SnugDock: paratope structural optimization during antibody-antigen docking compensates for errors in antibody homology models. by Aroop Sircar, Jeffrey J Gray

“…The accuracy of SnugDock predictions suggests a new genre of general docking algorithms with flexible binding interfaces targeted towards making homology models useful for further high-resolution predictions.…”
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The importance of precise and suitable descriptors in data‐driven approach to boost development of lithium batteries: A perspective by Zehua Wang, Li Wang, Hao Zhang, Hong Xu, Xiangming He

“…It does so by providing examples, summarizing current descriptors and ML algorithms, and examining the potential implications of future AI advancements for the sustainable energy industry.…”
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Comparison of Deep Learning Techniques in Detection of Sickle Cell Disease. by Mabirizi, Vicent, Kawuma, Simon, Kyarisiima, Addah, Bamutura, David, Atwiine, Barnabas, Nanjebe, Deborah, Oyesigye, Adolf Mukama

“…This is evidenced by some models and algorithms with ≥90% prediction accuracy. From the literature, most of the proposed methods are trained and tested on pre-trained deep learning models like VGG16, VGG19, ResNet, Inception_V3, and ReNet. …”
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MORIX: Machine learning-aided framework for lethality detection and MORtality inference with eXplainable artificial intelligence in MAFLD subjects by Domenico Lofù, Paolo Sorino, Tommaso Colafiglio, Caterina Bonfiglio, Rossella Donghia, Gianluigi Giannelli, Angela Lombardi, Tommaso Di Noia, Eugenio Di Sciascio, Fedelucio Narducci

“…Moreover, a web application was developed to predict mortality risk and provide explanations of how the input features influenced the final prediction. …”
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A machine learning model revealed that exosome small RNAs may participate in the development of breast cancer through the chemokine signaling pathway by Jun-luan Mo, Xi Li, Lin Lei, Ji Peng, Xiong-shun Liang, Hong-hao Zhou, Zhao-qian Liu, Wen-xu Hong, Ji-ye Yin

“…Finally, target gene prediction and functional enrichment analysis were performed on these key RNAs. …”
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Federated Reinforcement Learning-Based Dynamic Resource Allocation and Task Scheduling in Edge for IoT Applications by Saroj Mali, Feng Zeng, Deepak Adhikari, Inam Ullah, Mahmoud Ahmad Al-Khasawneh, Osama Alfarraj, Fahad Alblehai

“…This algorithm is compared to DQN, DDQN, Dueling DQN, and Dueling DDQN models using Non-IID EMNIST, IID EMNIST datasets, and with the Crop Prediction dataset. …”
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Unraveling the oxidative stress landscape in diabetic foot ulcers: insights from bulk RNA and single-cell RNA sequencing data by Jialiang Lin, Linjuan Huang, Weiming Li, Haijun Xiao, Mingmang Pan

“…Machine learning algorithms (SVM-RFE, LASSO and RF) identified BCL2 and 和FOXP2 as candidate hub DORGs for DFU diagnosis. …”
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SUDDEN DEATH IN HYPERTROPHIC CARDIOMYOPATHY: SEARCH FOR NEW RISK FACTORS by N. S. Krylova, E. A. Kovalevskaya, N. G. Poteshkina, A. E. Demkina, F. M. Khashieva

“…The issue for prediction of SCD in this pathology does not lose its importance.Aim. …”
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Multiobjective optimization of CO2 injection under geomechanical risk in high water cut oil reservoirs using artificial intelligence approaches by Fankun Meng, Jia Liu, Gang Tong, Hui Zhao, Chengyue Wen, Yuhui Zhou, Vamegh Rasouli, Minou Rabiei

“…Therefore, a hybrid optimization framework was designed that combines artificial intelligence methods (Support Vector Regression with the Gaussian kernel, Gaussian-SVR or Long Short-Term Memory, LSTM) and multi-objective optimization algorithms (multiple objective particle swarm optimization, MOPSO or Non-dominated Sorting Genetic Algorithm II, NSGA-II) to find the optimal CO2 injection and production strategies under different water cut. …”
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A practical guide for nephrologist peer reviewers: evaluating artificial intelligence and machine learning research in nephrology by Yanni Wang, Wisit Cheungpasitporn, Hatem Ali, Jianbo Qing, Charat Thongprayoon, Wisit Kaewput, Karim M. Soliman, Zhengxing Huang, Min Yang, Zhongheng Zhang

“…Artificial intelligence (AI) and machine learning (ML) are transforming nephrology by enhancing diagnosis, risk prediction, and treatment optimization for conditions such as acute kidney injury (AKI) and chronic kidney disease (CKD). …”
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Unraveling shared diagnostic genes and cellular microenvironmental changes in endometriosis and recurrent implantation failure through multi-omics analysis by Dongxu Qin, Yongquan Zheng, Libo Wang, Zhenyi Lin, Yao Yao, Weidong Fei, Caihong Zheng

