Measuring Optical Scattering in Relation to Coatings on Crystalline X-Ray Scintillator Screens by Matthias Diez, Simon Zabler

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Artificial Intelligence in Virtual Screening: Transforming Drug Research and Discovery—A Review by Sayantani Roy, Karuppiah Nagaraj, Amit Mittal, Flora C. Shah, Kaliyaperumal Raja

“…Additionally, CHARMM software was applied for molecular dynamics simulations to calculate empirical energy functions. AI-driven algorithms such as KarmaDock and DeepDock were utilized for large-scale ligand screening and for improving protein–ligand docking accuracy. …”
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The use of pretrained neural networks for solving the problem of reverse searching of X-ray images of prohibited items and substances by A. K. Volkov, L. V. Mironova, S. E. Potapova

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Applications of Artificial Intelligence in Drug Repurposing by Zhaoman Wan, Xinran Sun, Yi Li, Tianyao Chu, Xueyu Hao, Yang Cao, Peng Zhang

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Intelligent screening of narrow anterior chamber angle based on portable slit lamp by Xingru He, Guangzheng Dai, Huixin Che, Chenguang Zhang, Hairu Yan, Yu Dang, Haifeng Dong

“…Despite generalization challenges, portable slit lamps equipped with advanced algorithms show promise for NACA screening.…”
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Using machine learning algorithms to predict colorectal cancer by Xingjian Xiao, Bo Hong, Kubra Maqsood, Xiaohan Yi, Guoqun Xie, Hailei Zhao, Bo Sun, Jianying Mao, Shiyou Liu, Xianglong Xu

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Using machine learning algorithms to predict colorectal polyps by Xingjian Xiao, Shiyou Liu, Kubra Maqsood, Xiaohan Yi, Guoqun Xie, Hailei Zhao, Bo Sun, Jianying Mao, Xianglong Xu

“…Interpretation: Using non-invasive factors and machine learning algorithms can accurately predict the occurrence of colorectal polyps in individuals with positive initial screening results. …”
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High-content screening (HCS) workflows for FAIR image data management with OMERO by Riccardo Massei, Wibke Busch, Beatriz Serrano-Solano, Matthias Bernt, Stefan Scholz, Elena K. Nicolay, Hannes Bohring, Jan Bumberger

“…Abstract High-content screening (HCS) for bioimaging is a powerful approach to studying biological processes, enabling the acquisition of large amounts of images from biological samples. …”
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In silico methods for immunogenicity risk assessment and human homology screening for therapeutic antibodies by Aimee E. Mattei, Andres H. Gutierrez, Soorya Seshadri, Jacob Tivin, Matt Ardito, Amy S. Rosenberg, William D. Martin, Anne S. De Groot

“…This toolkit has evolved and now contains an array of algorithms that can be used individually and/or consecutively for immunogenicity assessment and protein engineering. …”
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The role of artificial intelligence in breast cancer screening as a supportive tool for radiologists by Agata Król, Katarzyna Kwaterska, Karol Kutyłowski, Paweł Łuckiewicz

“…Difficulties, possible errors and people’s opinion were also highlighted. Conclussion AI algorithms find their potential application in breast cancer screening, mainly as a supportive tool for radiologists. …”
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Machine learning algorithms reveal the secrets of mitochondrial dynamics by Jack J Collier, Robert W Taylor

“…In this issue of EMBO Molecular Medicine, supervised machine learning algorithms underlie a novel tool that enables automated, high throughput and unbiased screening of changes in mitochondrial morphology measured using confocal microscopy. …”
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Development and Clinical Validation of Visual Inspection With Acetic Acid Application-Artificial Intelligence Tool Using Cervical Images in Screen-and-Treat Visual Screening for Ce... by Usha Rani Poli, Anirudh G. Gudlavalleti, Jaya Bharadwaj Y, Hira B. Pant, Varun Agiwal, G.V.S. Murthy

“…The perceived challenge rate for false positives was 20%.CONCLUSIONThis novel cervical image–based VIA-AI algorithm showed promising results in real-life settings, and could help minimize overtreatment in single-visit VIA screening and treatment programs in resource-constrained situations.…”
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Screening of serum biomarkers in patients with PCOS through lipid omics and ensemble machine learning. by Ji-Ying Chen, Wu-Jie Chen, Zhi-Ying Zhu, Shi Xu, Li-Lan Huang, Wen-Qing Tan, Yong-Gang Zhang, Yan-Li Zhao

