Analysis and Validation of Autophagy-Related Gene Biomarkers and Immune Cell Infiltration Characteristic in Bronchopulmonary Dysplasia by Integrating Bioinformatics and Machine Lea... by Xiao S, Ding Y, Du C, Lv Y, Yang S, Zheng Q, Wang Z, Zheng Q, Huang M, Xiao Q, Ren Z, Bi G, Yang J

“…Subsequently, the hub genes were identified by Lasso and Cytoscape with three machine-learning algorithms (MCC, Degree and MCODE). In addition, hub genes were validated with ROC, single-cell sequence and IHC in hyperoxia mice. …”
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Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patients by Ran Du, Lijun Jing, Denggang Fu

“…The signature showed strong predictive power with AUC values of 0.68, 0.73, and 0.76 for 2-, 3-, and 5-year survival, respectively. …”
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SECONDGRAM: Self-conditioned diffusion with gradient manipulation for longitudinal MRI imputation by Brandon Theodorou, Anant Dadu, Mike Nalls, Faraz Faghri, Jimeng Sun

“…These models are computer algorithms that simulate how information changes. SECONDGRAM addresses data scarcity by generating realistic follow-up MRI imaging features, thereby enriching limited datasets. …”
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Estimating Winter Canola Aboveground Biomass from Hyperspectral Images Using Narrowband Spectra-Texture Features and Machine Learning by Xia Liu, Ruiqi Du, Youzhen Xiang, Junying Chen, Fucang Zhang, Hongzhao Shi, Zijun Tang, Xin Wang

“…Subsequently, machine learning algorithms were applied to develop estimation models for winter canola biomass. …”
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Factors influencing the response to periodontal therapy in patients with diabetes: post hoc analysis of a randomized clinical trial using machine learning by Nidia CASTRO DOS SANTOS, Arthur MANGUSSI, Tiago RIBEIRO, Rafael Nascimento de Brito SILVA, Mauro Pedrine SANTAMARIA, Magda FERES, Thomas VAN DYKE, Ana Carolina LORENA

“…We tested seven different algorithms: K-Nearest Neighbors, Decision Tree, Support Vector Machine, Random Forest, Extreme Gradient Boosting, and Logistic Regression. …”
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Quality monitoring of hybrid welding processes: A comprehensive review by Solomon Habtamu Tessema, Dariusz Bismor

“…Furthermore, the review highlights the integration of sensor data with AI/ML algorithms and digital twin technologies, enhancing the capabilities of quality monitoring systems. …”
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microRNA-497-5p-based screening identifies a novel synthetic lethal-type interaction via PKMYT1 and WEE1 in pleural mesothelioma by Nathanael Pruett, Sierra Wilferd, Anand Singh, Agnes Y. Choi, Shivani Dixit, Vivek Singh, Charlize Nguyen, Olivia Lin, David S. Schrump, Christopher L. Plaisier, Chuong D. Hoang

“…Surprisingly, multiple identified targets were not predicted by in silico algorithms. Using patient samples, cell lines, murine xenograft models, and our localized nanoparticle miRNA delivery platform, we validated miR-497-5p anti-tumor mechanisms, which consisted of pro-apoptotic and anti-cell-cycle effects. …”
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Development of a circadian-related prognostic signature highlights RBM17 as a stemness regulator in liver cancer by Jingsong Yan, Xiao Yang, Jiabin Lu, Shasha Wu, Yanchen Wang, Yuyang Du, Jingyi Zheng, Fenfen Wang, Han Gao, Hui Yang, Shaoyan Xi, Yan Li

“…Employing machine learning algorithms, we pinpointed four pivotal dysregulated LCGs. …”
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Visualising fibre path and generating G-code for melt electrowriting of tubular scaffolds using Grasshopper software by Kelly L. O’Neill, Taite McLoughlin, Georgia Van der Linden, Ignacio Lopez Buson, Naomi C. Paxton, Mary Polites, Paul D. Dalton

“…This visual prototyping platform through Rhinoceros and Grasshopper offers a new method in predicting fibre paths and incorporating scaffold design parameters, meeting the need for diverse tubular scaffolds in different fields. …”
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Modeling Asteroseismic Yields for the Roman Galactic Bulge Time-domain Survey by Trevor J. Weiss, Noah J. Downing, Marc H. Pinsonneault, Joel C. Zinn, Dennis Stello, Timothy R. Bedding, Kaili Cao, Marc Hon, Claudia Reyes, B. Scott Gaudi, Robert F. Wilson, Daniel Huber, Sanjib Sharma

