Intervention of machine learning in bladder cancer research using multi-omics datasets: systematic review on biomarker identification by Blessy Kiruba, P. S. Athul Narayan, Badhari Raj, S. Rohieth Raj, Sam George Mathew, Sudhakaran Sajitha Lulu, Vino Sundararajan

“…Integrated models combining computational techniques with large multi-omics datasets have gained significant attention, enabling the identification of significant BC biomarkers that include genes coding for diverse cellular functions, differentially expressed genes, proteins, and metabolites. …”
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Uncovering glycolysis-driven molecular subtypes in diabetic nephropathy: a WGCNA and machine learning approach for diagnostic precision by Chenglong Fan, Guanglin Yang, Cheng Li, Jiwen Cheng, Shaohua Chen, Hua Mi

“…Results Eleven GRGs showed abnormal expression in DN samples, leading to the identification of two distinct glycolysis clusters, each with its own immune profile and functional pathways. The analysis of the GSE142153 dataset showed that these clusters had specific immune characteristics. …”
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Fine-Tuned Machine Learning Classifiers for Diagnosing Parkinson’s Disease Using Vocal Characteristics: A Comparative Analysis by Mehmet Meral, Ferdi Ozbilgin, Fatih Durmus

“…<b>Background/Objectives</b>: This paper is significant in highlighting the importance of early and precise diagnosis of Parkinson’s Disease (PD) that affects both motor and non-motor functions to achieve better disease control and patient outcomes. …”
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Design and optimization of human-machine interaction interface for the intelligent Internet of Things based on deep learning and spatial computing by Wencong Wang, Ke Wang, Hui Du

“…NeuroSpatialIOT functions by tracking eye movements, comprehending the room’s 2D layout, and employing deep learning to predict user intentions. …”
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Identification of biomarkers between coronary artery disease and non-alcoholic steatohepatitis: a combination of bioinformatics and machine learning by Yihong Lin, Jingmei Song, Xiaohong Li

“…Three machine learning methods—LASSO, random forest, and SVM-RFE—were used to identify signature genes. …”
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Identification of Biomarkers Associated with Heart Failure Caused by Idiopathic Dilated Cardiomyopathy Using WGCNA and Machine Learning Algorithms by Mengyi Sun, Linping Li

“…We then identified the differentially expressed genes (DEGs) and analyzed their functions and related pathways by using “Metascape”. …”
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Efficient merging and validation of deep learning-based nuclei segmentations in H&E slides from multiple models by Jagadheshwar Balan, Shannon K. McDonnell, Zachary Fogarty, Nicholas B. Larson

“…Characterizing cellular composition in tissue samples offers fundamental insights into functional and biological processes. Understanding the abundance or lack of specific cell types, such as inflammatory cells in the context of microenvironments such as tumor can help guide disease progression and personalized medicine. …”
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Investigating the Prognostic Role of Telomerase-Related Cellular Senescence Gene Signatures in Breast Cancer Using Machine Learning by Qiong Li, Hongde Liu

“…The model demonstrated strong predictive accuracy and was successfully validated in multiple independent cohorts. Functional enrichment analyses indicated significant associations with immune responses and aging-related pathways. …”
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A privacy-preserved horizontal federated learning for malignant glioma tumour detection using distributed data-silos. by Shagun Sharma, Kalpna Guleria, Ayush Dogra, Deepali Gupta, Sapna Juneja, Swati Kumari, Ali Nauman

“…The most essential part of the nervous system is glial cells, which support the brain's functioning prominently. However, with the evolution of glioma, tumours form that invade healthy tissues in the brain, leading to neurological impairment, seizures, hormonal dysregulation, and venous thromboembolism. …”
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The role of senescence-related genes in major depressive disorder: insights from machine learning and single cell analysis by Kun Lian, Wei Yang, Jing Ye, Yilan Chen, Lei Zhang, Xiufeng Xu

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Possibilities of using immersive learning based on abstract highly formalized mathematical models for training future mathematics teachers by Valentina A. Matveeva, Olga Yu. Zaslavskaya

