Transcriptome Derived Artificial neural networks predict PRRC2A as a potent biomarker for epilepsy by Wayez Naqvi, Prekshi Garg, Prachi Srivastava

“…The model’s performance was evaluated using the “pROC” R package, and an AUC of 0.720 was obtained, indicating that the model had excellent classification accuracy. …”
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A Comprehensive Review on Sensor-Based Electronic Nose for Food Quality and Safety by Teodora Sanislav, George D. Mois, Sherali Zeadally, Silviu Folea, Tudor C. Radoni, Ebtesam A. Al-Suhaimi

“…Our review found that most of the efforts use portable, low-cost electronic noses, coupled with pattern recognition algorithms, for evaluating the quality levels in certain well-defined food classes, reaching accuracies exceeding 90% in most cases. …”
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Clinical application and immune infiltration landscape of stemness‐related genes in heart failure by Wenting Yan, Yanling Li, Gang Wang, Yuan Huang, Ping Xie

“…Stemness refers to the self‐renewal and differentiation ability of cells. However, little is known about the heart's stemness properties. …”
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Construction of a novel CD8T cell-related index for predicting clinical outcomes and immune landscape in ovarian cancer by combined single-cell and RNA-sequencing analysis by Yu Zhang, Peng Wan, Liangliang Wang, Ruiping Ren

“…Furthermore, immune landscape analysis was performed using CIBERSORT, ssGSEA, TIMER, and ESTIMATE algorithms. Tumor mutation burden (TMB) analysis and drug sensitivity analysis were used to evaluate the potential predictive value of CCAI. …”
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Identification of novel gut microbiota-related biomarkers in cerebral hemorrhagic stroke by Fengli Ye, Huili Li, Hongying Li, Xiue Mu

“…Hub genes were screened using LASSO, RandomForest, and SVM-RFE algorithms. Validation was conducted in plasma samples from ICH patients (n=20) and controls (n < 20) by qRT-PCR, and in a collagenase-induced ICH mouse model. …”
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Multi-Camera Machine Learning for Salt Marsh Species Classification and Mapping by Marco Moreno, Sagar Dalai, Grace Cott, Ben Bartlett, Matheus Santos, Tom Dorian, James Riordan, Chris McGonigle, Fabio Sacchetti, Gerard Dooly

“…This demonstrates that data fusion and band reduction techniques improve species differentiation, particularly for vegetation with overlapping spectral signatures. …”
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Approach to Neonates and Young Infants with Fever without a Source Who Are at Risk for Severe Bacterial Infection by Susanna Esposito, Victoria Elisa Rinaldi, Alberto Argentiero, Edoardo Farinelli, Marta Cofini, Renato D’Alonzo, Antonella Mencacci, Nicola Principi

“…Among neonates and infants <3 months of age with fever without a source (FWS), 5% to 15% of cases are patients with fever caused by a serious bacterial infection (SBI). To favour the differentiation between low- and high-risk infants, several algorithms based on analytical and clinical parameters have been developed. …”
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Advancing coal fire detection model for large-scale areas based on RS indices and machine learning by Jinglong Liu, Feng Zhao, Yunjia Wang, Yanan Wang, Sen Du, Libo Dang, Jordi J. Mallorqui

“…CFDM outperformed other ML algorithms, achieving Recall, Precision, F1-score, and Kappa coefficient values of 0.89, 0.94, 0.93, and 0.92, respectively. …”
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A Monocyte-Driven Prognostic Model for Multiple Myeloma: Multi-Omics and Machine Learning Insights by Xie L, Gao M, Tan S, Zhou Y, Liu J, Wang L, Li X

“…Through multi-omics analyses and machine learning algorithms, we established a robust monocyte-related prognostic signature. …”
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Exploration of shared pathogenic factors and causative genes in early-stage endometrial cancer and osteoarthritis by Yiyun Bai, Sang Luo, Ruzhen Shuai, Xiaomei Zhang, Liwei Yuan, Dan Liu

“…Genes with diagnostic value were identified using multiple machine learning algorithms to construct EC prediction models and evaluate their performance. …”
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Identification of biomarkers related to Escherichia coli infection for the diagnosis of gastrointestinal tumors applying machine learning methods by Tingting Ge, Wei Wang, Dandan Zhang, Xubo Le, Lumei Shi