“…The expected yield and stellar parameter precision we predict for Roman asteroseismology promise to characterize planet-hosting stellar populations and to resolve questions regarding the formation history of the bulge.…”
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Enhanced Detection of Intrusion Detection System in Cloud Networks Using Time-Aware and Deep Learning Techniques by Nima Terawi, Huthaifa I. Ashqar, Omar Darwish, Anas Alsobeh, Plamen Zahariev, Yahya Tashtoush

“…We generate real DoS traffic, including normal, Internet Control Message Protocol (ICMP), Smurf attack, and Transmission Control Protocol (TCP) classes, and develop nine predictive algorithms, combining traditional machine learning and advanced deep learning techniques with optimization methods, including the synthetic minority sampling technique (SMOTE) and grid search (GS). …”
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Transdimensional Inference for Gravitational-wave Astronomy with Bilby by Hui Tong, Nir Guttman, Teagan A. Clarke, Paul D. Lasky, Eric Thrane, Ethan Payne, Rowina Nathan, Ben Farr, Maya Fishbach, Gregory Ashton, Valentina Di Marco

“…The tBilby package allows users to set up transdimensional inference calculations using the existing Bilby architecture with off-the-shelf nested samplers and/or Markov Chain Monte Carlo algorithms. Transdimensional models are particularly helpful when seeking to test theoretically uncertain predictions described by phenomenological models. …”
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A Self-Supervised Feature Point Detection Method for ISAR Images of Space Targets by Shengteng Jiang, Xiaoyuan Ren, Canyu Wang, Libing Jiang, Zhuang Wang

“…The experiments demonstrate that SFPD has better performance in feature point detection and feature point matching than usual algorithms.…”
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Application of a Hybrid Model for Data Analysis in Hydroponic Systems by Kuanysh Bakirov, Jamalbek Tussupov, Akhmet Tussupov, Ibraheem Shayea, Aruzhan Shoman

“…Experimental evaluation demonstrated superior predictive performance by using XGBoost, achieving an accuracy and F1-score of 97.88%, ROC-AUC of 99.99%, and computational efficiency (training completed in 2.3 s), outperforming RandomForest and GradientBoosting algorithms. …”
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A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision by Rongyong Zhao, Feng Hua, Bingyu Wei, Cuiling Li, Yulong Ma, Eric S. W. Wong, Fengnian Liu

“…Secondly, typical abnormal behavior datasets at home and abroad are sorted out, and the characteristics of these datasets are compared in detail from multiple perspectives such as scale, characteristics, and uses, and the performance indicators of different algorithms on the datasets are compared and analyzed. …”
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An FPGA-accelerated multi-level AI-integrated simulation framework for multi-time domain power systems with high penetration of power converters by Chen Liu, Peng Su, Hao Bai, Xizheng Guo, Alber Filbà Martínez, Jose Luis Dominguez Garcia

“…The framework structures systems hierarchically using energy transmission functions and unified energy information flow-based surrogate models with defined ports, ensuring compatibility with artificial intelligence algorithms. By integrating AI techniques, such as back propagation neural networks, the framework predicts variables with high computational complexity, improving accuracy and simulation efficiency. …”
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Adaptive FPGA-Based Accelerators for Human–Robot Interaction in Indoor Environments by Mangali Sravanthi, Sravan Kumar Gunturi, Mangali Chinna Chinnaiah, Siew-Kei Lam, G. Divya Vani, Mudasar Basha, Narambhatla Janardhan, Dodde Hari Krishna, Sanjay Dubey

“…The experimental validation was conducted in a hospital environment. The proposed algorithms were coded in Verilog HDL, simulated, and synthesized using VIVADO 2017.3. …”
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Distributed hybrid consensus–based square-root cubature quadrature information filter and its application to maneuvering target tracking by Jun Liu, Yu Liu, Kai Dong, Ziran Ding, You He, Qichao Li

“…The simulation results show that the proposed hybrid consensus–based square-root cubature quadrature information filter outperforms the existing algorithms at the expense of a slight increase in computational cost.…”
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Use of artificial intelligence in the diagnosis, treatment and surveillance of patients with kidney cancer by E. Yu. Timofeeva, С. R. Azilgareeva, A. O. Morozov, M. S. Taratkin, D. V. Enikeev

“…AI finds its application in histopathological evaluation: the AI model reaches 100.0% sensitivity and 97.1% specificity in the differential diagnosis of normal tissue from RCC. AI model algorithms may be used to identify patients at high risk of relapse requiring long-term follow-up, as well as to develop individual treatment and follow-up strategies. …”
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The relationship between epigenetic biomarkers and the risk of diabetes and cancer: a machine learning modeling approach by Shiqi Zhang, Shiqi Zhang, Jianan Jin, Benfeng Xu, Qi Zheng, Haibo Mou

“…Nine machine learning algorithms were used to build models: AdaBoost, GBM, KNN, lightGBM, MLP, RF, SVM, XGBoost, and logistics. …”
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