Unravelling NK cell subset dynamics and specific gene signatures post-ibrutinib therapy in chronic lymphocytic leukaemia via single-cell transcriptomics by Chunlan Liu, Tianjian Ding, Rong Zou, Aili Zhang, Zhengzhuo Zhi, Sili Wang

“…A cell subset-specific novel index (CNI) was constructed based on these core genes and was shown to be capable of predicting responses to immunotherapy. Oncopredictive algorithms and molecular docking screenings further identified semaxanib and ulixertinib as potential therapeutic candidates for CLL. …”
Get full text

Integrative multi-omics analysis reveals the role of toll-like receptor signaling in pancreatic cancer by Jie Peng, Jiaao Sun, Youfeng Yu, Qihang Yuan, Yong Zhang

“…The model showed strong performance in predicting the survival state of pancreatic cancer samples. …”
Get full text

Effective Machine Learning Techniques for Dealing with Poor Credit Data by Dumisani Selby Nkambule, Bhekisipho Twala, Jan Harm Christiaan Pretorius

“…The differences in performance of all seven machine learning algorithms are significant at the 95% level.…”
Get full text

Мethods of Machine Learning in Ophthalmology: Review by D. D. Garri, S. V. Saakyan, I. P. Khoroshilova-Maslova, A. Yu. Tsygankov, O. I. Nikitin, G. Yu. Tarasov

“…Machine learning includes models and algorithms that mimic the architecture of biological neural networks. …”
Get full text

Identification and experimental validation of BMX as a crucial PANoptosis‑related gene for immune response in Spinal Cord Injury. by Tianbao Feng, Jiating Hu, Mi Xie, Guodong Shi, Qi Wang, Jingyuan Yao, Xiaoqin Liu

“…Additionally, a protein-protein interaction (PPI) network was established to identify hub genes, and 8 machine learning algorithms were used to narrowed down hub genes. BMX and CASP5 were consistently identified across all algorithms. …”
Get full text

Bio inspired optimization techniques for disease detection in deep learning systems by A. Ashwini, Vanajaroselin Chirchi, S. Balasubramaniam, Mohd Asif Shah

“…This work assists researchers in selecting the most effective bio-inspired algorithm for disease categorization, prediction, and the analysis of high-dimensional biomedical data.…”
Get full text

Machine learning in dentistry and oral surgery: charting the course with bibliometric insights by Shuangwei Liu, Yuquan Hao, Shijie Zhu, Liyao Wan, Zhe Yi, Zhichang Zhang

“…Keyword bursts indicate the evolution of research focus from traditional machine learning algorithms to deep learning algorithms and the emerging importance of multimodal data and foundation models. …”
Get full text

Prognostic model identification of ribosome biogenesis-related genes in pancreatic cancer based on multiple machine learning analyses by Yuan Sun, Yan Li, Anlan Zhang, Tao Hu, Ming Li

“…Prognostic gene sets were screened using machine learning algorithms to construct a risk model, which was externally validated via GEO database. …”
Get full text

Few-Shot Learning With Prototypical Networks for Improved Memory Forensics by Muhammad Fahad Malik, Ammara Gul, Ayesha Saadia, Faeiz M. Alserhani

“…Prototypical networks are a type of few-shot learning algorithm that excels at classifying new categories with minimal examples. …”
Get full text

Inversion of SPAD Values of Pear Leaves at Different Growth Stages Based on Machine Learning and Sentinel-2 Remote Sensing Data by Ning Yan, Qu Xie, Yasen Qin, Qi Wang, Sumin Lv, Xuedong Zhang, Xu Li

“…Subsequently, four machine learning algorithms—K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and an Optimized Integrated Algorithm (OIA)—were employed to develop SPAD retrieval models, and the performance differences across various input combinations and models were systematically evaluated. …”
Get full text

Synergistic feature selection and distributed classification framework for high-dimensional medical data analysis by D. Dhinakaran, L. Srinivasan, S. Edwin Raja, K. Valarmathi, M. Gomathy Nayagam

“…In order to overcome these drawbacks, the new integrated algorithm is presented here: Synergistic Kruskal-RFE Selector and Distributed Multi-Kernel Classification Framework (SKR-DMKCF). …”
Get full text

