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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Optimizing skin cancer screening with convolutional neural networks in smart healthcare systems. by Ali Raza, Akhtar Ali, Sami Ullah, Yasir Nadeem Anjum, Basit Rehman

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Catalyzing early ovarian cancer detection: Platelet RNA-based precision screening by Eunyong Ahn, Se Ik Kim, Sungmin Park, Sarah Kim, Hyejin Lee, Yeochan Kim, Sangick Park, Suyeon Lee, Dong Won Hwang, Heeyeon Kim, HyunA Jo, Untack Cho, Juwon Lee, Cheol Lee, TaeJin Ahn, Yong-Sang Song

“…We diverged from traditional methods by employing intron-spanning reads (ISR) counts rather than gene expression levels to use splice junctions as features in our models. If integrated with current screening methods, our algorithm holds promise for identifying ovarian or endometrial cancer in its early stages.…”
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Measuring Optical Scattering in Relation to Coatings on Crystalline X-Ray Scintillator Screens by Matthias Diez, Simon Zabler

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Investigation of the influence of the heterogeneous structure of concrete on its strength by V.M. Volchuk, M.A. Kotov, Ye G. Plakhtii, O.A. Tymoshenko, O.H. Zinkevych

“…This approach allowed screening out low-sensitivity structure features from the multifractal spectrum. …”
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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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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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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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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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Analysis of vehicle and pedestrian detection effects of improved YOLOv8 model in drone-assisted urban traffic monitoring system. by Huili Dou, Sirui Chen, Fangyuan Xu, Yuanyuan Liu, Hongyang Zhao

“…The multi-scale feature fusion module enhances the model's detection ability for targets of different sizes by combining feature maps of different scales; the improved non-maximum suppression algorithm effectively reduces repeated detection and missed detection by optimizing the screening process of candidate boxes. …”
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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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Molecular function validation and prognostic value analysis of the cuproptosis-related gene ferredoxin 1 in papillary thyroid carcinoma by Shiyue He, Wenzhong Peng, Xinyue Hu, Yong Chen

“…LASSO regression analyses were utilized to screen the optimal combination of cuproptosis-related genes for constructing a Cox proportional-hazards model, and the cuproptosis-related risk score (CRRS) was calculated to stratify PTC patients in prognosis. …”
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A risk signature constructed by Tregs-related genes predict the clinical outcomes and immune therapeutic response in kidney cancer by Gang Li, Jingmin Cui, Tao Li, Wenhan Li, Peilin Chen

“…Through the machine learning algorithm—Boruta, the potentially important KTRGs were screened further and submitted to construct a risk model. …”
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Machine learning for epithelial ovarian cancer platinum resistance recurrence identification using routine clinical data by Li-Rong Yang, Mei Yang, Liu-Lin Chen, Yong-Lin Shen, Yuan He, Zong-Ting Meng, Wan-Qi Wang, Feng Li, Zhi-Jin Liu, Lin-Hui Li, Yu-Feng Wang, Xin-Lei Luo

“…Following this screening process, five machine learning algorithms were employed to develop predictive models based on the selected variables. …”
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Development of a high-performing, cost-effective and inclusive Afrocentric predictive model for stroke: a meta-analysis approach  by M Nweke, P Oyirinnaya, P Nwoha, SB Mitha, N Mshunqane, N Govender, M Ukwuoma, SC Ibeneme

“…Conclusions Targeted screening via the CAPMS 1 and CAPMS 2 models offers a cost-effective solution for stroke screening in African clinics and communities. …”
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Analysis and validation of novel biomarkers related to palmitoylation in adenomyosis by Hongyu Zhang, Yufeng Li, Huijuan Cao, Yiling Zhao, Hongwen Zhu, Tiansheng Qin

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Prognostic model of lung adenocarcinoma from the perspective of cancer-associated fibroblasts using single-cell and bulk RNA-sequencing by Jiarui Zhao, Chuanqing Jing, Rui Fan, Wei Zhang

“…Further, our inverse convolution algorithm showed that MyCAFs have prognostic potential in LUAD, and via LASSO-COX model regression, we obtained a MyCAFs-related prognostic model. …”
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Artificial Intelligence–Aided Diagnosis System for the Detection and Classification of Private-Part Skin Diseases: Decision Analytical Modeling Study by Wei Wang, Xiang Chen, Licong Xu, Kai Huang, Shuang Zhao, Yong Wang

“…MethodsIn this decision analytical modeling study, a 2-stage AI-aided diagnosis system was developed to classify PPSDs. …”
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Integrating machine learning models with multi-omics analysis to decipher the prognostic significance of mitotic catastrophe heterogeneity in bladder cancer by Haojie Dai, Zijie Yu, You Zhao, Ke Jiang, Zhenyu Hang, Xin Huang, Hongxiang Ma, Li Wang, Zihao Li, Ming Wu, Jun Fan, Weiping Luo, Chao Qin, Weiwen Zhou, Jun Nie

“…Subsequently by multivariate cox regression as well as survshap(t) model we screened core prognostic gene and identified it by Mendelian randomization. …”
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