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

“…This study aims to create a detailed dataset to build strong predictive models with various machine learning algorithms. An ensemble modeling approach was employed to screen the DrugBank database, aiming to repurpose approved medications as potential treatments for Parkinson’s disease (PD). …”
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Estimating rare disease prevalence and costs in the USA: a cohort study approach using the Healthcare Cost Institute claims data by Keith A Crandall, Christine M Cutillo, Ainslie Tisdale, Mahdi Baghbanzadeh, Reva L Stidd, Manpreet S Khural, Laurie J Hartman, Jeff Greenberg, Kevin B Zhang, Ali Rahnavard

“…Building capabilities to use machine learning to accelerate the diagnosis of RDs would vastly improve with changes to healthcare data, such as standardising data input, linking databases, addressing privacy issues and assigning ICD-10 codes for all RDs, resulting in more robust data for RD analytics.…”
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Coordinated conformational changes in P450 decarboxylases enable hydrocarbons production from renewable feedstocks by Wesley Cardoso Generoso, Alana Helen Santana Alvarenga, Isabelle Taira Simões, Renan Yuji Miyamoto, Ricardo Rodrigues de Melo, Ederson Paulo Xavier Guilherme, Fernanda Mandelli, Clelton Aparecido Santos, Rafaela Prata, Camila Ramos dos Santos, Felippe Mariano Colombari, Mariana Abrahão Bueno Morais, Rodrigo Pimentel Fernandes, Gabriela Felix Persinoti, Mario Tyago Murakami, Leticia Maria Zanphorlin

“…Combining X-ray crystallography, molecular dynamics simulations, and machine learning, we have identified intricate molecular rearrangements within the active site that enable the Cβ atom of the substrate to approach the heme iron, thereby promoting oleate decarboxylation. …”
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Improving explainability of post-separation suicide attempt prediction models for transitioning service members: insights from the Army Study to Assess Risk and Resilience in Servi... by Emily R. Edwards, Joseph C. Geraci, Sarah M. Gildea, Claire Houtsma, Jacob A. Holdcraft, Chris J. Kennedy, Andrew J. King, Alex Luedtke, Brian P. Marx, James A. Naifeh, Nancy A. Sampson, Murray B. Stein, Robert J. Ursano, Ronald C. Kessler

“…As universal prevention programs have been unable to resolve this problem, a previously reported machine learning model was developed using pre-separation predictors to target high-risk transitioning service members (TSMs) for more intensive interventions. …”
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Advancing soil-structure interaction (SSI): a comprehensive review of current practices, challenges, and future directions by Imtiyaz Akbar Najar, Raudhah Ahmadi, Akeem Gbenga Amuda, Raghad Mourad, Neveen El Bendary, Idawati Ismail, Nabilah Abu Bakar, Shanshan Tang

“…Additionally, the review discusses recent innovations, including the application of machine learning and advanced computational tools, and their potential to enhance the accuracy and efficiency of SSI analysis. …”
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Advancing Horticultural Crop Loss Reduction Through Robotic and AI Technologies: Innovations, Applications, and Practical Implications by H. W. Gammanpila, M. A. Nethmini Sashika, S. V. G. N. Priyadarshani

“…In horticulture crop loss reduction, AI plays a vital role when coupled with machine learning algorithms. By analyzing extensive volumes of data encompassing weather patterns, soil conditions, and occurrences of pests and diseases, AI systems can provide farmers with real-time insights and predictive models. …”
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Estimation of Prevalence of Hospital-Acquired Blood Infections among Patients Admitted at a Tertiary Hospital in Oman over a Period of Five Years: A Cross-Sectional Study by Marah El-Beeli, Yahya Al-Farsi, Abdullah Balkhair, Zakariya Al-Muharrmi, Mansoor Al-Jabri, Samir Al-Adawi

“…The study calls for the timely formulation and adoption of national HA-BSI screening and management programs centered on surveillance systems based on real-time analytics and machine learning.…”
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A Heterogeneous Ensemble Learning Method Combining Spectral, Terrain, and Texture Features for Landslide Mapping by Yi He, Hesheng Chen, Qing Zhu, Qing Zhang, Lifeng Zhang, Tao Liu, Wende Li, Huaiyuan Chen

“…The existing landslide recognition methods mainly focus on the use of spectral bands of optical remote sensing and machine learning base classifiers, which are insufficient in landslide characterization in complex scenes, resulting in a high missed and false detection of landslides. …”
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Impact of short-term soil disturbance on cadmium remobilization and associated risk in vulnerable regions by Zhong Zhuang, Hao Qi, Siyu Huang, Qiqi Wang, Yanan Wan, Huafen Li

“…This study highlights the potential of hybrid data-driven approaches, combining machine learning, mechanistic model and stochastic prediction to simplify the complex environmental process, allowing for integrated risk evaluations.…”
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Data-Driven Model for the Prediction of Total Dissolved Gas: Robust Artificial Intelligence Approach by Mohamed Khalid AlOmar, Mohammed Majeed Hameed, Nadhir Al-Ansari, Mohammed Abdulhakim AlSaadi

