Non-Destructive Detection of Silage pH Based on Colorimetric Sensor Array Using Extended Color Components and Novel Sensitive Dye Screening Method by Kai Zhao, Haiqing Tian, Jue Zhang, Yang Yu, Lina Guo, Jianying Sun, Haijun Li

“…Extended color components, a novel sensitive dye screening method, and a feature screening method were integrated and applied to enhance pH detection. …”
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Analytical screening of polymorphic variants of 20S proteasome genes when planning a study of pathogenetic effects of modification of NFKB1 post-translational processing by A. V. Meyer, M. V. Ulyanova, D. O. Imekina, A. D. Padyukova, T. A. Tolochko, E. A. Astafieva, M. B. Lavryashina

“…To calculate the genetic distances between populations, we used the methord of comparing the populations by frequencies of polymorphic marker alleles proposed by Ney, the obtained matrices are illustrated by the method of multidimensional scaling in space using Statistica v.8.0.Results. Discussion of the algorithm and results of analytical screening of polymorphic variants of 14 genes (PSMA1-PSMA7, PSMB1–PSMB7) encoding proteasome subunits 20S. …”
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Recent advances in AI-based toxicity prediction for drug discovery by Hyundo Lee, Jisan Kim, Ji-Woon Kim, Yoonji Lee, Yoonji Lee

“…The advent of computational approaches has accelerated a shift toward in silico modeling, virtual screening, and, notably, artificial intelligence (AI) to identify potential toxicities earlier in the pipeline. …”
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Software with artificial intelligence-derived algorithms for detecting and analysing lung nodules in CT scans: systematic review and economic evaluation by Julia Geppert, Peter Auguste, Asra Asgharzadeh, Hesam Ghiasvand, Mubarak Patel, Anna Brown, Surangi Jayakody, Emma Helm, Dan Todkill, Jason Madan, Chris Stinton, Daniel Gallacher, Sian Taylor-Phillips, Yen-Fu Chen

“…Although more data were available to populate the screening population model, there was substantial uncertainty across all models. …”
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Estimation of Canopy Water Content by Integrating Hyperspectral and Thermal Imagery in Winter Wheat Fields by Chenkai Gao, Shuimiao Liu, Pengnian Wu, Yanli Wang, Ke Wu, Lingyun Li, Jinghui Wang, Shilong Liu, Peimeng Gao, Zhiheng Zhao, Jing Shao, Haolin Yu, Xiaokang Guan, Tongchao Wang, Pengfei Wen

“…</b> Ultimately, the CWC prediction model of winter wheat hyperspectral characteristic bands and thermal imaging information fusion was created using the GRA algorithm. …”
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Deep Learning–Based Precision Cropping of Eye Regions in Strabismus Photographs: Algorithm Development and Validation Study for Workflow Optimization by Dawen Wu, Yanfei Li, Zeyi Yang, Teng Yin, Xiaohang Chen, Jingyu Liu, Wenyi Shang, Bin Xie, Guoyuan Yang, Haixian Zhang, Longqian Liu

“…The control experiment reduced image preparation time from 10 hours for manual cropping of 900 photos to 30 seconds with the automated model. Downstream strabismus screening task validation showed our model (with head tilt correction) achieving an area under the curve of 0.917 (95% CI 0.901‐0.933), surpassing Dlib-toolkit and faster R-CNN (both without head tilt correction) with an area under the curve of 0.856 (PP ConclusionsThis study delivers an AI-driven platform featuring a preprocessing algorithm that automates eye region cropping, correcting head tilt variations to improve image quality for AI development and clinical use. …”
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Comparison between logistic regression and machine learning algorithms on prediction of noise-induced hearing loss and investigation of SNP loci by Jie Lu, Xinhao Lu, Yixiao Wang, Hengdong Zhang, Lei Han, Baoli Zhu, Boshen Wang

“…The SNP loci screened by these models are pivotal in the process of NIHL prediction, which further improves the prediction accuracy of the model. …”
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Melanoma risk prediction models by Nikolić Jelena, Lončar-Turukalo Tatjana, Sladojević Srđan, Marinković Marija, Janjić Zlata

“…A continuous melanoma database growth would provide for further adjustments and enhancements in model accuracy as well as offering a possibility for successful application of more advanced data mining algorithms.…”
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Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study by Natthanaphop Isaradech, Wachiranun Sirikul, Nida Buawangpong, Penprapa Siviroj, Amornphat Kitro

“…The ML algorithms implemented in this study include the k-nearest neighbors algorithm, random forest ML algorithms, multilayer perceptron artificial neural network, logistic regression models, gradient boosting classifier, and linear support vector machine classifier. …”
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Rapid diagnosis of latent and active pulmonary tuberculosis by autofluorescence spectroscopy of blood plasma combined with artificial neural network algorithm by Fengjiao Yue, Si Li, Lijuan Wu, Xuerong Chen, Jianhua Zhu

