klebsiella pneumoniae in positive blood cultures via MALDI-TOF MS and tree-based machine learning... by Xiaobo Xu, Zhaofeng Wang, Erjie Lu, Tao Lin, Hengchao Du, Zhongfei Li, Jiahong Ma

“…Results The collected MALDI-TOF MS data of 640 E. coli and 444 K. pneumoniae were analysed by machine learning algorithms. The area under the receiver operating characteristic curve (AUROC) for the diagnosis of E. coli susceptibility to carbapenems by the DT, RF, GBM, XGBoost, and ERT models were 0.95, 1.00, 0.99, 0.99, and 1.00, respectively, and the accuracy in predicting 149 E. coli-positive blood cultures were 0.89, 0.92, 0.90, 0.92, and 0.86, respectively. …”
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Exploring the comorbidity mechanisms between atherosclerosis and hashimoto’s thyroiditis based on microarray and single-cell sequencing analysis by Yirong Ma, Shuguang Wu, Junyu Lai, Qiang Wan, Jingxuan Hu, Yanhong Liu, Ziyi Zhou, Jianguang Wu

“…Two pivotal genes, PTPRC and TYROBP, were identified using five algorithms from the cytoHubba plugin. Validation through external datasets confirmed their substantial diagnostic value for AS and HT. …”
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Integrating Remote Sensing and Soil Features for Enhanced Machine Learning-Based Corn Yield Prediction in the Southern US by Sayantan Sarkar, Javier M. Osorio Leyton, Efrain Noa-Yarasca, Kabindra Adhikari, Chad B. Hajda, Douglas R. Smith

“…Four regression and machine learning algorithms were evaluated for yield prediction: linear regression, random forest, extreme gradient boosting, and gradient boosting regressor. …”
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Detection of I491F and V170F rpoB mutations associated with misdiagnosis of rifampicin resistance among patients with drug-susceptible tuberculosis treatment failure, Myanmar, 2022 by Phyu Win Ei, Mi Mi Htwe, Myat Htut Nyunt, Aye Su Mon, Zaw Myint, Wint Wint Nyunt, Su Mon Win, Sandar Aung, Wai Myat Thwe, Wah Wah Aung

“…This demonstrates a gap in current World Health Organization-endorsed algorithms for capturing all multidrug-resistant-TB strains.…”
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Prevention and management of degenerative lumbar spine disorders through artificial intelligence-based decision support systems: a systematic review by Paolo Giaccone, Federico D’Antoni, Fabrizio Russo, Luca Ambrosio, Giuseppe Francesco Papalia, Onorato d’Angelis, Gianluca Vadalà, Albert Comelli, Luca Vollero, Mario Merone, Rocco Papalia, Vincenzo Denaro

“…Several different machine learning and deep learning algorithms were employed, and their predictive ability on clinical, demographic, psychosocial, and imaging data was assessed. …”
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Using remote sensing and machine learning to generate 100-cm soil moisture at 30-m resolution for the black soil region of China: Implication for agricultural water management by Liwen Chen, Boting Hu, Jingxuan Sun, Y. Jun Xu, Guangxin Zhang, Hongbo Ma, Jingquan Ren

“…However, soil moisture datasets or algorithms fail to simultaneously meet the requirements of multi-layer, high spatiotemporal resolution soil moisture information for large-scale agricultural production areas. …”
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Characterisation of cardiovascular disease (CVD) incidence and machine learning risk prediction in middle-aged and elderly populations: data from the China health and retirement lo... by Qing Huang, Zihao Jiang, Bo Shi, Jiaxu Meng, Li Shu, Fuyong Hu, Jing Mi

“…Five machine learning (ML) algorithms were employed for risk prediction. Data preprocessing included missing value imputation via random forest. …”
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An investigation of machine learning methods applied to genomic prediction in yellow-feathered broilers by Bogong Liu, Huichao Liu, Junhao Tu, Jian Xiao, Jie Yang, Xi He, Haihan Zhang

