Supervised machine learning and genotype by trait biplot as promising approaches for selection of phytochemically enriched Rhus coriaria genotypes by Hamid Hatami Maleki, Reza Darvishzadeh, Ahmad Alijanpour, Yousef Seyfari

“…By using 13 feature selection algorithms, ISSR loci (U823) L1, (U835) L1, (U801) L1, (U816) L2, (U816) L4, (U835) L4, (U854) L1, and (U835) L9 were identified as functional markers which could predict phytochemical response of sumac germplasm. …”
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Characteristics of Extreme Runoff in Beijiang River Basin during Dry Season from 1954 to 2020 by ZENG Zhiping, ZHENG Yanhui, ZHOU Yueying, GU Xihui, HE Yanhu

“…It also employed indicators of hydrological alteration (IHA), heuristic segmentation algorithms, Mann-Kendall trend test, and wavelet analysis to examine the distribution characteristics, trends, variability, and cycle of extreme runoff in the Beijiang River Basin during dry season across various time scales. …”
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EPIGENETIC REGULATION OF GENE EXPRESSION IN HEAD AND NECK SQUAMOUS CELL CARCINOMA: THERAPEUTIC PERSPECTIVES by R. B. Samsonov, Z. A. Radzhabova, Yu. V. Cheburkin, V. A. Klyuge, E. V. Tkachenko, A. V. Malek

“…Unfortunately, optimization of diagnostic algorithms and wide implementation of instrumental diagnostics (ultrasound examination, computed tomography, fiber endoscopy) do not improve the situation. …”
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Accurate bladder cancer diagnosis using ensemble deep leaning by Rana A. El-Atier, M. S. Saraya, Ahmed I. Saleh, Asmaa H. Rabie

“…The proposed EDL consists of three deep learning algorithms, which are; Convolutional Neural Network (CNN), Generative Adversarial Network (GAN), and a new deep learning method called Explainable Deep Learning (XDL) that depends on Guided Gradient Weighted Class Activation Map (Guided Grad-CAM). …”
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Machine learning-based identification of exosome-related biomarkers and drugs prediction in nasopharyngeal carcinoma by Zhengyu Wei, Guoli Wang, Yanghao Hu, Chongchang Zhou, Yuna Zhang, Yi Shen, Yaowen Wang

“…Results Through the application of three machine learning algorithms, five key genes (LTF, IDH1, ITGAV, CCL2, and LGALS3BP) were identified for the construction of a diagnostic model. …”
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Deep learning driven methodology for the prediction of mushroom moisture content using a novel LED-based portable hyperspectral imaging system by Kai Yang, Ming Zhao, Dimitrios Argyropoulos

“…For comparison purposes, state-of-the-art machine learning algorithms, i.e., support vector machine regression (SVMR) and partial least squares regression (PLSR) were also investigated for the model development based on five spectra pre-processed methods using two different lighting systems i.e., enhanced light-emitting diode (LED) and tungsten halogen (TH). …”
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Prediction of Pt, Ir, Ru, and Rh complexes light absorption in the therapeutic window for phototherapy using machine learning by V. Vigna, T. F. G. G. Cova, A. A. C. C. Pais, E. Sicilia

“…The model is efficient, fast, and resource-light, using decision tree-based algorithms that provide interpretable results. This interpretability helps to understand classification rules and facilitates targeted structural modifications to convert inactive complexes into potentially active ones.…”
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Phase tomography with axial structured illumination by Nishant Goyal, Kedar Khare

“…Iterative frameworks naturally offer several advantages for addressing the data incompleteness issues (e.g. missing illumination angles) and have superior noise handling capability, since they employ suitable constraint functions. Despite this algorithmic framework shift, the HT system hardware still largely uses the multi-angle illumination geometries that were suitable for reconstructions based on the Fourier diffraction theorem. …”
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The value of radiomics features of white matter hyperintensities in diagnosing cognitive frailty: a study based on T2-FLAIR imaging by Qinmei Liao, Xihao Hu, Zhiqiong Jiang, Xiaoyun Huang, Jiacheng Guo, Yuanzhong Zhu, Wenjing He

“…Three machine learning algorithms—K-Nearest Neighbors (KNN), Logistic Regression (LR), and Support Vector Machine (SVM)—were used to construct radiomic models, clinical models, and combined models. …”
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Identification of hub immune-related genes and construction of predictive models for systemic lupus erythematosus by bioinformatics combined with machine learning by Su Zhang, Weitao Hu, Yuchao Tang, Hongjie Lin, Xiaoqing Chen

