Symmetry-invariant quantum machine learning force fields by Isabel Nha Minh Le, Oriel Kiss, Julian Schuhmacher, Ivano Tavernelli, Francesco Tacchino

Subjects: “…molecular force fields…”
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Multiple machine learning-based integrations of multi-omics data to identify molecular subtypes and construct a prognostic model for HNSCC by Xiaoqin Luo, Chao Li, Gang Qin

“…Conclusion Our study delineates two molecular subtypes of HNSCC and establishes a robust prognostic model using multi-omics data and machine learning. …”
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RETRACTED ARTICLE: A prospective diagnostic model for breast cancer utilizing machine learning to examine the molecular immune infiltrate in HSPB6 by Lizhe Wang, Yu Wang, Yueyang Li, Li Zhou, Sihan Liu, Yongyi Cao, Yuzhi Li, Shenting Liu, Jiahui Du, Jin Wang, Ting Zhu

“…Conclusion Our findings have identified compelling molecular targets and distinctive diagnostic markers for the clinical management of breast cancer. …”
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Retraction Note: A prospective diagnostic model for breast cancer utilizing machine learning to examine the molecular immune infiltrate in HSPB6 by Lizhe Wang, Yu Wang, Yueyang Li, Li Zhou, Sihan Liu, Yongyi Cao, Yuzhi Li, Shenting Liu, Jiahui Du, Jin Wang, Ting Zhu

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Structural bioinformatics for rational drug design by Soroush Mozaffari, Agnethe Moen, Che Yee Ng, Gerry A.F. Nicolaes, Kanin Wichapong

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A comprehensive analysis of molecular characteristics of hot and cold tumor of gastric cancer by Chenxi Lv, Tianwei Chen, Jiangtao Li, Yuqiang Shan, Hong Zhou

“…However, a detailed molecular characterization of the tumor immune microenvironment in GC is essential to further optimize these therapies and enhance their efficacy. …”
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Bio-primed machine learning to enhance discovery of relevant biomarkers by David M. Henke, Alexander Renwick, Joseph R. Zoeller, Jitendra K. Meena, Nicholas J. Neill, Elizabeth A. Bowling, Kristen L. Meerbrey, Thomas F. Westbrook, Lukas M. Simon

“…The advent of high-throughput technologies presents unprecedented opportunities to explore molecular disease mechanisms but also challenges due to high dimensionality and collinearity among features. …”
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Using computer modeling to find new LRRK2 inhibitors for parkinson’s disease by María C. García, Sebastián A. Cuesta, José R. Mora, Jose L. Paz, Yovani Marrero-Ponce, Frank Alexis, Edgar A. Márquez

“…The DrugBank screening resulted in three promising drugs—triamterene, phenazopyridine, and CRA_1801—with predicted pIC50 values greater than 7, warranting further investigation as novel PD treatments. Molecular docking and molecular dynamics simulations were performed to provide a comprehensive understanding of the interactions between LRRK2 and the inhibitors in the data set and best molecules of the screening. …”
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Unlocking biological complexity: the role of machine learning in integrative multi-omics by Ravindra Kumar, Rajrani Ruhel, Andre J. van Wijnen

“…Machine learning has emerged as a powerful tool to help and resolve these challenges. …”
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Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applications by Aditya Venkatraman, Mark A. Wilson, David Montes de Oca Zapiain

“…Abstract Molecular Dynamics (MD) simulations are used to understand the effects of corrosion on metallic materials in salt brine. …”
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Machine learning integrated with in vitro experiments for study of drug release from PLGA nanoparticles by Yu Sun, Shuhuai Qin, Yingli Li, Naimul Hasan, Yan Vivian Li, Jiangguo Liu

“…The focus is to understand the effect of drug solubility, drug molecular weight, particle size, and pH-value of the release matrix/environment on drug release profiles. …”
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Development of an mPBPK machine learning framework for early target pharmacology assessment of biotherapeutics by Krutika Patidar, Nikhil Pillai, Saroj Dhakal, Lindsay B. Avery, Panteleimon D. Mavroudis

“…In the present work, we propose a machine learning-based target pharmacology assessment framework that utilizes minimal physiologically based pharmacokinetic (mPBPK) modeling and machine learning (ML) to infer optimal physicochemical properties of antibodies and their targets. …”
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Personalized prediction of anticancer potential of non-oncology drugs through learning from genome derived molecular pathways by Xiaobao Dong, Huanhuan Liu, Ting Tong, Liuxing Wu, Jianhua Wang, Tianyi You, Yongjian Wei, Xianfu Yi, Hongxi Yang, Jie Hu, Haitao Wang, Xiaoyan Wang, Mulin Jun Li

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Accelerating biopharmaceutical cell line selection with label-free multimodal nonlinear optical microscopy and machine learning by Jindou Shi, Alexander Ho, Corey E. Snyder, Eric J. Chaney, Janet E. Sorrells, Aneesh Alex, Remben Talaban, Darold R. Spillman, Marina Marjanovic, Minh Doan, Gary Finka, Steve R. Hood, Stephen A. Boppart

“…A machine learning (ML)-assisted analysis pipeline leveraged high-dimensional information to classify single cells into their respective lines. …”
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Multi-omics and machine learning-driven CD8+ T cell heterogeneity score for head and neck squamous cell carcinoma by Di He, Zhan Yang, Tian Zhang, Yaxian Luo, Lianjie Peng, Jiatao Yan, Tao Qiu, Jingyu Zhang, Luying Qin, Zhichao Liu, Mouyuan Sun

“…Moreover, drug sensitivity analysis and molecular docking studies have indicated that simvastatin and pazopanib are potential inhibitors of OLR1. …”
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Machine Learning Diagnostic Model for Hepatocellular Carcinoma Based on Liquid–Liquid Phase Separation and Ferroptosis-Related Genes by Wenchao Chen, Ting Zhu, Xiaofan Pu, Linlin Zhao, Senhao Zhou, Xin Zhong, Suihan Wang, Tianyu Lin

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Development of machine learning models for predicting non-remission in early RA highlights the robust predictive importance of the RAID score-evidence from the ARCTIC study by Gaoyang Li, Shrikant S. Kolan, Franco Grimolizzi, Joseph Sexton, Giulia Malachin, Guro Goll, Tore K. Kvien, Tore K. Kvien, Nina Paulshus Sundlisæter, Manuela Zucknick, Siri Lillegraven, Espen A. Haavardsholm, Espen A. Haavardsholm, Bjørn Steen Skålhegg

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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

“…These genes were analyzed using univariate COX and machine learning algorithms to develop a prognostic scoring algorithm. …”
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