“…Three machine learning models, logistic regression, random forest, and support vector machine, showed that screened biomarkers had better classification ability and effect. …”
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Inferring regulatory networks by combining perturbation screens and steady state gene expression profiles. by Ali Shojaie, Alexandra Jauhiainen, Michael Kallitsis, George Michailidis

“…The proposed approach is based on a three-step algorithm to estimate the underlying directed but cyclic network, that uses as input both perturbation screens and steady state gene expression data. …”
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Development of Electronic Nose as a Complementary Screening Tool for Breath Testing in Colorectal Cancer by Chih-Dao Chen, Yong-Xiang Zheng, Heng-Fu Lin, Hsiao-Yu Yang

“…We then used machine learning algorithms to develop predictive models and provided the estimated accuracy and reliability of the breath testing. (3) Results: We enrolled 77 patients, with 40 cases and 37 controls. …”
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Design and evaluation of screening and self-care (mobile) application for oral and dental problems and emergencies by Sedighe Sadat Hashemikamangar, Aidin Sooratgar, Mina Khayamzadeh, Shayan Momeni, Ali Asghar Safaei, Behnaz Behniafar

“…Materials and method: A system made up of web and mobile apps is proposed and evaluated for screening and self-care of oral and dental problems and for providing advice on dental emergencies and therapeutic measures. …”
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Advancements in biomarkers and machine learning for predicting of bronchopulmonary dysplasia and neonatal respiratory distress syndrome in preterm infants by Hanieh Talebi, Seyed Alireza Dastgheib, Maryam Vafapour, Reza Bahrami, Mohammad Golshan-Tafti, Mahsa Danaei, Sepideh Azizi, Amirhossein Shahbazi, Melina Pourkazemi, Maryam Yeganegi, Amirmasoud Shiri, Ali Masoudi, Heewa Rashnavadi, Hossein Neamatzadeh

“…For nRDS, biomarkers such as the lecithin/sphingomyelin (L/S) ratio and oxidative stress indicators have been effectively used in innovative diagnostic methods, including attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) and high-content screening for ABCA3 modulation. Machine learning algorithms like Partial Least Squares Regression (PLSR) and C5.0 have shown potential in accurately identifying critical health indicators. …”
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基于脑影像及临床特征的机器学习模型预测缺血性卒中后心房颤动 A Machine Learning Model Based on Brain Imaging and Clinical Features for Predicting Atrial Fibrillation Detected after Stroke... by 张栗源1，刘涛2，姜勇1，李子孝1，3，王拥军1，3，杨晓萌1，3(ZHANG Liyuan1, LIU Tao2, JIANG Yong1, LI Zixiao1,3, WANG Yongjun1,3, YANG Xiaomeng1,3 )

“…After retaining independent features, the least absolute shrinkage and selection operator (LASSO) regression algorithm was used for feature selection and to construct a joint prediction model. …”
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Improvement of the Diagnostics of the Fetus Heart Anomalies During a Routine Screening Ultrasound Examination by Markin L., Medvjedjeva O.

“…In our opinion, the prenatal detection of congenital heart defects strongly depends on the algorithm of conducting a fetal heart study in the screening regimen. …”
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Short-Term Photovoltaic Power Combined Prediction Based on Feature Screening and Weight Optimization by Liqing Geng, Yadong Yang, Genghuang Yang, Yongfeng Zheng, Xiaocong Liu

“…Aiming at the problem of low prediction accuracy caused by the intermittent and fluctuating characteristics of photovoltaic power, a short-term photovoltaic power combined prediction method based on feature screening and weight optimization is proposed. Firstly, K-means is used to cluster the photovoltaic power; Secondly, CEEMDAN is used to decompose photovoltaic power and wavelet decomposition is used to decompose irradiance, and sample entropy and K-means are used to reconstruct each component of photovoltaic power into high, intermediate, and low frequency terms; Then, Spearman’s correlation coefficient is used to calculate the correlation between each meteorological factor and the decomposed irradiance component and the high, intermediate, and low frequency terms of photovoltaic power, and the feature selection is carried out; Then, CNN-BiLSTM-Attention is used to predict the high frequency term, LSTM is used to predict the intermediate frequency and low frequency terms, and the results are superimposed to obtain the preliminary prediction value; Finally, the dung beetle algorithm is used to optimize the weights of the initial prediction values of the training set of high, intermediate, and low frequency terms, and the optimal weight is substituted into the test set to obtain the final prediction result. …”
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