“…Three machine learning algorithms were applied to DE-IRGs to screen for hub DE-IRGs. …”
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Machine learning unveils key Redox signatures for enhanced breast Cancer therapy by Tao Wang, Shu Wang, Zhuolin Li, Jie Xie, Kuiying Du, Jing Hou

“…We employed a comprehensive approach by combining ten distinct machine learning algorithms across 108 different combinations to scrutinize 88 pre-existing signatures of breast cancer. …”
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Pain sensitisation and joint inflammation in patients with active rheumatoid arthritis by Sam Norton, Bruce W Kirkham, James Galloway, Khaldoun Chaabo, Toby Garrood, Zoe Rutter-Locher, Estee Chan, Alex Vincent

“…We were unable to develop algorithms to identify different groups.Conclusion The unexpected group −FM−PD group may have peripheral nociplastic pain, not commonly recognised in rheumatology. …”
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Prediction of clinical stages of cervical cancer via machine learning integrated with clinical features and ultrasound-based radiomics by Maochun Zhang, Qing Zhang, Xueying Wang, Xiaoli Peng, Jiao Chen, Hanfeng Yang

“…Prediction models were developed utilizing several ML algorithms by Python based on an integrated dataset of clinical features and ultrasound radiomics. …”
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Statistical analysis of the formation mechanism of concepts-representations in organizational and technical systems by Alexander A. Solodov

“…Within the framework of the computer paradigm in organizational and technical systems, such obvious principles as digital representation of information and its processing with the use of algorithms implemented by computing means are used. …”
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Dynamics of quality of life of head and neck cancer patients after treatment. Clinical significance by A. V. Karpenko, R. R. Sibgatullin, A. A. Boyko, M. G. Kostova, O. M. Nikolayeva, E. M. Trunin

“…Site-specific EORTC QLQ-H&N35 questionnaire revealed several positive (pain in the head and neck, feeling ill, use of painkillers and weight gain) and negative (public eating, problems with taste and smell, sticky saliva and dry mouth) changes. Applying algorithms for determining clinical significance changed the number and value of several scales and domains. …”
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Impact of image reconstruction on cerebral blood flow measured with 15O-water positron emission tomography by Elin Bäck, My Jonasson, Elin Lindström, Andreas Tolf, Joachim Burman, Lieuwe Appel, Mark Lubberink

“…Images were reconstructed using two different algorithms; ordered subset expectation maximisation and block sequential regularised expectation maximalisation with varying reconstruction parameters. …”
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Development of a 101.6K liquid‐phased probe for GWAS and genomic selection in pine wilt disease‐resistance breeding in Masson pine by Jingyi Zhu, Qinghua Liu, Shu Diao, Zhichun Zhou, Yangdong Wang, Xianyin Ding, Mingyue Cao, Dinghui Luo

“…A total of 548 SNPs were considerably associated with disease‐resistance traits using four GWAS algorithms. Among them, 283 were located on or linked to 169 genes, including common plant disease resistance‐related protein families such as NBS‐LRR and AP2/ERF. …”
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Experimental investigation of shaft misalignment effects on bearing reliability through vibration signal analysis using machine learning and deep learning by Fransiskus Tatas Dwi Atmaji, Jamasri, Hari Agung Yuniarto, I Made Miasa

“…Six classification models—five machine learning algorithms (Multilayer Perceptron, Random Forest, Decision Tree, K-Nearest Neighbors, and Adaptive Boosting) and one deep learning model (Long Short-Term Memory, LSTM)—were evaluated for classifying four levels of misalignment severity. …”
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A comprehensive in silico investigation unravels the structural and functional consequences of non-synonymous single-nucleotide polymorphisms in human OXTR gene by Puja Mazumder, Shuvo Chandra Das, Sobuj Mondol, Ibrahim Khalil Afif, Mithu Howlader, Shipan Das Gupta, Md. Murad Hossain, Md. Mijanur Rahman

“…Twenty different sequence and structure-based bioinformatics tools and algorithms were utilized to characterize the pathogenic impacts of nsSNPs on the structure, function, stability, and conservation of OXTR protein. …”
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Developing and validating an artificial intelligence-based application for predicting some pregnancy outcomes: a multi-phase study protocol by Fatemeh Shabani, Ata Jodeiri, Sakineh Mohammad‑Alizadeh‑Charandabi, Fatemeh Abbasalizadeh, Jafar Tanha, Mojgan Mirghafourvand

“…In Phase 2, an artificial intelligence model will be developed using machine learning algorithms such as Random Forest, XGBoost, Support Vector Machines (SVM), and neural networks, followed by model training, validation, and integration into a user-friendly application. …”
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