Predicting drug-target interactions using machine learning with improved data balancing and feature engineering by Md. Alamin Talukder, Mohsin Kazi, Ammar Alazab

“…This study makes several contributions to address these issues, introducing a novel hybrid framework that combines advanced machine learning (ML) and deep learning (DL) techniques. The framework leverages comprehensive feature engineering, utilizing MACCS keys to extract structural drug features and amino acid/dipeptide compositions to represent target biomolecular properties. …”
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Highlighting the Best English Teaching Method for Katsina State Secondary Schools: Communicative Versus Traditional by Ibrahim Sani, Abdulhakim Saidu

“…This study was undertaken to highlight the best English teaching method in Katsina state secondary schools by comparing communicative method (CLT) against traditional method (GTM)  to ascertain the best approach for teaching Grammar, vocabulary, written composition, oral composition, and oral English. …”
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Machine Learning Monte Carlo Approaches and Statistical Physics Notions to Characterize Bacterial Species in Human Microbiota by Michele Bellingeri, Leonardo Mancabelli, Christian Milani, Gabriele Andrea Lugli, Roberto Alfieri, Massimiliano Turchetto, Marco Ventura, Davide Cassi

“…Recent studies have shown correlations between the microbiota’s composition and various health conditions. Machine learning (ML) techniques are essential for analyzing complex biological data, particularly in microbiome research. …”
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Novel machine learning driven design strategy for high strength Zn Alloys optimization with multiple constraints by Chenfeng Pan, Wenwen Lin, Jianxing Zhou, Wei Jian, Ka Chun Chan, Yuk Lun Chan, Lu Ren

“…Interpretability analysis of the models was performed using the SHAP method with particle swarm optimization (PSO). Furthermore, a ML-based Zn alloy composition design system (ZACDS) was proposed by integrating the Bayesian optimization algorithm. …”
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Predicting the Tensile Properties of Automotive Steels at Intermediate Strain Rates via Interpretable Ensemble Machine Learning by Houchao Wang, Fengyao Lv, Zhenfei Zhan, Hailong Zhao, Jie Li, Kangte Yang

“…Most importantly, the Shapley additive explanation (SHAP)-based method reveals major features that significantly affect tensile properties at intermediate strain rates. …”
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Optimal design of high‐performance rare‐earth‐free wrought magnesium alloys using machine learning by Shaojie Li, Zaixing Dong, Jianfeng Jin, Hucheng Pan, Zongqing Hu, Rui Hou, Gaowu Qin

“…The ML algorithms, including support vector machine regression (SVR), artificial neural network, and other three methods, are employed, and the SVR has the best performance in predicting mechanical properties based on the components, and process parameters, with the mean absolute percentage error of YS, UTS, and EL being 6.34%, 4.19%, and 13.64% in the test set, respectively. …”
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Developing predictive models for tocilizumab response in rheumatoid arthritis: a gene expression and machine learning approaches by Weiya Lan, Wei Du, Wukai Ma, Xueming Yao, Zong Jiang, Jing Zhou, Changming Chen, Chunxia Wang, Fang Tang

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A systematic review on machine learning-aided design of engineered biochar for soil and water contaminant removal by Yunpeng Ge, Kaiyang Ying, Guo Yu, Muhammad Ubaid Ali, Abubakr M. Idris, Abubakr M. Idris, Asfandyar Shahab, Habib Ullah, Habib Ullah

“…The design and application of engineered biochar is crucial for removing contaminants from soil and water,yet its development and commercialization still depend on time- and labor-intensive experimental methods. Machine learning (ML) offers a faster alternative, but despite its growing use in biochar research, no review systematically covers ML-driven design of engineered biochar for large-scale contaminant removal. …”
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Unveiling new insights into migraine risk stratification using machine learning models of adjustable risk factors by Yu-Chen Liu, Ye-Hai Liu, Hai-Feng Pan, Wei Wang

“…Second, we trained ensemble machine learning (ML) algorithms that incorporated these factors, with Shapley Additive exPlanations (SHAP) value analysis quantifying predictor importance. …”
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Optimizing Boride Coating Thickness on Steel Surfaces Through Machine Learning: Development, Validation, and Experimental Insights by Selim Demirci, Durmuş Özkan Şahin, Sercan Demirci, Armağan Gümüş, Mehmet Masum Tünçay

“…In this study, a comprehensive machine learning (ML) model was developed to predict and optimize boride coating thickness on steel surfaces based on boriding parameters such as temperature, time, boriding media, method, and alloy composition. …”
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Improved estimation of two-phase capillary pressure with nuclear magnetic resonance measurements via machine learning by Oriyomi Raheem, Misael M. Morales, Wen Pan, Carlos Torres-Verdín