“…Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify key genes. Machine learning algorithms, including Random Forest (RF) and XGBoost, were utilized to screen for shared diagnostic genes, which were subsequently validated through receiver operating characteristic (ROC) analysis and clinical prediction models. …”
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Color-Sensitive Sensor Array Combined with Machine Learning for Non-Destructive Detection of AFB₁ in Corn Silage by Daqian Wan, Haiqing Tian, Lina Guo, Kai Zhao, Yang Yu, Xinglu Zheng, Haijun Li, Jianying Sun

“…The combined 1st D-PCA-KNN model showed optimal prediction performance, with determination coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mi>p</mi><mn>2</mn></msubsup></mrow></semantics></math></inline-formula> = 0.87), root mean square error (<i>RMSEP</i> = 0.057), and relative prediction deviation (<i>RPD</i> = 2.773). …”
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Effects of missing data imputation methods on univariate blood pressure time series data analysis and forecasting with ARIMA and LSTM by Nicholas Niako, Jesus D. Melgarejo, Gladys E. Maestre, Kristina P. Vatcheva

“…Results All imputation techniques either increased or decreased the data autocorrelation and with this affected the forecasting performance of the ARIMA and LSTM algorithms. The best imputation technique did not guarantee better predictions obtained on the imputed data. …”
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Integrated analysis of single-cell and bulk transcriptomics reveals cellular subtypes and molecular features associated with osteosarcoma prognosis by Feng Liu, Tingting Zhang, Yongqiang Yang, Kailun Wang, Jinlan Wei, Ji-Hua Shi, Dong Zhang, Xia Sheng, Yi Zhang, Jing Zhou, Faming Zhao

“…Multiple machine learning algorithms were applied to develop tumor purity prediction models based on transcriptomic profile of OS. …”
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In-Memory Versus Disk-Based Computing with Random Forest for Stock Analysis: A Comparative Study by Chitra Joshi, Chitrakant Banchorr, Omkaresh Kulkarni, Kirti Wanjale

“…The effectiveness of these frameworks plays a crucial role in determining data processing speed, model training efficiency and predictive accuracy. As data become increasingly large, diverse and fast-moving, conventional processing systems often fall short of the performance required for modern analytics.Objective: This research seeks to thoroughly assess the performance of two prominent big data processing frameworks-Apache Spark (in-memory computing) and MapReduce (disk-based computing)-with a focus on applying random forest algorithms to predict stock prices. …”
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Computational methods and artificial intelligence-based modeling of magnesium alloys: a systematic review of machine learning, deep learning, and data-driven design and optimizatio... by Hanxuan Wang, Raman Kumar, Raman Kumar, Ashutosh Pattanaik, Rajender Kumar, Rajender Kumar, Ali Saeed Owayez Khawaf Aljaberi, Mayada Ahmed Abass

“…The review highlights the extensive application of models, including Artificial Neural Networks, Convolutional Neural Networks, and hybrid frameworks that combine ML with optimization algorithms or physical simulations. These approaches enhance predictions on mechanical properties, microstructural changes, corrosion behavior, and processing results of Mg alloys. …”
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Autism spectrum disorder diagnosis with neural networks by Asude Demir, Seher Arslankaya

“…Autism Spectrum Disorder (ASD) affects the whole life of children and leads their families to seek effective treatment and education. According to the Centres for Disease Control and Prevention, the disorder affects one in every 36 children today. …”
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Landslide and Collapse Susceptibility Analysis in Wenchuan Earthquake-damaged Area Based on Ensemble Learning Methods by DING Jiawei, WANG Xiekang

“…This anomaly indicated the presence of prediction bias in the logistic regression model, potentially ascribable to the limitations of the logistic regression algorithm and the lack of representative data.ConclusionsThe predictive capabilities of the advanced ensemble learning models in assessing landslide and collapse susceptibility in Wenchuan County surpassed those of the two traditional models. …”
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Research on inter-plant weeding control in peanut at LADRC based on IACO-PSO optimization by Xiaolong Zhang, Hongzhen Xu, Jiaodi Liu, Longzhe Quan, Lichang Zhang, Bang Zhang

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The relationship between kinship and foster placement on mental health indicators in children and youth seeking treatment by Shannon L. Stewart, Boden Brock, Jordyn Manis, Aadhiya Vasudeva, Jeffrey W. Poss

“…These assessments include a variety of embedded evidence-informed scales and algorithms to examine the mental health needs, preferences and strengths of these vulnerable children. …”
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