LatentDE: latent-based directed evolution for protein sequence design by Thanh V T Tran, Nhat Khang Ngo, Viet Thanh Duy Nguyen, Truong-Son Hy

“…To mitigate this extensive procedure, recent advancements in machine learning-guided methodologies center around the establishment of a surrogate sequence-function model. In this paper, we propose latent-based DE (LDE), an evolutionary algorithm designed to prioritize the exploration of high-fitness mutants in the latent space. …”
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Autoimmune gastritis detection from preprocessed endoscopy images using deep transfer learning and moth flame optimization by Fadiyah M. Almutairi, Sara A. Althubiti, Shabnam Mohamed Aslam, Habib Dhahri, Omar Alhajlah, Nitin Mittal

“…Various stages in the DL tool comprise; (i) Image collection and resizing, (ii) image pre-processing using Entropy-function and Moth-Flame (MF) Algorithm, (iii) deep-features extraction using a chosen DL-model, (iv) feature optimization using MF algorithm and serial features concatenation, and (iv) classification and performance confirmation using five-fold cross-validation. …”
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Integrating machine learning and multi-omics analysis to unveil key programmed cell death patterns and immunotherapy targets in kidney renal clear cell carcinoma by Fanyan Ou, Yi Pan, Qiuli Chen, Lixiong Zeng, Kanglai Wei, Delin Liu, Qian Guo, Liquan Zhou, Jie Yang

“…We utilized a combination of 101 machine learning algorithms to analyze the TCGA-KIRC cohort and the GSE22541 KIRC patients, screening for cell death patterns closely associated with prognosis from 18 potential modes. …”
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Nomogram for predicting the severity of high-risk plaques in acute coronary syndrome by Miao-Na Bai, Miao-Na Bai, Miao-Na Bai, Ji-Xiang Wang, Ji-Xiang Wang, Xiao-Wei Li, Xiao-Wei Li, Xiao-Wei Li, Jing-Xian Wang, Jing-Xian Wang, Yu-Hang Wang, Yu-Hang Wang, Yin Liu, Yin Liu, Yin Liu, Jing Gao, Jing Gao, Jing Gao, Jing Gao

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Enhancing Daylight and Energy Efficiency in Hot Climate Regions with a Perforated Shading System Using a Hybrid Approach Considering Different Case Studies by Basma Gaber, Changhong Zhan, Xueying Han, Mohamed Omar, Guanghao Li

“…A hybrid approach integrating parametric modeling, machine learning, multi-criteria decision-making (MCDM), and genetic algorithm (GA) is used to optimize the design incorporating architects’ preferences. …”
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Early gestational diabetes mellitus risk predictor using neural network with NearMiss by Min Zhao, Xiaojie Su, Lihong Huang

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Sparse Temporal Data-Driven SSA-CNN-LSTM-Based Fault Prediction of Electromechanical Equipment in Rail Transit Stations by Jing Xiong, Youchao Sun, Junzhou Sun, Yongbing Wan, Gang Yu

“…The experiments showed that the proposed prediction method improved the RMSE by 0.000699, the MAE by 0.00042, and the R2 index by 0.109779 when predicting the fault rate data of platform screen doors on all of the lines. When predicting the fault rate data of the screen doors on a single line, the performance of the model was better than that of the CNN-LSTM model optimized with the PSO algorithm.…”
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Identification of ALDH2 as a novel target for the treatment of acute kidney injury in kidney transplantation based on WGCNA and machine learning algorithms and exploration of its p... by Jinpu Peng, Shili Wang, Xingyu Pan, Moudong Wu, Xiong Zhan, Dan Wang, Guohua Zhu, Wei Wang, Hongyu Tang, Nini An, Jun Pei

“…Next, we constructed a rat renal IRI model and explored the role of key genes in renal IRI. …”
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Examining the empathy levels of medical students using CHAID analysis by Nesrin Hark Söylemez

“…Methods The study was conducted with 322 medical students from a public university in Turkey. A relational screening model was applied, using a “Personal Information Form” and an “Empathy Scale” to gather data. …”
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Exploring shared pathogenic mechanisms and biomarkers in hepatic fibrosis and inflammatory bowel disease through bioinformatics and machine learning by Shangkun Li, Haoyu Li, Mingran Qi

“…The key diagnostic biomarkers were determined via a protein-protein interaction (PPI) network combined with two machine learning algorithms. The logistic regression model was subsequently developed based on these key genes. …”
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A Novel Strategy Coupling Optimised Sampling with Heterogeneous Ensemble Machine-Learning to Predict Landslide Susceptibility by Yongxing Lu, Honggen Xu, Can Wang, Guanxi Yan, Zhitao Huo, Zuwu Peng, Bo Liu, Chong Xu

“…The stacking ensemble machine-learning model outperformed those three baseline models. Notably, the accuracy of the hybrid OS–Stacking model is most promising, up to 97.1%. …”
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Molecular biomarkers in salivary diagnostic materials: Point-of-Care solutions — PoC-Diagnostics and -Testing by Ziyad S. Haidar