“…The purpose of this work is to describe the technology of ‘weak immersive learning’. Methodology . The study of the problem is considered using the example of the mathematics section “Number Theory”. …”
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Machine Learning-Based Differential Diagnosis of Parkinson&#x2019;s Disease Using Kinematic Feature Extraction and Selection by Masahiro Matsumoto, Abu Saleh Musa Miah, Nobuyoshi Asai, Jungpil Shin

“…PD presents both motor and non-motor symptoms that progressively impair daily functioning. The severity of these symptoms is typically assessed using the MDS-UPDRS rating scale, which is subjective and dependent on the physician&#x2019;s experience. …”
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Effects of a virtual simulative diabetes care program on learning ability and clinical thinking skills in nursing interns by Tianhui Xu, Qin Wang, Fang Liu, Li Yang, Rong Wang, Huiting Weng

“…The virtual simulation program was rated as effective, with an overall score of 8.37 ± 1.20 and "Excellent" ratings for learning functionality. Conclusions The virtual simulation-based diabetes care program significantly improved nursing interns’ learning and clinical skills.…”
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Integrating bioinformatics and machine learning to elucidate the role of protein glycosylation-related genes in the pathogenesis of diabetic kidney disease. by Ziyang Liu, Zengyuan Qin, Wenxin Bai, Shasha Wang, Chunling Huang, Na Li, Lei Yan, Yue Gu, Fengmin Shao

“…Differentially expressed glycosylation-related genes were identified, followed by unsupervised clustering to define molecular subtypes. Functional enrichment, immune cell infiltration analysis, and machine learning algorithms (including feature selection for hub genes) were employed. qPCR validation was performed on clinical DKD and normal kidney tissues, and ROC curves were generated to assess diagnostic potential.…”
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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys by Fei Shuang, Zixiong Wei, Kai Liu, Wei Gao, Poulumi Dey

“…Recent advances in machine learning, combined with the generation of extensive density functional theory (DFT) datasets, have enabled the development of universal machine learning interatomic potentials (uMLIPs). …”
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Simultaneous segmentation and classification of 99mTc-DMSA renal scintigraphic images with a deep learning approach by Jiayi Wang, Mingyan Wu, Xiemei Ruan, Jiaying Zhang, Zhengguo Chen, Yihui Zhai, Hong Xu, Ha Wu, Jeff L. Zhang

“…Abstract Background 99mTc-DMSA scan plays an important role in assessing functional abnormalities in kidneys. As a promising network for deep learning (DL), Mask R-CNN has the capability of simultaneously segmenting and classifying objects in images. …”
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Identification of palmitoylated biomarkers in non-alcoholic fatty liver disease via integrated bioinformatics analysis and machine learning by Zheng Liu, Xiaohong Wang, Mingzhu Xiu, Rui Luo, Xiaomin Shi, Yizhou Wang, Yusong Ye, Ruiyu Wang, Sha Liu, Muhan Lv, Xiaowei Tang

“…This study integrated bioinformatics analysis and machine learning to identify palmitoylation-related biomarkers for NAFLD. …”
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Identification of optimal biomarkers associated with distant metastasis in breast cancer using Boruta and Lasso machine learning algorithms by Jia-ning Qin, Wen-bin Dai, Wen-hai Zhang, Bin-jie Chen, Ling Liang, Chun-feng Liang, Chun-guo Lu, Qi-xing Tan, Chang-yuan Wei, Yang Tan, Fang Wu

“…The selected key indicators were evaluated for their association with distant metastasis risk using multivariate logistic regression analysis and restricted cubic spline functions. Discriminative performance was assessed through ROC curve analysis. …”
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Identifying Lactylation-related biomarkers and therapeutic drugs in ulcerative colitis: insights from machine learning and molecular docking by Yao Yang, Xu Sun, Bin Liu, Yunshu Zhang, Tong Xie, Junchen Li, Jifeng Liu, Qingkai Zhang

“…Through machine learning algorithms, the diagnostic model was established. …”
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A novel smart baby cradle system utilizing IoT sensors and machine learning for optimized parental care by Kunal Chandnani, Suryakant Tripathy, Ashutosh Krishna Parbhakar, Kshitij Takiar, Urvi Singhal, P. Sasikumar, S. Maheswari

“…This system integrates Internet of Things (IoT) technology, machine learning, and smart automation to offer a safer, more responsive, and comfortable environment for babies. …”
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