“…Weighted correlation network analysis (WGCNA) and differentially expressed genes (DEGs) analysis were used to identify genes related to E. coli infection in gastrointestinal tumors. …”
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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

“…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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Integrative machine learning and bioinformatics analysis to identify cellular senescence-related genes and potential therapeutic targets in ulcerative colitis and colorectal cancer by Tianle Xue, Yunpeng Chen, Xiaomeng Li, Zhixiang Zhou, Qiyang Chen

“…An integrated machine learning (IML) model—utilizing 12 algorithms with 10-fold cross-validation—was constructed to pinpoint key diagnostic biomarkers. …”
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Analysis of the molecular subtypes and prognostic models of anoikis-related genes in colorectal cancer by Lei Shen, Kang Hou, Jifeng Zhang, Xiaodong Li

“…Additionally, various computational algorithms were employed to evaluate the immunotherapeutic responses of different risk groups. …”
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Identification of gene signatures and potential pharmaceutical candidates linked to COVID-19-related depression based on gene expression profiles by Shaojun Chen, Yiyuan Luo, Lihua Zhang

“…The main objective of this study was to evaluate gene-expression profiles and, identify key genes, and discovery potential therapeutic agents for co-occurrence in COVID-19 and major depressive disorder (MDD).MethodsInitially, we identified differentially expressed genes (DEGs) in datasets from COVID-19 (GSE188847) or MDD (GSE101521). …”
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Comparison of Raman spectroscopy with mass spectrometry for sequence typing of Acinetobacter baumannii strains: a single-center study by Suling Liu, Ni Zhang, Jiawei Tang, Chong Chen, Weisha Wang, Jingfang Zhou, Long Ye, Xiaoli Chen, ZhengKang Li, Liang Wang

“…This study developed a novel approach, combining surface-enhanced Raman spectroscopy (SERS) with machine-learning (ML) algorithms, to construct predictive models for A. baumannii sequence typing based on SERS spectra. …”
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Critical role of Oas1g and STAT1 pathways in neuroinflammation: insights for Alzheimer’s disease therapeutics by Zhixin Xie, Linxi Li, Weizhong Hou, Zhongxi Fan, Lifan Zeng, Limin He, Yunxiang Ji, Jingbai Zhang, Fangran Wang, Zhou Xing, Yezhong Wang, Yongyi Ye

“…Machine learning algorithms were applied to DEGs, and genes selected by both methods were identified as hub genes, with ROC curves used to evaluate their diagnostic accuracy. …”
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Abnormal eye movement, brain regional homogeneity in schizophrenia and clinical high-risk individuals and their associated gene expression profiles by Zhaobin Chen, Yangpan Ou, Yudan Ding, Ying Wang, Huabing Li, Feng Liu, Ping Li, Dongsheng Lv, Yong Liu, Bing Lang, Jingping Zhao, Wenbin Guo

“…Machine-learning algorithms were used to differentiate FSZ from CHR. …”
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Automated classification of stress and relaxation responses in major depressive disorder, panic disorder, and healthy participants via heart rate variability by Sangwon Byun, Ah Young Kim, Min-Sup Shin, Hong Jin Jeon, Hong Jin Jeon, Chul-Hyun Cho, Chul-Hyun Cho

“…This study evaluated the feasibility of using machine-learning algorithms to detect stress automatically in MDD and PD patients, as well as healthy controls (HCs), based on HRV features.MethodsThe study included 147 participants (MDD: 41, PD: 47, HC: 59) who visited the laboratory up to five times over 12 weeks. …”
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Incidence of Rapid Rate Non‐Sustained and Sustained Ventricular Tachycardia in Implantable Cardioverter‐Defibrillator Recipients and Its Correlation With Heart Failure Guideline‐Di... by Muhammad Taha Khan, Ghazala Irfan, Sarim Ansari, Zubair Mumtaz, Faisal Qadir, Azam Shafquat

“…Rapid rate non‐sustained ventricular tachycardia (RR‐NSVT) and sustained ventricular tachycardia (VT) can be detected on implantable cardioverter‐defibrillator (ICD) interrogation due to discrimination algorithms that differentiate supra‐ventricular from ventricular tachycardia. …”
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