Progress and trends on machine learning in proteomics during 1997-2024: a bibliometric analysis by Chao Tan, Hao Liu, Zhen Zhang, Xinyu Liu, Yinquan Ai, Xiumin Wu, Enlin Jian, Yongyan Song, Jin Yang

“…ObjectiveDespite growing interest in the application of machine learning (ML) in proteomics, a comprehensive and systematic mapping of this research domain has been lacking. …”
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Predicting anemia management in dialysis patients using open-source machine learning libraries by Takahiro Inoue, Norio Hanafusa, Yuki Kawaguchi, Ken Tsuchiya

“…These models closely mirrored actual prescribing patterns, suggesting feasibility for clinical integration. …”
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Single-cell sequencing combined with machine learning to identify glioma biomarkers and therapeutic targets by Yu Yan, Zhengmin Chu, Qi Zhong, Genghuan Wang

“…A 16-gene DRG signature was developed for predicting the survival of glioma patients. Machine learning identified four important genes with high AUCs in both training and test sets. …”
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Machine learning framework for investigating nano- and micro-scale particle diffusion in colonic mucus by Marco Tjakra, Kristína Lidayová, Christophe Avenel, Christel A.S. Bergström, Shakhawath Hossain

“…This study presents a machine-learning-driven framework that integrates microrheological features into diffusional fingerprinting to characterize nano- and micro-scale particle diffusion patterns in mucus and assess the effect of mucus microrheology on such movements. …”
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Advancing sustainable mobility in India with electric vehicles: market trends and machine learning insights by Ezhilmaran Devarasan, Deepikaa Nagarajan, Jenisha Rachel

“…This study offers an in-depth analysis of India’s Electric Vehicle (EV) market dynamics from FY 2014 to February 2024, utilizing machine learning techniques to identify sales trends, regional disparities, and adoption drivers. …”
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Machine Learning Approaches to Predict No-Shows in Saudi Arabian Primary and General Healthcare Settings by Abdulrahman Alshehri, Abdullah Saeed, Abdullah AlShafea, Sabah Althubiany, Mohammed Alshehri, Amer Alzahrani, Khalid Hakami, Lamia Ibrahim, Abdulrahim Alshehri, Rana Alamri

“…Regular appointments and PHCCs showed different attendance patterns than hospitals, while walk-in appointments did. …”
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Using Explainable Machine Learning Methods to Predict the Survivability Rate of Pediatric Respiratory Diseases by Roshan Kumar, V Srirama, Krishnaraj Chadaga, H Muralikrishna, Niranjana Sampathila, Srikanth Prabhu, Rajagopala Chadaga

“…Large datasets of clinical variables are analyzed by machine learning (ML) to find patterns and co-relations that human clinicians might not be able to predict immediately. …”
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Machine Learning and Digital-Twins-Based Internet of Robotic Things for Remote Patient Monitoring by Sehat Ullah, Sangeen Khan, David Vanecek, Inam Ur Rehman

“…Furthermore, health carers cannot forecast abnormalities based on health data. Machine Learning (ML) can analyze massive amounts of data and perceive patterns to anticipate anomalous health conditions. …”
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Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis by Kevin Barrera-Llanga, Jordi Burriel-Valencia, Angel Sapena-Bano, Javier Martinez-Roman

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Machine heuristic in algorithm aversion: Perceived creativity and effort of output created by or with artificial intelligence by Sigurd Birk Heimstad, Anders Hauge Wien, Tarje Gaustad

“…Our main theoretical contribution is the identification of the machine heuristic (MH), individuals' preexisting beliefs about AI capabilities, as a key moderator. …”
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Nuclear morphometrics coupled with machine learning identifies dynamic states of senescence across age by Sahil A. Mapkar, Sarah A. Bliss, Edgar E. Perez Carbajal, Sean H. Murray, Zhiru Li, Anna K. Wilson, Vikrant Piprode, You Jin Lee, Thorsten Kirsch, Katerina S. Petroff, Fengyuan Liu, Michael N. Wosczyna

“…Here we show that this method reveals dynamic, age-associated patterns of senescence in regenerating skeletal muscle and osteoarthritic articular cartilage. …”
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An evolution of forensic linguistics: From manual analysis to machine learning – A narrative review by R. Thamizh Mani, Vikram Palimar, Mamatha Shivananda Pai, T.S. Shwetha, M. Nirmal Krishnan

“…Forensic linguistics has evolved from manual textual analysis to machine learning (ML)-driven methodologies, fundamentally transforming its role in criminal investigations. …”
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Integrating Machine Learning Algorithms to Construct a Triaptosis-Related Prognostic Model in Melanoma by Xie J, Zhang M, Qi M

“…Key triaptosis-related genes and pathways were identified and incorporated into machine learning models to construct a prognostic signature. …”
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Optimizing Reservoir Separability in Liquid State Machines for Spatio-Temporal Classification in Neuromorphic Hardware by Oscar I. Alvarez-Canchila, Andres Espinal, Alberto Patiño-Saucedo, Horacio Rostro-Gonzalez

“…In this paper, we propose an optimization approach using Particle Swarm Optimization (PSO) to enhance reservoir separability in Liquid State Machines (LSMs) for spatio-temporal classification in neuromorphic systems. …”
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Predicting high confidence ctDNA somatic variants with ensemble machine learning models by Rugare Maruzani, Liam Brierley, Andrea Jorgensen, Anna Fowler

“…Rule-based variant filtering methods either remove a substantial number of true positive ctDNA variants along with false variant calls or retain an implausibly large number of total variants. Machine Learning (ML) enables identification of complex patterns which may improve ability to distinguish between real somatic ctDNA variants and false positive calls. …”
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Machine learning-based identification of exosome-related biomarkers and drugs prediction in nasopharyngeal carcinoma by Zhengyu Wei, Guoli Wang, Yanghao Hu, Chongchang Zhou, Yuna Zhang, Yi Shen, Yaowen Wang

“…The least absolute shrinkage and selection operator regression, support vector machine, and random forest approaches were utilized to develop NPC diagnostic model. …”
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Clinical subtypes identification and feature recognition of sepsis leukocyte trajectories based on machine learning by ShengHui Miao, YiJing Liu, Min Li, Jing Yan

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Trust-driven approach to enhance early forest fire detection using machine learning by Tayyab Khan, Karan Singh, Bhoopesh Singh Bhati, Khaleel Ahmad, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene

“…Our technique combines trust mechanisms with machine learning algorithms to create a very advanced forest fire detection system.…”
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Time Series Analysis of Solar Power Generation Based on Machine Learning for Efficient Monitoring by Umer Farooq, Muhammad Faheem Mushtaq, Zahid Ullah, Muhammad Talha Ejaz, Urooj Akram, Sheraz Aslam

“…However, meteorological factors, such as solar irradiation, weather patterns, precipitation, and overall climate conditions, pose challenges to the seamless integration of energy production into the power grid. …”
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A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities by José Luis Uc Castillo, Ana Elizabeth Marín Celestino, Diego Armando Martínez Cruz, José Tuxpan Vargas, José Alfredo Ramos Leal, Janete Morán Ramírez

“…It observed that Artificial Neural Networks (ANN) models were preferred, probably due to their capability to learn and model non-linear and complex relationships in addition to other popular models such as Random Forest (RF) and Support Vector Machines (SVM). It identified that the selection and application of the algorithms rely on the study objective and the data patterns. …”
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