“…The prediction accuracy of above algorithms could be optimized using genome-wide association study (GWAS) to select subsets of significant SNPs. …”
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Deep learning and hyperspectral features for seedling stage identification of barnyard grass in paddy field by Siqiao Tan, Qiang Xie, Wenshuai Zhu, Yangjun Deng, Lei Zhu, Xiaoqiao Yu, Zheming Yuan, Zheming Yuan, Yuan Chen, Yuan Chen

“…Notably, this surpasses the capabilities of other models that rely on amalgamations of machine learning algorithms and feature dimensionality reduction methods. …”
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Synergetic monitoring of pressure and temperature stimulations in multisensory electronic skin based on time decoupling effect by Zhiyi Gao, Ye Zhang, Zhenyu Hu, Dongdong Zhang, Shengbin Li, Huiyun Xiao, Ziyin Xiang, Dan Xu, Haifeng Zhang, Yuanzhao Wu, Yiwei Liu, Jie Shang, Runwei Li

“…However, the intuitive and interference‐free reading of multiple sensory signals without involving complex algorithms is a critical challenge. Herein, we propose a flexible multisensory E‐skin by developing a highly homogeneous dispersion of BaTiO3 nanoparticles in polydimethylsiloxane dielectric layer. …”
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A minimal gene set characterizes TIL specific for diverse tumor antigens across different cancer types by Zhen Zeng, Tianbei Zhang, Jiajia Zhang, Shuai Li, Sydney Connor, Boyang Zhang, Yimin Zhao, Jordan Wilson, Dipika Singh, Rima Kulikauskas, Candice D. Church, Thomas H. Pulliam, Saumya Jani, Paul Nghiem, Suzanne L. Topalian, Patrick M. Forde, Drew M. Pardoll, Hongkai Ji, Kellie N. Smith

“…Our three-gene “MANAscore” algorithm outperforms other RNAseq-based algorithms in identifying validated neoantigen-specific CD8+ clones, and accurately identifies TILs that recognize other classes of tumor antigens, including cancer testis antigens, endogenous retroviruses and viral oncogenes. …”
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Combining a Risk Factor Score Designed From Electronic Health Records With a Digital Cytology Image Scoring System to Improve Bladder Cancer Detection: Proof-of-Concept Study by Sandie Cabon, Sarra Brihi, Riadh Fezzani, Morgane Pierre-Jean, Marc Cuggia, Guillaume Bouzillé

“…MethodsThe first step relied on designing a predictive model based on clinical data (ie, risk factors identified in the literature) extracted from the clinical data warehouse of the Rennes Hospital and machine learning algorithms (logistic regression, random forest, and support vector machine). …”
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A Cross-Stage Focused Small Object Detection Network for Unmanned Aerial Vehicle Assisted Maritime Applications by Gege Ding, Jiayue Liu, Dongsheng Li, Xiaming Fu, Yucheng Zhou, Mingrui Zhang, Wantong Li, Yanjuan Wang, Chunxu Li, Xiongfei Geng

“…The CFSD-UAVNet model was evaluated on the publicly available SeaDronesSee maritime dataset and compared with other cutting-edge algorithms. The experimental results showed that the CFSD-UAVNet model achieved an mAP@50 of 80.1% with only 1.7 M parameters and a computational cost of 10.2 G, marking a 12.1% improvement over YOLOv8 and a 4.6% increase compared to DETR. …”
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Throughout Maximization for IRS-Assisted WPCN With Hybrid TDMA-NOMA Scheme by Yizheng Ma, Ruoyi Wu, Yi Zhang, Yupeng Shang, Linzhen Zhu

“…Additionally, the semi-closed-form expressions of IRS phase shifts during WET and WIT stages are iteratively achieved through Riemannian Manifold Optimization (RMO) and quadratic transformation (QT)-based Alternating Direction Method of Multipliers (ADMM) algorithms. Finally, the numerical results are demonstrated to highlight the effectiveness of the proposed algorithm, the optimal IRS phase shift design, and the optimal WET time scheduling design in comparison to the benchmarks.…”
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