Screening of multi deep learning-based de novo molecular generation models and their application for specific target molecular generation by Yishu Wang, Mengyao Guo, Xiaomin Chen, Dongmei Ai

“…Abstract Traditional virtual screening methods need to explore expanse and vast chemical spaces and need to be based on existing chemical libraries. …”
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Sensitive Multispectral Variable Screening Method and Yield Prediction Models for Sugarcane Based on Gray Relational Analysis and Correlation Analysis by Shimin Zhang, Huojuan Qin, Xiuhua Li, Muqing Zhang, Wei Yao, Xuegang Lyu, Hongtao Jiang

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A recurrent neural network and parallel hidden Markov model algorithm to segment and detect heart murmurs in phonocardiograms. by Andrew McDonald, Mark J F Gales, Anurag Agarwal

“…These properties make the algorithm a promising tool for screening of abnormal heart murmurs.…”
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Research on the optimization model of anti-breast cancer candidate drugs based on machine learning by Zhou Dong, Hong Chen, Yuchen Yang, Hairong Hao

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Prediction of Coiled Tubing Erosion Rate Based on Sparrow Search Algorithm Back-Propagation Neural Network Model by Yinping Cao, Fengying Fang, Guowei Wang, Wenyu Zhu, Yijie Hu

“…However, with the increase in fracturing, drilling, and sand-washing operations, the erosion of coiled tubing walls caused by solid particles has become one of the main failure modes. To accurately predict the erosion rate of coiled tubing, this study studied the influence law of erosion rate through experiments, screened the main influencing factors of erosion rate by grey relational analysis (GRA), and established a back-propagation neural network (BPNN) model optimized by the sparrow search algorithm (SSA) to predict the erosion rate. …”
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Machine learning algorithms in constructing prediction models for assisted reproductive technology (ART) related live birth outcomes by Junwei Peng, Xiaoyujie Geng, Yiyue Zhao, Zhijin Hou, Xin Tian, Xinyi Liu, Yuanyuan Xiao, Yang Liu

“…Multiple candidate predictors were screened out by using the importance scores. Four machine learning (ML) algorithms including random forest, extreme gradient boosting, light gradient boosting machine and binary logistic regression were used to construct prediction models. …”
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Construction of a predictive model for cognitive impairment among older adults in Northwest China by Yu Wang, Ni Wang, Yanjie Zhao, Xiaoyan Wang, Yuqin Nie, Liping Ding

“…Model performance was evaluated on the basis of the area under the curve, sensitivity, specificity, accuracy, F1 score, precision, and recall.ResultsA total of 12,332 older adults were recruited and screened with the Mini-Mental State Examination Scale. …”
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Does advancement in marker-less pose-estimation mean more quality research? A systematic review by Shivam Bhola, Shivam Bhola, Hyun-Bin Kim, Hyeon Su Kim, BonSang Gu, Jun-Il Yoo, Jun-Il Yoo

“…Publication frequency trend has accelerated in recent years, with more than half of these studies published after 2021. …”
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COMPUTER- AIDED MODELING AND IMPROVING OF RISOGRAPH PRINTING by P. E. Sulim, V. S. Yudenkov

“…The considered improvement of qualit y of the risofraph print based on a mathematical model in the environment Matlab by using the specialized algorithms and digital filter of the Image Processing Toolbox. …”
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DKK3 and SERPINB5 as novel serum biomarkers for gastric cancer: facilitating the development of risk prediction models for gastric cancer by Yan-Yu Liu, Yan-Yu Liu, Yan-Fang Fu, Yan-Fang Fu, Wan-Yu Yang, Wan-Yu Yang, Zheng Li, Zheng Li, Qian Lu, Qian Lu, Xin Su, Xin Su, Jin Shi, Si-Qi Wu, Di Liang, Yu-Tong He, Yu-Tong He

“…The existing gastric cancer (GC) risk prediction models based on biomarkers are limited. This study aims to identify new promising biomarkers for GC to develop a risk prediction model for effective assessment, screening, and early diagnosis. …”
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Construction of risk prediction model of sentinel lymph node metastasis in breast cancer patients based on machine learning algorithm by Qianmei Yang, Cuifang Liu, Yongyue Wang, Guifang Dong, Jinghuan Sun

“…Subsequently, five ML algorithms, namely LOGIT, LASSO, XGBOOST, RANDOM FOREST model and GBM model were employed to train and develop an ML model. …”
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Design of low-carbon planning model for vehicle path based on adaptive multi-strategy ant colony optimization algorithm by Qi Guo, Rui Li, Changjiang Zheng, Gwanggil Jeon

“…At the same time, the global search capability of the model is augmented via an ant colony optimization algorithm to ascertain the final optimized path. …”
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Development and validation of a biomarker-based prediction model for metastasis in patients with colorectal cancer: Application of machine learning algorithms by Erfan Ayubi, Sajjad Farashi, Leili Tapak, Saeid Afshar

“…Subsequently, the prediction model was developed and internally validated using five machine learning (ML) algorithms including lasso and elastic-net regularized generalized linear model (glmnet), k-nearest neighbors (kNN), support vector machine (SVM) with Radial Basis Function Kernel, random forest (RF), and eXtreme Gradient Boosting (XGBoost). …”
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Covariate Model Selection Approaches for Population Pharmacokinetics: A Systematic Review of Existing Methods, From SCM to AI by Mélanie Karlsen, Sonia Khier, David Fabre, David Marchionni, Jérôme Azé, Sandra Bringay, Pascal Poncelet, Elisa Calvier

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RETRACTED ARTICLE: Screening and identification of susceptibility genes for cervical cancer via bioinformatics analysis and the construction of an mitophagy-related genes diagnosti... by Zhang Zhang, Fangfang Chen, Xiaoxiao Deng

“…Abstract Purpose This study aims to utilize bioinformatics methods to systematically screen and identify susceptibility genes for cervical cancer, as well as to construct and validate an mitophagy-related genes (MRGs) diagnostic model. …”
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Development and validation of a quick screening tool for predicting neck pain patients benefiting from spinal manipulation: a machine learning study by Changxiao Han, Guangyi Yang, Haibao Wen, Minrui Fu, Bochen Peng, Bo Xu, Xunlu Yin, Ping Wang, Liguo Zhu, Minshan Feng

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Development of machine learning models to predict the risk of fungal infection following flexible ureteroscopy lithotripsy by Haofang Zhang, Changbao Xu, Chenge Hu, Yunlai Xue, Daoke Yao, Yifan Hu, Ankang Wu, Miao Dai, Hang Ye

“…Our study aimed to construct a machine learning algorithm predictive model to predict the risk of fungal infection following F-URL. …”
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Predictive performance of risk prediction models for lung cancer incidence in Western and Asian countries: a systematic review and meta-analysis by Yah Ru Juang, Lina Ang, Wei Jie Seow

“…Abstract Numerous prediction models have been developed to identify high-risk individuals for lung cancer screening, with the aim of improving early detection and survival rates. …”
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A machine learning-based screening model for the early detection of prostate cancer developed using serum microRNA data from a mixed cohort of 8,741 participants by Cong Lai, Zhensheng Hu, Zhuohang Li, Zhikai Wu, Kuiqing Li, Lin Li, Hongze Liu, Juanyi Shi, Yi Zhou, Kewei Xu, Cheng Liu

“…Six machine learning algorithms were employed to develop a screening model for PCa using the training dataset. …”
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Prediction and Screening of Lead-Free Double Perovskite Photovoltaic Materials Based on Machine Learning by Juan Wang, Yizhe Wang, Xiaoqin Liu, Xinzhong Wang

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