Machine learning-assisted quantitative metabolomics of West African patients with advanced breast cancer by Aboubacar Dit Tietie Bissan, Mathieu Michel, Xavier Dieu, Cinzia Bocca, Awo Emmanuela Hilda Amegonou, Fatoumata Matokoma Sidibe, Madani Ly, Bocary Sidi Koné, Nènè Oumou Kesso Barry, Kletigui Casimir Dembélé, Bakary Cissé, Bourèma Kouriba, Gilles Simard, Delphine Mirebeau-Prunier, Juan Manuel Chao de la Barca, Zahra Ouzzif, Pascal Reynier

“…The data were compared with those of 79 healthy controls using a combination of several machine learning approaches and statistical analyses. The predictive models obtained with the machine learning algorithms were comparable, with the best AUC of 0.878 obtained with ridge logistic regression using Boruta feature selection. …”
Get full text

Research on Stacking Distribution of Steel Plates Input Based on Improved Multi-objective Particle Swarm Optimization by ZHONG Chuanjie, CHENG Wenming, DU Run, GAO Xuchao, ZHANG Lin

“…The simulation was conducted using a small-batch data scale that best fit the actual production requirements of the steel sheet stock warehouse. The algorithm without the Levy flight speed update strategy exhibited significantly reduced convergence, while the algorithm without the local search strategy showed a marked reduction in diversity. …”
Get full text

Deep learning in microbiome analysis: a comprehensive review of neural network models by Piotr Przymus, Krzysztof Rykaczewski, Adrián Martín-Segura, Jaak Truu, Enrique Carrillo De Santa Pau, Mikhail Kolev, Mikhail Kolev, Irina Naskinova, Aleksandra Gruca, Alexia Sampri, Alexia Sampri, Marcus Frohme, Alina Nechyporenko, Alina Nechyporenko

“…These computational techniques have become essential for addressing the inherent complexity and high-dimensionality of microbiome data, which consist of different types of omics datasets. Deep learning algorithms have shown remarkable capabilities in pattern recognition, feature extraction, and predictive modeling, enabling researchers to uncover hidden relationships within microbial ecosystems. …”
Get full text

Effectiveness of biomarker-guided duration of antibiotic treatment in children hospitalised with confirmed or suspected bacterial infection: the BATCH RCT by Cherry-Ann Waldron, Philip Pallmann, Simon Schoenbuchner, Debbie Harris, Lucy Brookes-Howell, Céu Mateus, Jolanta Bernatoniene, Katrina Cathie, Saul N Faust, Josie Henley, Lucy Hinds, Kerry Hood, Chao Huang, Sarah Jones, Sarah Kotecha, Sarah Milosevic, Helen Nabwera, Sanjay Patel, Stéphane Paulus, Colin VE Powell, Jenny Preston, Huasheng Xiang, Emma Thomas-Jones, Enitan D Carrol

“…In the presence of robust AMS programmes to reduce antibiotic use, a PCT-guided algorithm may offer little added value. Future trials must include an implementation framework to improve trial intervention fidelity, and repeated cycles of education and training to facilitate implementation of biomarker-guided algorithms into routine clinical care. …”
Get full text

Cyclical hybrid imputation technique for missing values in data sets by Kurban Kotan, Serdar Kırışoğlu

“…Because incorrect imputation of missing data increases the error in the modeling phase and reduces the prediction performance of the model. When it comes to health, it is inevitable to choose models that show a higher success rate. …”
Get full text

Machine learning based on patch antenna design and optimization for 5 G applications at 28GHz by Md․Sohel Rana, Sheikh Md․ Rabiul Islam, Sanjukta Sarker

“…The efficiency of the design-I was 89.66 %, and design-II was, 52.65 %. In this paper, a predictive model was developed using a polynomial regression algorithm to create a predictive model for the designed antenna. …”
Get full text

A comparative study of bone density in elderly people measured with AI and QCT by Min Guo, Min Guo, Yu Zhang, Yu Zhang, XinXin Gu, XinXin Gu, Xuhui Liu, Xuhui Liu, Fei Peng, Fei Peng, Zongjun Zhang, Zongjun Zhang, Mei Jing, Mei Jing, Yingxia Fu, Yingxia Fu

“…The linear regression fit between the R2 values of QCT and Bone Density AI for measuring lumbar spine BMD with different equipment ranged from 0.88 to 0.96, indicating a high degree of consistency between the two measurement methods across devices.ConclusionThis multicenter study pioneers a dual-validation framework to establish the clinical validity of deep learning-based BMD prediction algorithms using routine thoracic/abdominal CT scans. …”
Get full text

Short-Term Traffic Flow Forecasting Model Based on GA-TCN by Rongji Zhang, Feng Sun, Ziwen Song, Xiaolin Wang, Yingcui Du, Shulong Dong

“…The prediction error was considered as the fitness value and the genetic algorithm was used to optimize the filters, kernel size, batch size, and dilations hyperparameters of the temporal convolutional neural network to determine the optimal fitness prediction model. …”
Get full text

Calibration Transfer of Near-Infrared Spectroscopic Model for Soluble Solid Content Predication of Apples by the Combined Use of Direct Standardization and Piecewise Direct Standar... by CHENG Ye, HUANG Haoran, WANG Ying, XIONG Zhixin

“…The results indicated that compared with DS and PDS, the combined algorithm not only significantly improved the prediction performance of the model for the slave instrument, but also mitigated the artifacts caused by PDS. …”
Get full text

