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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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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Swedish regional population-based organised prostate cancer testing: why, what and how? by Ola Bratt, Salma Tunå Butt, Charlotte Carlsson, Lisa Jelf-Eneqvist, Olof Gunnarsson, Alma Ihre, Thomas Jiborn, Anna Lantz, Heide Larsson, Helena Strömqvist, Johan Styrke, Nils-Erik Svedberg, Rebecka Arnsrud Godtman

“…A general experience is that communication and organisational matters have been more challenging than medical decisions. Conclusions: The Swedish population-based OPT programmes provide organisational experiences, diagnostic outcomes, and research results of value for future national prostate cancer screening programmes. …”
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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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Prevalence of advanced liver fibrosis in the general population of the Paris region according to FIB-4 score and liver risk score by Henri Tran, Stefano Caruso, Anne-Laure Mazialivoua, Cecile Fargeat, Odile Rousselet, Adrien Ko, Vincent Leroy, Patrick Ingiliz

“…An adapted new pragmatic screening algorithm using LRS should be considered.…”
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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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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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A New Computer-Aided Diagnosis System for Breast Cancer Detection from Thermograms Using Metaheuristic Algorithms and Explainable AI by Hanane Dihmani, Abdelmajid Bousselham, Omar Bouattane

“…To achieve these goals, we proposed a new multi-objective optimization approach named the Hybrid Particle Swarm Optimization algorithm (HPSO) and Hybrid Spider Monkey Optimization algorithm (HSMO). …”
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Influencing factors and prediction algorithms to estimate public preference for painless medical procedures in China: a national cross-sectional study by Xiangyang Guo, Xiaoxiao Wang, Yanan Song, Chen Luo, Zhengqian Li, Wei You, Sichen Liu, Kaixi Liu

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Combined Prediction of Dust Concentration in Opencast Mine Based on RF-GA-LSSVM by Shuangshuang Xiao, Jin Liu, Yajie Ma, Yonggui Zhang

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Machine learning algorithms that predict the risk of prostate cancer based on metabolic syndrome and sociodemographic characteristics: a prospective cohort study by Tao Thi Tran, Jeonghee Lee, Junetae Kim, Sun-Young Kim, Hyunsoon Cho, Jeongseon Kim

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Machine learning algorithms predict breast cancer incidence risk: a data-driven retrospective study based on biochemical biomarkers by Qianqian Guo, Peng Wu, Junhao He, Ge Zhang, Wu Zhou, Qianjun Chen

“…Abstract Background Current breast cancer prediction models typically rely on personal information and medical history, with limited inclusion of blood-based biomarkers. …”
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Mean Nocturnal Baseline Impedance (MNBI) Provides Evidence for Standardized Management Algorithms of Nonacid Gastroesophageal Reflux-Induced Chronic Cough by Yiqing Zhu, Tongyangzi Zhang, Shengyuan Wang, Wanzhen Li, Wenbo Shi, Xiao Bai, Bingxian Sha, Mengru Zhang, Siwan Wen, Cuiqin Shi, Xianghuai Xu, Li Yu

“…Proximal MNBI < 2140 Ω may be used to screen patients with nonacid GERC suitable for standard antireflux therapy and in standardized management algorithms for nonacid GERC. …”
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Research of color models in digital graphics by Hetman Oksana, Shpetna Svitlana

“…The study focuses on a detailed examination of the RGB, CMYK, HSL/HSV, and LAB color models. It is established that the RGB model is an additive system optimized for screens and displays, as it provides a broad and vibrant color range suitable for digital applications. …”
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Data Collection for Automatic Depression Identification in Spanish Speakers Using Deep Learning Algorithms: Protocol for a Case-Control Study by Luis F Brenes, Luis A Trejo, Jose Antonio Cantoral-Ceballos, Daniela Aguilar-De León, Fresia Paloma Hernández-Moreno

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Using data science to diagnose and characterize heterogeneity of Alzheimer's disease by Ting F.A. Ang, Ning An, Huitong Ding, Sherral Devine, Sanford H. Auerbach, Joseph Massaro, Prajakta Joshi, Xue Liu, Yulin Liu, Elizabeth Mahon, Rhoda Au, Honghuang Lin

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A voice-based algorithm can predict type 2 diabetes status in USA adults: Findings from the Colive Voice study. by Abir Elbéji, Mégane Pizzimenti, Gloria Aguayo, Aurélie Fischer, Hanin Ayadi, Franck Mauvais-Jarvis, Jean-Pierre Riveline, Vladimir Despotovic, Guy Fagherazzi

“…Our findings suggest that voice-based algorithms could serve as a more accessible, cost-effective, and noninvasive screening tool for T2D. …”
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Enhancing noninvasive pancreatic cystic neoplasm diagnosis with multimodal machine learning by Wei Huang, Yue Xu, Zhao Li, Jun Li, Qing Chen, Qiang Huang, Yaping Wu, Hongtan Chen

“…Remarkably, for patients with mucinous cystic neoplasms (MCNs), regardless of undergoing MRI or CT imaging, the model achieved a 100% prediction accuracy rate. It indicates that our non-invasive multimodal machine learning model offers strong support for the early screening of MCNs, and represents a significant advancement in PCN diagnosis for improving clinical practice and patient outcomes. …”
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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

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Optimization method for educational resource recommendation combining LSTM and feature weighting by Meixia Yang

“…Ordinary educational resource recommendation models are usually based on simple search functions and user profiles for recommendation. …”
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