“…The accurate and more reliable prediction of TDG has a very significant role in preserving the diversity of aquatic organisms and reducing the phenomenon of fish deaths. Herein, two machine learning approaches called support vector regression (SVR) and extreme learning machine (ELM) have been applied to predict the saturated TDG% at USGS 14150000 and USGS 14181500 stations which are located in the USA. …”
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Strategies to Improve the Quality and Freshness of Human Bone Marrow-Derived Mesenchymal Stem Cells for Neurological Diseases by Da Yeon Lee, Sung Eun Lee, Do Hyeon Kwon, Saraswathy Nithiyanandam, Mi Ha Lee, Ji Su Hwang, Shaherin Basith, Jung Hwan Ahn, Tae Hwan Shin, Gwang Lee

“…As studies on the traditional characteristics of hBM-MSCs before transplantation into the brain are very limited, omics and machine learning approaches are needed to evaluate cell conditions with indepth and comprehensive analyses. …”
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Deep learning based tractography with TractSeg in patients with hemispherotomy: Evaluation and refinement by Johannes Gruen, Tobias Bauer, Theodor Rüber, Thomas Schultz

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Psychological distress in adolescence and later economic and health outcomes in the United States population: A retrospective and modeling study. by Nathaniel Z Counts, Noemi Kreif, Timothy B Creedon, David E Bloom

“…<h4>Methods and findings</h4>This analysis estimated the relationship between psychological distress in those aged 15 to 17 years in 2000 and economic and health outcomes approximately 10 years later, accounting for an array of explanatory variables using machine learning-enabled methods. The cohort was from the National Longitudinal Study of Youth 1997 and nationally representative of those aged 12 to 18 years in 1997. …”
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Deep Learning-Based Feature Extraction Technique for Single Document Summarization Using Hybrid Optimization Technique by Jyotirmayee Rautaray, Sangram Panigrahi, Ajit Kumar Nayak, Premananda Sahu, Kaushik Mishra

“…The proposed approach&#x2019;s results were compared with existing methods, including CSO, QABC, PSO, GJO, FF, and machine learning techniques like SVM and RF. The hybrid CSO-HHO algorithm achieved an accuracy of 99.56%, demonstrating its superiority in text summarization tasks.…”
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Construction and validation of risk prediction models for renal replacement therapy in patients with acute pancreatitis by Fei Zuo, Lei Zhong, Jie Min, Jinyu Zhang, Longping Yao

“…Using these features, four machine learning (ML) algorithms were developed. The optimal model was visualized and clarified using SHapley Additive exPlanations (SHAP) and presented as a nomogram. …”
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Feature selection enhances peptide binding predictions for TCR-specific interactions by Hamid Teimouri, Hamid Teimouri, Zahra S. Ghoreyshi, Zahra S. Ghoreyshi, Anatoly B. Kolomeisky, Anatoly B. Kolomeisky, Anatoly B. Kolomeisky, Jason T. George, Jason T. George, Jason T. George, Jason T. George

“…A broad range of physicochemical properties, including amino acid composition, dipeptide composition, and tripeptide features, were integrated into the machine learning-based feature selection framework to identify key properties contributing to binding affinity.ResultsOur analysis reveals that leveraging optimized feature subsets not only simplifies the model complexity but also enhances predictive performance, enabling more precise identification of TCR peptide interactions. …”
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Predicting the exposure of mycophenolic acid in children with autoimmune diseases using a limited sampling strategy: A retrospective study by Ping Zheng, Ting Pan, Ya Gao, Juan Chen, Liren Li, Yan Chen, Dandan Fang, Xuechun Li, Fei Gao, Yilei Li

“…This study aims to use machine learning and deep learning algorithms to develop a prediction model of MPA exposure for pediatric autoimmune diseases with optimizing sampling frequency. …”
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Environmental exposures related to gut microbiota among children with asthma: a pioneer study in Taiwan by Aji Kusumaning Asri, Tsunglin Liu, Hui-Ju Tsai, Jiu-Yao Wang, Chih-Da Wu

“…Air pollution was estimated using an ensemble learning model that combined regression and machine-learning algorithms, while greenspace was quantified using the normalized difference vegetation index (NDVI) and green land-cover data. …”
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Novel immune cross-talk between inflammatory bowel disease and IgA nephropathy by Qianqian Yan, Zihao Zhao, Dongwei Liu, Jia Li, Shaokang Pan, Jiayu Duan, Zhangsuo Liu

“…Weighted gene co-expression network analysis (WGCNA) was implemented in the IBD dataset to identify the major immune infiltration modules, and the Boruta algorithm, RFE algorithm, and LASSO regression were applied to filter the cross-talk genes. Next, multiple machine learning models were applied to confirm the optimal cross-talk genes. …”
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Current update on surgical management for spinal tuberculosis: a scientific mapping of worldwide publications by Romaniyanto Romaniyanto, Muhana Fawwazy Ilyas, Aldebaran Lado, Daffa Sadewa, Daffa Sadewa, Dykall Naf'an Dzikri, Enrico Ananda Budiono, Enrico Ananda Budiono

“…The recent phase reflects a shift towards technology-driven approaches, including minimally invasive techniques, artificial intelligence, and machine learning. China emerged as the leading country with the most contributions based on author, affiliations, funding sponsors, and countries. …”
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