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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Study on classification of aluminum plastic packaging tablets for drugs based on SOM-FDA using XRF spectroscopy(基于SOM-FDA利用XRF对药品铝塑包装片的分类) by 姜红(JIANG Hong), 康瑞雪(KANG Ruixue), 郝小辉(HAO Xiaohui)

“…The established classification model is scientifically accurate and can provide assistance for public security organs in large-scale screening, determining investigation directions, and shortening investigation time.…”
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Optimized Allocation of Flood Control Emergency Materials Based on Loss Quantification by Wei Wang, Yunqing Wang, Li Huang, Yue Song

“…The center of gravity method is used to address demand when constructing the quantitative function of out‐of‐stock loss. The NSGA‐II algorithm was selected to generate the results after the method comparison to ultimately determine the Pareto solution of the model. …”
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Machine Learning and Deep Learning Techniques for Prediction and Diagnosis of Leptospirosis: Systematic Literature Review by Suhila Sawesi, Arya Jadhav, Bushra Rashrash

“…MethodsUsing Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), and Prediction model Risk of Bias Assessment Tool (PROBAST) tools, we conducted a comprehensive review of studies applying ML and DL models for leptospirosis detection and prediction, examining algorithm performance, data sources, and validation approaches. …”
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Optimizing the dynamic treatment regime of outpatient rehabilitation in patients with knee osteoarthritis using reinforcement learning by Sijia Liu, Jiawei Luo, Chengqi He

“…Then, based on the key features screened out, a dynamic treatment recommendation system was constructed by using deep reinforcement learning algorithms, including Deep Deterministic Policy Gradien(DDPG), Deep Q-Network(DQN) and Batch-Constrained Q-learning(BCQ). …”
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Autonomic nervous system development-related signature as a novel predictive biomarker for immunotherapy in pan-cancers by Cunen Wu, Cunen Wu, Cunen Wu, Cunen Wu, Weiwei Xue, Yuwen Zhuang, Dayue Darrel Duan, Dayue Darrel Duan, Zhou Zhou, Zhou Zhou, Xiaoxiao Wang, Zhenfeng Wu, Jin-yong Zhou, Xiangkun Huan, Ruiping Wang, Haibo Cheng, Haibo Cheng

“…A pan-cancer predictive model for ICI prognosis based on ANSDR.Sig was constructed, with the random forest algorithm yielding the most robust performance. …”
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Leveraging diverse cell-death patterns in diagnosis of sepsis by integrating bioinformatics and machine learning by Mi Liu, Xingxing Gao, Hongfa Wang, Yiping Zhang, Xiaojun Li, Renlai Zhu, Yunru Sheng

“…The machine learning algorithm screened three PCD-related genes, NLRC4, TXN and S100A9, as potential biomarkers for sepsis. …”
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Intratumoral and peritumoral CT radiomics in predicting anaplastic lymphoma kinase mutations and survival in patients with lung adenocarcinoma: a multicenter study by Weiyue Chen, Guihan Lin, Ye Feng, Yongjun Chen, Yanjun Li, Jianbin Li, Weibo Mao, Yang Jing, Chunli Kong, Yumin Hu, Minjiang Chen, Shuiwei Xia, Chenying Lu, Jianfei Tu, Jiansong Ji

“…The GPTV3 radiomics model using a support vector machine algorithm achieved the best predictive performance, with the highest average AUC of 0.811 in the validation sets. …”
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Evaluation of machine learning methods for prediction of heart failure mortality and readmission: meta-analysis by Hamed Hajishah, Danial Kazemi, Ehsan Safaee, Mohammad Javad Amini, Maral Peisepar, Mohammad Mahdi Tanhapour, Arian Tavasol

“…In total, 346 machine learning models were evaluated, with the most common algorithms being random forest, logistic regression, and gradient boosting. …”
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Ekyeyo Mobile Application by Nkuba, Blair, Kansiime, Daniel

“…The app incorporates advanced search algorithms, tailored job recommendations, and streamlined candidate screening to improve job-matching accuracy. …”
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Acoustic-based machine learning approaches for depression detection in Chinese university students by Yange Wei, Yange Wei, Shisen Qin, Fengyi Liu, Rongxun Liu, Yunze Zhou, Yuanle Chen, Xingliang Xiong, Wei Zheng, Guangjun Ji, Yong Meng, Fei Wang, Fei Wang, Ruiling Zhang

