Structure- and machine learning-guided engineering demonstrate that a non-canonical disulfide in an anti-PD-1 rabbit antibody does not impede antibody developability by Wei-Ching Liang, Hongkang Xi, Dawei Sun, Luigi D’Ascenzo, Jonathan Zarzar, Nicole Stephens, Ryan Cook, Yinyin Li, Zhengmao Ye, Marissa Matsumoto, Jian Payandeh, Matthieu Masureel, Yan Wu

“…Next, and prompted by recent developments in machine learning (ML)-guided protein engineering, we used an unbiased ML- and structure-guided approach to rapidly and efficiently generate a different variant with recovered affinity. …”
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Data-Driven Approach for the Prediction of In Situ Gas Content of Deep Coalbed Methane Reservoirs Using Machine Learning: Insights from Well Logging Data by Qian Zhang, Shuheng Tang, Songhang Zhang, Zhaodong Xi, Tengfei Jia, Xiongxiong Yang, Donglin Lin, Wenfu Yang

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'Medical Minds and Machine Learning': Awareness and Opinions on Artificial Intelligence in Healthcare among Undergraduate Medical Students of a Tertiary Care Institute of Kolkata, India by Pattanayak Shalini, Basu Mausumi, Sinha Debasish, Kerketta Prince

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Analisis Sentimen untuk Evaluasi Reputasi Merek Motor XYZ Berkaitan dengan Isu Rangka Motor di Twitter Menggunakan Pendekatan Machine Learning by Ferdian Maulana Akbar, Robby Hermansyah, Sofian Lusa, Dana Indra Sensuse, Nadya Safitri, Damayanti Elisabeth

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RETRACTED ARTICLE: Single-cell omics and machine learning integration to develop a polyamine metabolism-based risk score model in breast cancer patients by Xiliang Zhang, Hanjie Guo, Xiaolong Li, Wei Tao, Xiaoqing Ma, Yuxing Zhang, Weidong Xiao

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Machine Learning and Multi-Omics Integration to Reveal Biomarkers and Microbial Community Assembly Differences in Abnormal Stacking Fermentation of Sauce-Flavor <i>Baijiu</i> by Shuai Li, Yueran Han, Ming Yan, Shuyi Qiu, Jun Lu

Subjects: “…machine learning…”
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Global trends and research frontiers on machine learning in sustainable animal production in times of climate change: Bibliometric analysis aimed at insights and orientations for the coming decades by Robson Mateus Freitas Silveira, Concepta Mcmanus, Iran José Oliveira da Siva

“…The present pioneering review provides a longitudinal perspective on the current state of academic research in the emerging machine learning field linked to sustainable animal production in times of climate change. …”
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A Step Forward in Identifying Socially Desirable Respondents: An Integrated Machine Learning Model Considering T-Scores, Response Time, Kinematic Indicators, and Eye Movements by Cristina Mazza, Irene Ceccato, Loreta Cannito, Merylin Monaro, Eleonora Ricci, Emanuela Bartolini, Alessandra Cardinale, Adolfo Di Crosta, Matteo Cardaioli, Pasquale La Malva, Marco Colasanti, Renata Tambelli, Luciano Giromini, Rocco Palumbo, Riccardo Palumbo, Alberto Di Domenico, Paolo Roma

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Development and validation of machine-learning models for predicting the risk of hypertriglyceridemia in critically ill patients receiving propofol sedation using retrospective data: a protocol by Jiawen Deng, Hemang Yadav, Kiyan Heybati

“…Early identification of patients at risk for propofol-associated hypertriglyceridemia is crucial for optimising sedation strategies and preventing adverse outcomes. Machine-learning (ML) models offer a promising approach for predicting individualised patient risks of propofol-associated hypertriglyceridemia.Methods and analysis We propose the development of an ML model aimed at predicting the risk of propofol-associated hypertriglyceridemia in ICU patients receiving IMV. …”
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A machine learning framework for short-term prediction of chronic obstructive pulmonary disease exacerbations using personal air quality monitors and lifestyle data by M. Atzeni, G. Cappon, J. K. Quint, F. Kelly, B. Barratt, M. Vettoretti