Using Features Extracted From Upper Limb Reaching Tasks to Detect Parkinson’s Disease by Means of Machine Learning Models by Giuseppe Cesarelli, Leandro Donisi, Francesco Amato, Maria Romano, Mario Cesarelli, Giovanni D'Addio, Alfonso M. Ponsiglione, Carlo Ricciardi

“…The investigation carried out in our work has proved the predictive power of the features, extracted from the reaching tasks involving the upper limbs, to distinguish HCs and PD patients.…”
Get full text

Machine learning identifies lipid-associated genes and constructs diagnostic and prognostic models for idiopathic pulmonary fibrosis by Xingren Liu, Junmei Song, Shujin Guo, Yi Liao, Jun Zou, Liqing Yang, Caiyu Jiang

“…Genes from this module were used to construct diagnostic and prognostic models, which demonstrated strong predictive performance across multiple validation datasets. …”
Get full text

METTL1-driven nucleotide metabolism reprograms the immune microenvironment in hepatocellular carcinoma: a multi-omics approach for prognostic biomarker discovery by Xie Weng, Yangyue Huang, Zhuoya Fu, Xingli Liu, Fuli Xie, Jiale Wang, Qiaohua Zhu, Dayong Zheng, Dayong Zheng

“…Moreover, the prognostic model integrating METTL1 expression and immune checkpoint profiles shows strong predictive performance across independent cohorts, highlighting its potential clinical utility.ConclusionThis study highlights the innovative role of METTL1-driven nucleotide metabolism reprogramming in reshaping the immune microenvironment of HCC. …”
Get full text

Intelligent CO2 Monitoring for Diagnosis of Sleep Apnea Using Neural Cryptography Techniques by Manar Ahmed Hamza, Maha M. Althobaiti, Ola Abdelgney Omer Ali, Souad Larabi-Marie-Sainte, Majdy M. Eltahir, Anwer Mustafa Hilal, Mesfer Al Duhayyim, Ishfaq Yaseen

“…The key advantage of the proposed work shows excellent performance in the prediction of adsorbing carbon and accuracy. The accuracy of the GEP-KNN algorithm with different K values produced the highest accuracy at K=9 and k=10 of 95.12% and 95.67%; the lowest accuracy is K=1 of 65.34%.…”
Get full text

Flight safety event management method as illustrated by a guided reactivity model by B. I. Bachkalo, S. D. Baynetov, S. G. Bolbat

“…The article provides a brief analysis of the problems of the existing flight safety management system in the state aviation of the Russian Federation, based on the lack of stable positive dynamics of accidents reduction. Brief conclusions from the analysis of the currently proposed scientific approaches for accidents reduction are given. …”
Get full text

A new approach to K-nearest neighbors distance metrics on sovereign country credit rating

“…The primary objective is to enhance KNN's predictive accuracy by integrating a feature importance mechanism derived from the random forest algorithm, which prioritizes significant features and reduces the impact of less relevant ones, refining the distance computation within KNN. …”
Get full text

Simultaneous determination of the amylose and amylopectin content of foxtail millet flour by hyperspectral imaging by Guoliang Wang, Min Liu, Hongtao Xue, Erhu Guo, Aiying Zhang

“…Results demonstrated that the key band extraction combined algorithm effectively reduced data dimension without compromising the accuracy of the prediction model. …”
Get full text

Russian Cross-disciplinary Consensus on the diagnosis and treatment of spondyloarthritis associated with inflammatory bowel diseases by Galina V. Lukina, Oleg V. Knyazev, Elena A. Belousova, Diana I. Abdulganieva, Elena N. Aleksandrova, Igor G. Bakulin, Olga I. Barysheva, Maria А. Borisova, Bella А. Vykova, Alla А. Godzenko, Irina V. Gubonina, Tatiana V. Dubinina, Tatiana N. Zhigalova, Evgeny V. Zhilyaev, Anna V. Kagramanova, Tatiana V. Korotaeva, Alexander V. Kuzin, Maria A. Livzan, Aleksander M. Lila, Vadim I. Mazurov, Evgeny L. Nasonov, Alexander A. Novikov, Marina F. Osipenko, Asfold I. Parfenov, Larisa V. Tarasova, Olga V. Khlynova, Marina V. Shapina, Oksana B. Shchukina, Shandor F. Erdes, Aleksei A. Iakovlev

Get full text

Comprehensive profiling of chemokine and NETosis-associated genes in sarcopenia: construction of a machine learning-based diagnostic nomogram by Yingwei Wang, Le Wang, Yan Zhang, Minghui Wang, Huaying Zhao, Cheng Huang, Huaiyang Cai, Shuangyang Mo

“…The nomogram models demonstrated high predictive accuracy in distinguishing sarcopenia from both healthy and pre-sarcopenic states, as evidenced by AUC values of 0.837 (95% CI 0.703–0.947) and 0.903 (95% CI 0.789–0.989), respectively. …”
Get full text

Enhancing security and efficiency in Mobile Ad Hoc Networks using a hybrid deep learning model for flooding attack detection by Pramodh Krishna D., E. Sandhya, Khaja Shareef Sk, Srihari Varma Mantena, Venkata Subbaiah Desanamukula, Ch Koteswararao, Srinivasa Rao Vemula, Maruthi Vemula

“…To optimize the model’s efficiency, a unique DECEHGS algorithm combining Differential Evolution and Evolutionary Population Dynamics techniques is employed, enhancing both convergence and performance. …”
Get full text

Application of Bivariate Reproducing Kernel-Based Best Interpolation Method in Electrical Tomography by Yongguang Tan, Jingqi Wang, Junqi Yu, Boqi Wu, Jinchao Shen, Xiangchen Guo

Get full text