“…Among five machine learning algorithms, LDA model demonstrated the highest classification performance, with an AUC of 0.771. …”
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Prospective Validation and Usability Evaluation of a Mobile Diagnostic App for Obstructive Sleep Apnea by Pedro Amorim, Daniela Ferreira-Santos, Marta Drummond, Pedro Pereira Rodrigues

“…Current guidelines recommend the development of clinical prediction algorithms in screening prior to PSG. A recent intuitive and user-friendly tool (OSABayes), based on a Bayesian network model using six clinical variables, has been proposed to quantify the probability of OSA. …”
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A hybrid super learner ensemble for phishing detection on mobile devices by Routhu Srinivasa Rao, Cheemaladinne Kondaiah, Alwyn Roshan Pais, Bumshik Lee

“…Furthermore, many of these techniques are unsuitable for mobile devices, which face additional constraints, such as limited RAM, smaller screen sizes, and lower computational power. To address these limitations, this paper proposes a novel hybrid super learner ensemble model named Phish-Jam, a mobile application specifically designed for phishing detection on mobile devices. …”
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Machine Learning and Interpretability Study for Predicting 30-Day Unplanned Readmission Risk of Schizophrenia: A Retrospective Study by Tan Y, Chen G, Wang S, Zhan X, Cheng R, Qiao L, Zhang Z, Liu Y

“…Models were constructed after screening variables using the multiple linear regression and feature importance methods. …”
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Progress and challenges of artificial intelligence in lung cancer clinical translation by Erjia Zhu, Amgad Muneer, Jianjun Zhang, Yang Xia, Xiaomeng Li, Caicun Zhou, John V. Heymach, Jia Wu, Xiuning Le

“…Abstract Artificial intelligence (AI) algorithms, such as convolutional neural networks and transformers, have significantly impacted cancer care. …”
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Identification of retinol dehydrogenase 10 as a shared biomarker for metabolic dysfunction-associated steatotic liver disease and type 2 diabetes mellitus by Fangyu Li, Rui Li, Hongjun Deng

“…The LASSO algorithm and ROC curve identified Retinol Dehydrogenase 10 (RDH10) as the best diagnostic gene for MASLD and T2DM. …”
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LABORATORY OF CLINICAL IMMUNOLOGY N.V. SKLIFOSOVSKY RESEARCH INSTITUTE FOR EMERGENCY MEDICINE (HISTORY AND PRESENT) by M. A. Godkov, G. V. Bulava

“…During 45 years of work of immunological service formed the algorithm of the adequate immunological screening was formed, number of innovative methods of diagnosis was developed, the ideology of post-test counseling of patients by immunologists was created, mathematical methods of storage, modeling and processing of research results was introduced. …”
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Identifying Safeguards Disabled by Epstein-Barr Virus Infections in Genomes From Patients With Breast Cancer: Chromosomal Bioinformatics Analysis by Bernard Friedenson

“…EBV-transformed human mammary cells accelerate breast cancer when transplanted into immunosuppressed mice, but the virus can disappear as malignant cells reproduce. If this model applies to human breast cancers, then they should have genome damage characteristic of EBV infection. …”
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Dielectric tensor prediction for inorganic materials using latent information from preferred potential by Zetian Mao, WenWen Li, Jethro Tan

“…We develop an equivariant readout decoder to predict total, electronic, and ionic dielectric tensors while preserving O(3) equivariance, and benchmark its performance against state-of-the-art algorithms. Virtual screening of thermodynamically stable materials from Materials Project for two discovery tasks, high-dielectric and highly anisotropic materials, identifies promising candidates including Cs2Ti(WO4)3 (band gap E g = 2.93eV, dielectric constant ε = 180.90) and CsZrCuSe3 (anisotropic ratio α r = 121.89). …”
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Sialyltransferase-related genes as predictive factors for therapeutic response and prognosis in cervical cancer by Jia Shao, Can Zhang, Yaonan Tang, Aiqin He, Xiangyan Cheng

“…Cox regression analysis and “glmnet” R package were applied to establish the relevant risk model. “MCPcounter” R package, ESTIMATE algorithm and TIMER online tools were used to depict the tumor immune microenvironment in CC. …”
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