“…Recent advancements in nanomaterials and fabrication techniques, coupled with emerging computational approaches such as artificial intelligence (AI), machine learning, and deep learning, have revolutionized high-throughput screening and laboratory automation. AI-driven algorithms now process and analyze salivary proteomic data with remarkable accuracy, identifying patterns and biomarkers associated with diseases such as oral cancer at an early stage. …”
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Short-term Wind Power Forecasting Based on BWO‒VMD and TCN‒BiGRU by LU Jing, ZHANG Yanru, WANG Rui

“…Given the instability and high volatility of wind power generation, this study proposes a short-term wind power prediction method based on BWO‒VMD and TCN‒BiGRU to improve the accuracy of wind power prediction and better support the energy transition under the “dual carbon” strategy.MethodsA short-term wind power generation prediction model based on the beluga whale optimization (BWO) algorithm, variational mode de-composition (VMD), temporal convolutional network (TCN), and bidirectional gated recurrent unit (BiGRU) was carefully proposed to improve the prediction accuracy of wind power generation, particularly considering its inherent instability and high volatility. …”
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A negative combined effect of exposure to maternal Mn-Cu-Rb-Fe metal mixtures on gestational anemia, and the mediating role of creatinine in the Guangxi Birth Cohort Study (GBCS):... by Yuen Zhong, Yu Bao, Hong Cheng, Chaoqun Liu, Shengzhu Huang, Hualong Qiu, Honglin Huang, Jiajun Ren, Hailiu Jin, Caitong He, Long Tian, Yu Zhang, Bangzhu Luo, Tao Liang, Mujun Li, Zengnan Mo, Longman Li, Xiaobo Yang

“…We utilized twelve machine learning (ML) algorithms to independently screen for effective metal mixtures, assess their combined impacts and dose-response relationships on gestational anemia, and estimate the mediating role of kidney function. …”
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Remote Sensing for Urban Biodiversity: A Review and Meta-Analysis by Michele Finizio, Federica Pontieri, Chiara Bottaro, Mirko Di Febbraro, Michele Innangi, Giovanna Sona, Maria Laura Carranza

“…Our analysis incorporated technical (e.g., sensor, platform, algorithm), geographic (e.g., country, city extent, population) and ecological (biodiversity target, organization level, biome) meta-data, examining their frequencies, temporal trends (Generalized Linear Model—GLM), and covariations (Cramer’s V). …”
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Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth by Rui Zhou, Ziqian Liu, Tongtong Wu, Xianwei Pan, Tongtong Li, Kaiting Miao, Yuru Li, Xiaohui Hu, Haigang Wu, Andrew M. Hemmings, Beier Jiang, Zhenzhen Zhang, Ning Liu

“…Identification of new selective EGFR-T790M inhibitors has proven challenging through traditional screening platforms. With great advances in computer algorithms, machine learning improved the screening rates of molecules at full chemical spaces, and these molecules will present higher biological activity and targeting efficiency. …”
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Oxidative stress-related genes in uveal melanoma: the role of CALM1 in modulating oxidative stress and apoptosis and its prognostic significance by Yue Wu, Xiaoyan Cai, Menghan Hu, Runyan Cao, Yong Wang

“…Protein–protein interaction (PPI) networks were constructed to identify hub genes, and machine learning algorithms were utilized to screen for diagnostic genes, employing methods such as least absolute shrinkage and selection operator (LASSO) regression, random forest, support vector machine (SVM), gradient boosting machine (GBM), neural network algorithm (NNET), and eXtreme gradient boosting (XGBoost). …”
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Civil Aircraft Landing Attitude Ultra-Limit Warning System Based on mRMR-LSTM by Fei Lu, Tong Jing, Chunsheng Xie, Haonan Chen

“…Then, the Max-Relevance and Min-Redundancy algorithm was applied to screen the QAR (Quick Access Recorder) parameters with the highest correlation with the predictor variables, and the LSTM network model was established to predict the pitch and roll angles of the aircraft landing, respectively. …”
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A Small-Sample Scenario Optimization Scheduling Method Based on Multidimensional Data Expansion by Yaoxian Liu, Kaixin Zhang, Yue Sun, Jingwen Chen, Junshuo Chen

“…Firstly, based on spatial correlation, the daily power curves of PV power plants with measured power are screened, and the meteorological similarity is calculated using multicore maximum mean difference (MK-MMD) to generate new energy output historical data of the target distributed PV system through the capacity conversion method; secondly, based on the existing daily load data of different types, the load historical data are generated using the stochastic and simultaneous sampling methods to construct the full historical dataset; subsequently, for the sample imbalance problem in the small-sample scenario, an oversampling method is used to enhance the data for the scarce samples, and the XGBoost PV output prediction model is established; finally, the optimal scheduling model is transformed into a Markovian decision-making process, which is solved by using the Deep Deterministic Policy Gradient (DDPG) algorithm. …”
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DIPOLE ANTENNAS WITH A SECTOR-SHAPED RADIATION PATTERN by M. M. Gorobets, N. P. Yeliseyeva, S. L. Berdnyk, O. M. Horobets

“…Results. The algorithms and calculation programs developed allow studying the electrodynamic characteristics of the antenna over a wide range of screen electrical dimensions and distances between the dipole and the screen. …”
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