Peak-Valley difference based pricing strategy and optimization for PV-storage electric vehicle charging stations through aggregators by Qin Yan, Jinxin Wang, Tao Lin, Archie James Johnston

Get full text

A Capacitated Vehicle Routing Model for Distribution and Repair with a Service Center by Irma-Delia Rojas-Cuevas, Elias Olivares-Benitez, Alfredo S. Ramos, Samuel Nucamendi-Guillén

“…<i>Results:</i> The model was applied to a real-world case study, achieving a 40% reduction in fuel costs, a reduction from 5 to 3 routes, and a sustainable logistics operations model with potential reductions of greenhouse gas emissions and item disposals. …”
Get full text

Channel estimation method of massive MIMO-OFDM system based on adaptive compressed sensing by Yiyang HU, Lina QI

“…Massive multiple-input multiple-output (MIMO) is a solution for efficiently providing connection services for a variety of machine equipment in the Internet of things (IoT), and efficient connection services require accurate channel estimation.Aimed at the problems of high pilot overhead and poor performance of normalized mean square error (NMSE) estimation in downlink channel estimation of massive MIMO systems, based on the compressed sensing (CS) theory, the common sparsity of the channel space domain was combined while using the feature of lower sparsity of adjacent time slot differential channel impulse response (CIR), which leaded to a significant reduction in pilot overhead.In the reconstruction algorithm, a two-stage differential estimation algorithm, which divided the channel estimation in consecutive time slots with time correlation into two stages, was proposed and the idea of adaptive compressed sensing was combined to achieve fast and accurate CIR estimate.The simulation results show that the proposed two-stage differential channel estimation algorithm not only has a significant improvement in the estimated NMSE performance and data transmission rate compared to the existing CS-based multiple measurement vector (MMV) algorithm, but also show a certain reduction in runtime complexity.…”
Get full text

Denoising of acoustic emission signals from rock failure processes through ICEEMDAN combined with multiple criteria and wavelet transform by Tao Wang, Weiwei Ye, Liyuan Liu, Zhihui Zhao, Wei Huang

“…Quantitative analysis of denoising effects on typical rock uniaxial compression fracture AE signals reveals that the optimized method has a positive impact on high-frequency noise reduction. The peak frequency range is unaffected before and after optimization, while the main amplitude reduction is concentrated in the high-frequency range. …”
Get full text

Robust Tube-Based MPC with Piecewise Affine Control Laws by Meng Zhao, Xiaoming Tang

“…This paper presents a tube-based model predictive control (MPC) algorithm with piecewise affine control laws for discrete-time linear systems in the presence of bounded disturbances. …”
Get full text

An integrated machine learning framework for developing and validating a prognostic risk model of gastric cancer based on endoplasmic reticulum stress-associated genes by Gang Wei, Yan Wang, Ru Liu, Lei Liu

“…This risk model proved to have a good predictive performance for estimating the overall survival of these patients. …”
Get full text

Identification of PET/CT radiomic signature for classification of locally recurrent rectal cancer: A network-based feature selection approach by Sara Dalmonte, Maria Adriana Cocozza, Dajana Cuicchi, Daniel Remondini, Lorenzo Faggioni, Paolo Castellucci, Andrea Farolfi, Emilia Fortunati, Alberta Cappelli, Riccardo Biondi, Arrigo Cattabriga, Gilberto Poggioli, Stefano Fanti, Gastone Castellani, Francesca Coppola, Nico Curti

“…Feature selection was performed using a novel approach derived from gene expression analysis, based on the DNetPRO algorithm. The prediction was done using a Support Vector Classifier (SVC) with a 10-fold cross-validation. …”
Get full text

Application of Artificial Intelligence Large Language Model in Power Equipment Operation and Maintenance by Xiaohong Chen, Wenrun Fu, Chaoming Liu, Zehong Liu, Junpeng Li, Zhiliang Hu, Dongbin Hu

“…This study aims to explore the enabling role of multimodal AI-LLM in health assessment, operational state prediction, fault diagnosis, life prediction, and maintenance strategy recommendation, among other specific scenarios of power equipment operation and maintenance. …”
Get full text

Bayesian Adaptive Lasso for Regression Models with Nonignorable Missing Responses by Yuanying Zhao, Xingde Duan

“…We extend the partial posterior predictive p value for goodness-of-fit statistic to investigate the plausibility of the posited model. …”
Get full text