“…This study demonstrates the possibility of using blood plasma autofluorescence spectroscopy and Artificial Neural Network (ANN) algorithm for the rapid and accurate diagnosis of latent and active pulmonary TB from healthy subjects. …”
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Joint Decision-Making Model Based on Consensus Modeling Technology for the Prediction of Drug-Induced Liver Injury by Yukun Wang, Xuebo Chen

“…Submodels for each consensus model were obtained through joint optimization. The parameters and features of each submodel were optimized jointly based on the hybrid quantum particle swarm optimization (HQPSO) algorithm. …”
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Machine Learning-Based Prediction of First Trimester Down Syndrome Risk in East Asian Populations by Chen YT, Chen GJ, Lin YS

“…Fourteen features (including maternal age, nuchal translucency thickness, serum markers, etc.) were input into the twelve machine learning models, along with seven data-balancing algorithms, to explore the risk prediction outcomes. …”
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Supervised machine learning algorithms for the classification of obesity levels using anthropometric indices derived from bioelectrical impedance analysis by Rodrigo Yáñez-Sepúlveda, Aldo Vásquez-Bonilla, Rodrigo Olivares, Pablo Olivares, Juan Pablo Zavala-Crichton, Claudio Hinojosa-Torres, Catalina Muñoz-Strale, Frano Giakoni-Ramírez, Josivaldo de Souza-Lima, Jacqueline Páez-Herrera, Jorge Olivares-Arancibia, Tomás Reyes-Amigo, Guillermo Cortés-Roco, Juan Hurtado-Almonacid, Eduardo Guzmán-Muñoz, Nicole Aguilera-Martínez, José Francisco López-Gil, Boryi A. Becerra-Patiño, Juan David Paucar-Uribe, Exal Garcia-Carrillo, Vicente Javier Clemente-Suárez

“…These models can enhance the effectiveness of obesity screening in clinical and community settings.…”
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Estimation of Daylily Leaf Area Index by Synergy Multispectral and Radar Remote-Sensing Data Based on Machine-Learning Algorithm by Minhuan Hu, Jingshu Wang, Peng Yang, Ping Li, Peng He, Rutian Bi

“…Because of low estimation accuracy of empirical models based on single-source data, we proposed a machine-learning algorithm combining optical and microwave remote-sensing data as well as the random forest regression (RFR) importance score to select features. …”
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Characterization and feature selection of volatile metabolites in Yangxian pigmented rice varieties through GC-MS and machine learning algorithms by Kaiqi Cheng, Ruonan Dong, Fei Pan, Wen Su, Lingjie Xi, Meng Zhang, Jingzhang Geng, Ruichang Gao, Ruichang Gao, Wengang Jin, A. M. Abd El-Aty, A. M. Abd El-Aty

“…Four machine learning models were further used for the classification of various colored rice varieties, and random forest model was the optimum for predicting classification, with an accuracy of 0.97. …”
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Throw out an oligopeptide to catch a protein: Deep learning and natural language processing-screened tripeptide PSP promotes Osteolectin-mediated vascularized bone regeneration by Yu Chen, Long Chen, Jinyang Wu, Xiaofeng Xu, Chengshuai Yang, Yong Zhang, Xinrong Chen, Kaili Lin, Shilei Zhang

“…In summary, our study established a precise and efficient composite model of DL and NLP to screen bioactive peptides, opening an avenue for the development of various peptide-based therapeutic strategies applicable to a broader range of diseases.…”
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Fault Classification in Power Transformers via Dissolved Gas Analysis and Machine Learning Algorithms: A Systematic Literature Review by Vuyani M. N. Dladla, Bonginkosi A. Thango

“…In this paper, a systematic literature review (SLR) is conducted using the Preferred Reporting Items for Systematic Reviews (PRISMA) framework to record and screen current research work pertaining to the application of machine learning algorithms for DGA-based transformer fault classification. …”
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Optimization of a Coupled Neuron Model Based on Deep Reinforcement Learning and Application of the Model in Bearing Fault Diagnosis by Shan Wang, Jiaxiang Li, Xinsheng Xu, Ruiqi Wu, Yuhang Qiu, Xuwen Chen, Zijian Qiao

“…Using the SNR as the evaluation metric, the algorithm performs data screening on the replay buffer parameters before training the deep network for predicting coupled neuron model performance. …”
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Data stewardship and curation practices in AI-based genomics and automated microscopy image analysis for high-throughput screening studies: promoting robust and ethical AI applicat... by Asefa Adimasu Taddese, Assefa Chekole Addis, Bjorn T. Tam

“…The study also examined specific AI considerations, such as algorithmic bias, model explainability, and the application of advanced cryptographic techniques. …”
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The implementation and appraisal of a novel confirmatory HIV-1 testing algorithm in the Microbicides Development Programme 301 Trial (MDP301). by Ute Jentsch, Precious Lunga, Charles Lacey, Jonathan Weber, Janet Cairns, Gisela Pinheiro, Sarah Joseph, Wendy Stevens, Sheena McCormack

“…This triggered the use of the algorithm which made use of archived serum and Buffy Coat samples. …”
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