“…In this study, we introduce rock classification techniques and implement a data-driven machine learning (ML) method to estimate saturation-dependent capillary pressure from core petrophysical properties. …”
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Efficiently charting the space of mixed vacancy-ordered perovskites by machine-learning encoded atomic-site information by Fan Zhang, Li Fu, Weiwei Gao, Peihong Zhang, Jijun Zhao

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PDCNet: A Polarimetric Data-Enhanced Contrastive Learning Network for PolSAR Land Cover Classification by Bo Ren, Chaoyue Hua, Biao Hou, Jian Lv, Chen Yang, Licheng Jiao, Jocelyn Chanussot

“…The design process for polarimetric contrastive learning involves the construction of positive samples, the establishment of a PolSAR-based network architecture for contrastive learning, and the formulation of the loss function. …”
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Multiobjective optimization of dielectric, thermal, and mechanical properties of inorganic glasses utilizing explainable machine learning and genetic algorithm by Jincheng Qin, Faqiang Zhang, Mingsheng Ma, Yongxiang Li, Zhifu Liu

“…This study developed machine learning models to predict permittivity, dielectric loss, thermal conductivity, coefficient of thermal expansion, and Young’s modulus based on the composition features of inorganic glasses. …”
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Development of a deep learning predictive model for estimating higher heating value in municipal solid waste management by Nasreen Banu Mohamed Ishaque, S. Metilda Florence

“…In this work, a novel deep learning-based framework called DLHHV-MSW is presented it estimates the HHV of MSW from its elemental composition, such as the amount of ash, carbon, hydrogen, nitrogen, oxygen, sulfur, and water. …”
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Quantitative evaluation of brittleness of deep shale gas reservoirs of Wufeng- Longmaxi formations in Lintanchang area, southeastern Sichuan Basin by Shaoke FENG, Liang XIONG, Shuai YIN, Xiaoxia DONG, Limin WEI

“…The fracture toughness of shale samples was closely related to the content of brittle minerals, and the fracture toughness values of type Ⅰ and type Ⅱ samples with laminations perpendicular to bedding planes were relatively lower. Based on the shale characteristics of mineral composition, triaxial rock mechanics, and fracture toughness, a deep learning weight analysis model was developed using brittleness indices Bel and Bmine3 and fracture toughness index IKIC as data inputs.The cumulative risk value was less than 5, indicating the high reliability of the model.A comprehensive brittleness index B was established based on the model, and its correlation with the measured brittleness index BS of core samples was significantly improved (R=0.852 7). …”
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Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning by Jakub Horvath, Pavel Jedlicka, Marie Kratka, Zdenek Kubat, Eduard Kejnovsky, Matej Lexa

“…Results We used machine learning methods suitable for DNA sequence classification and applied them to a large dataset of plant LTR retrotransposon sequences. …”
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Noise Pollution Prediction in a Densely Populated City Using a Spatio-Temporal Deep Learning Approach by Marc Semper, Manuel Curado, Jose Luis Oliver, Jose F. Vicent

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Machine learning integrates region-specific microbial signatures to distinguish geographically adjacent populations within a province by Li Luo, Li Luo, Bangwei Chen, Bangwei Chen, Shengyin Zeng, Shengyin Zeng, Yaxin Li, Yaxin Li, Xiaolin Chen, Xiaolin Chen, Jianguo Zhang, Xiangjie Guo, Shujin Li, Lei Ruan, Shida Zhu, Cairong Gao, Cuntai Zhang, Tao Li

“…To obtain the optimal model that can distinguish geographically close populations, three machine learning (ML) algorithms based on microbiota or functions were employed.ResultsSignificant differences in microbial α diversity and β diversity were observed. …”
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Machine learning prediction of obesity-associated gut microbiota: identifying Bifidobacterium pseudocatenulatum as a potential therapeutic target by Hao Wu, Yuan Li, Yuxuan Jiang, Xinran Li, Shenglan Wang, Changle Zhao, Changle Zhao, Ximiao Yang, Baocheng Chang, Juhong Yang, Juhong Yang, Jianjun Qiao, Jianjun Qiao

“…BackgroundThe rising prevalence of obesity and related metabolic disorders highlights the urgent need for innovative research approaches. Utilizing machine learning (ML) algorithms to predict obesity-associated gut microbiota and validating their efficacy with specific bacterial strains could significantly enhance obesity management strategies.MethodsWe leveraged gut microbiome data from 1,563 healthy individuals and 2,043 overweight patients sourced from the GMrepo database. …”
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