“…To address this, we designed a machine learning (ML) framework that leverages data from personal air quality monitors, health records, lifestyle, and living condition information to build models that perform short-term prediction of COPD exacerbations. …”
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Automated post-run analysis of arrayed quantitative PCR amplification curves using machine learning [version 1; peer review: awaiting peer review] by David Garrett Brown, Darwin J. Operario, Lan Wang, Shanrui Wu, Daniel T. Leung, Eric R. Houpt, James A. Platts-Mills, Jie Liu, Ben J. Brintz

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A Novel Modeling Technique for the Forecasting of Multiple-Asset Trading Volumes: Innovative Initial-Value-Problem Differential Equation Algorithms for Reinforcement Machine Learning by Mazin A. M. Al Janabi

“…In addition, they can be applied to artificial intelligence and machine learning for the policymaking process, reinforcement machine learning techniques for the Internet of Things (IoT) data analytics, expert systems in finance, FinTech, and within big data ecosystems.…”
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A hybrid approach to analyzing factors influencing international student retention in Turkish higher education: integrating fuzzy DEMATEL, machine learning, and statistical methods by Serra Usta Ergun

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Letter to the editor regarding ‘Correlation between neutrophil-to-lymphocyte ratio and contrast-induced acute kidney injury and the establishment of machine-learning-based predictive models’ by Ning Zhuo, Gang Wang, Gang Wu

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Correcting forest aboveground biomass biases by incorporating independent canopy height retrieval with conventional machine learning models using GEDI and ICESat-2 data by Biao Zhang, Zhichao Wang, Tiantian Ma, Zhihao Wang, Hao Li, Wenxu Ji, Mingyang He, Ao Jiao, Zhongke Feng

“…Spaceborne LiDAR satellites, including GEDI and ICESat-2, have shown significant potential in estimating aboveground biomass (AGB) using machine learning (ML) methods. In contrast to advances focused on the refinement of ML algorithms, this study aims to enhance AGB estimation accuracy by integrating an additional Canopy Height (CH) information. …”
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Development and validation of a machine learning-based prediction model for hepatorenal syndrome in liver cirrhosis patients using MIMIC-IV and eICU databases by Fengwei Yao, Ji Luo, Qian Zhou, Luhua Wang, Zhijun He

“…By integrating the MIMIC-IV database and machine learning algorithms, we developed an effective predictive model for HRS in liver cirrhosis patients, providing a robust tool for early clinical intervention.…”
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Machine learning based predictive model and genetic mutation landscape for high-grade colorectal neuroendocrine carcinoma: a SEER database analysis with external validation by Ruixin Wu, Ruixin Wu, Sihao Chen, Sihao Chen, Yi He, Yi He, Ya Li, Song Mu, Aishun Jin, Aishun Jin

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Diagnosis-Based Hybridization of Multimedical Tests and Sociodemographic Characteristics of Autism Spectrum Disorder Using Artificial Intelligence and Machine Learning Techniques: A Systematic Review by M. E. Alqaysi, A. S. Albahri, Rula A. Hamid

“…It is challenging to discover autism in the early stages of life, which prompted researchers to intensify efforts to reach the best solutions to treat this challenge by introducing artificial intelligence (AI) techniques and machine learning (ML) algorithms, which played an essential role in greatly assisting the medical and healthcare staff and trying to obtain the highest predictive results for autism spectrum disorder. …”
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Machine Learning-Devised Immune-Related lncRNA Signature Panel Predicts the Prognosis and Immune Landscape in Breast Cancer Novel IRLP Signature in BRCA by Jun-Yu Zhu, An-Qi Lyu, Zhang-Ting Wang, Wai-Yee Chan, Tao Qin, Kai-Kei Miu, He-Rui Yao

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Mapping Susceptibility and Risk of Land Subsidence by Integrating InSAR and Hybrid Machine Learning Models: A Case Study in Xi&#x0027;an, China by Chen Chen, Mimi Peng, Mahdi Motagh, Xinxin Guo, Mengdao Xing, Yinghui Quan

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