Do We Need to Add the Type of Treatment Planning System, Dose Calculation Grid Size, and CT Density Curve to Predictive Models? by Reza Reiazi, Surendra Prajapati, Leonardo Che Fru, Dongyeon Lee, Mohammad Salehpour

“…<b>Methods:</b> This study evaluated dose calculation differences in the head and neck cancer treatment plans of 19 patients using two treatment planning systems, Pinnacle 9.10 and RayStation 11, with similar dose calculation algorithms. …”
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Integrative bioinformatics and machine learning identify key crosstalk genes and immune interactions in head and neck cancer and Hodgkin lymphoma by Meiling Qin, Xinxin Li, Xun Gong, Yuan Hu, Min Tang

“…The intersection of HNSCC DEGs and HL-related modules was evaluated using protein–protein interaction (PPI) network analysis. …”
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Development and validation of an early diagnosis model for severe mycoplasma pneumonia in children based on interpretable machine learning by Si Xie, Mo Wu, Yu Shang, Wenbin Tuo, Jun Wang, Qinzhen Cai, Chunhui Yuan, Cong Yao, Yun Xiang

“…S100A8/A9 can also be used as a biomarker for validity differentiation of SMPP when medical conditions are limited.…”
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Development and Validation of a Clinical Risk Model for Predicting Malignancy in Patients with Thyroid Nodules by Shiva Borzouei, Ali Safdari, Erfan Ayubi

“…Generally, there was no difference among the six ML algorithms in terms of prognostic performance; however, the prognostic parameters of GLM and LDA algorithms were higher than the others. …”
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Lipid-Metabolism-Related Gene Signature Predicts Prognosis and Immune Microenvironment Alterations in Endometrial Cancer by Zhangxin Wu, Yufei Nie, Deshui Kong, Lixiang Xue, Tianhui He, Kuaile Zhang, Jie Zhang, Chunliang Shang, Hongyan Guo

“…Tumor immune infiltration patterns were evaluated using single-sample Gene Set Enrichment Analysis (ssGSEA), Estimation of Stromal and Immune Cells in Malignant Tumors using Expression Data (ESTIMATE), and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms. …”
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A novel ensemble of hybrid starling murmuration optimized stacked gated Ghostnets for robust classification of DDoS attacks by P. Mamatha, S. Balaji, S. Sai Anuraghav

“…Due to hostile user techniques and the complexity of DDoS, advanced defensive measures are mandatorily required. As the algorithms of Artificial Intelligence (AI) serves as a key factor in developing the the intelligent intrusion detection systems which accurately differentiates the known and unknown attacks. …”
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Optimizing skin cancer screening with convolutional neural networks in smart healthcare systems. by Ali Raza, Akhtar Ali, Sami Ullah, Yasir Nadeem Anjum, Basit Rehman

“…A research used an optimized CNN and ResNet152V2 with the HAM10000 dataset to differentiate between the seven forms of skin cancer. …”
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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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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 &lt; 20) by qRT-PCR, and in a collagenase-induced ICH mouse model. …”
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Combined Use of Multiple Cloud‐Free Snow Cover Products in China and Its High‐Mountain Region: Implications From Snow Cover Identification to Snow Phenology Detection by Longhui Zhang, Hongbo Zhang, Xueyan Sun, Lun Luo

“…However, comprehensive analysis of differences among products and their impact on snow phenology detection is lacking. …”
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Predicting the generalization of computer aided detection (CADe) models for colonoscopy by Joel Shor, Carson McNeil, Yotam Intrator, Joseph R. Ledsam, Hiro-o Yamano, Daisuke Tsurumaru, Hiroki Kayama, Atsushi Hamabe, Koji Ando, Mitsuhiko Ota, Haruei Ogino, Hiroshi Nakase, Kaho Kobayashi, Masaaki Miyo, Eiji Oki, Ichiro Takemasa, Ehud Rivlin, Roman Goldenberg

“…Abstract Generalizability of AI colonoscopy algorithms is important for wider adoption in clinical practice. …”
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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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Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques by Liuqing Yang, Liuqing Yang, Liuqing Yang, Rui Xuan, Rui Xuan, Rui Xuan, Dawei Xu, Dawei Xu, Dawei Xu, Aming Sang, Aming Sang, Aming Sang, Jing Zhang, Jing Zhang, Jing Zhang, Yanfang Zhang, Xujun Ye, Xinyi Li, Xinyi Li, Xinyi Li

“…We then identified differentially expressed genes (DEGs), which were subjected to KEGG and GO enrichment analyses. …”
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Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in precision me... by Yutong Fang, Rongji Zheng, Yefeng Xiao, Qunchen Zhang, Junpeng Liu, Jundong Wu

“…We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”
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Subtypes detection of papillary thyroid cancer from methylation assay via Deep Neural Network by Andrea Colacino, Andrea Soricelli, Michele Ceccarelli, Ornella Affinito, Monica Franzese

“…When applied to the thyroid independent dataset, the proposed Neural Net architecture successfully discriminates tumor versus normal samples (AUC = 0.91 +/- 0.05) and follicular versus classical PTC subtypes (AUC = 0.80 +/- 0.05), outperforming traditional machine learning algorithms. Conclusions: In conclusion, the study highlights the effectiveness of CNNs in the methylation based classification of thyroid tumors and their subtypes, demonstrating its ability to capture subtle epigenetic differences with minimal preprocessing.This versatility makes the model adaptable for classifying other tumor types. …”
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The incremental value of tuberculosis detecting African giant pouched rats over Smear microscopy and Xpert MTB/RIF for Tanzanian TB detection. by Tefera B Agizew, Joseph Soka, Stephen Mwimanzi, Cynthia D Fast, Gilbert Mwesiga, Nashon Edward, Marygiven Stephen, Rehema Kondo, Christophe Cox, Negussie Beyene

“…Sputum-smear microscopy (smear) is being replaced by Xpert MTB/RIF (Xpert)-based diagnostic algorithms in many countries. We evaluated the incremental values of rat-based case detection over smear and Xpert.…”
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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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