Multi-omic and machine learning analysis of mitochondrial RNA modification genes in lung adenocarcinoma for prognostic and therapeutic implications by Xiao Zhang, Jiatao Liu, Yaolin Cao, Wei Wang, Haoran Lin, Yue Yu

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Ensemble machine learning models for lung cancer incidence risk prediction in the elderly: a retrospective longitudinal study by Songjing Chen, Sizhu Wu

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Enhancing Soil Moisture Prediction in Drought-Prone Agricultural Regions Using Remote Sensing and Machine Learning Approaches by Xizhuoma Zha, Shaofeng Jia, Yan Han, Wenbin Zhu, Aifeng Lv

“…The results indicated that the machine learning model achieved a correlation coefficient (R) of 0.92 for estimating spatial near-surface soil moisture. …”
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Vegetation height estimation based on machine learning model driven by multi-source data in Eurasian temperate grassland by Wuhua Wang, Jiakui Tang, Na Zhang, Xuefeng Xu, Anan Zhang, Yanjiao Wang, Kaihui Li, Yidan Wang

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Machine learning-based study of hardness in polypropylene/carbon nanotube and low-density polyethylene/carbon nanotube composites by Harshit Sharma, Gaurav Arora, Raj Kumar, Suman Debnath, Suchart Siengchin

“…Abstract In the present work, the hardness prediction of polypropylene/carbon nanotubes (PP/CNT) and low-density polyethylene/carbon nanotubes (LDPE/CNT) composite materials, processed by microwave technique, has been explored using machine learning models i.e. (Random Forest, Support Vector Regression, K-Nearest Neighbors, Linear Regression, and Neural Network). …”
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Comparative analysis of Sentinel-2 and PlanetScope imagery for chlorophyll-a prediction using machine learning models by Eden T. Wasehun, Leila Hashemi Beni, Courtney A. Di Vittorio, Christopher M. Zarzar, Kyana R.L. Young

“…Consequently, spatiotemporal maps of Chl-a concentration across the reservoir were generated using the best-performing machine learning models: XGBoost for S2 data and SVR for PS data. …”
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Assessing chemical exposure risk in breastfeeding infants: An explainable machine learning model for human milk transfer prediction by Xiaojie Huang, Jiajia Chen, Peineng Liu

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Predicting emergency department admissions using a machine-learning algorithm: a proof of concept with retrospective study by Cyrielle Brossard, Christophe Goetz, Pierre Catoire, Lauriane Cipolat, Christophe Guyeux, Cédric Gil Jardine, Mahuna Akplogan, Laure Abensur Vuillaume

“…Methods We performed a retrospective multicenter study in two French ED from January 1st, 2010 to December 31st, 2019.We tested several machine learning algorithms and compared the results. Results The arrival and departure times from the ED of 2 hospitals were collected from all consultations during the study period, then grouped into 87 600 one-hour slots. …”
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Supervised machine learning and genotype by trait biplot as promising approaches for selection of phytochemically enriched Rhus coriaria genotypes by Hamid Hatami Maleki, Reza Darvishzadeh, Ahmad Alijanpour, Yousef Seyfari

“…Here, the identified phytochemically superior sumac group was effectively distinguished from the inferior sumac group using ISSRs information via supervised machine learning. By using 13 feature selection algorithms, ISSR loci (U823) L1, (U835) L1, (U801) L1, (U816) L2, (U816) L4, (U835) L4, (U854) L1, and (U835) L9 were identified as functional markers which could predict phytochemical response of sumac germplasm. …”
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Prediction of Distant Metastasis of Renal Cell Carcinoma Based on Interpretable Machine Learning: A Multicenter Retrospective Study by Dong J, Duan M, Liu X, Li H, Zhang Y, Zhang T, Fu C, Yu J, Hu W, Peng S

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Prediction of urinary tract infection using machine learning methods: a study for finding the most-informative variables by Sajjad Farashi, Hossein Emad Momtaz

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Prediction of Pt, Ir, Ru, and Rh complexes light absorption in the therapeutic window for phototherapy using machine learning by V. Vigna, T. F. G. G. Cova, A. A. C. C. Pais, E. Sicilia

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A data driven machine learning approach for predicting and optimizing sulfur compound adsorption on metal organic frameworks by Mohsen Shayanmehr, Sepehr Aarabi, Ahad Ghaemi, Alireza Hemmati

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A data-driven machine learning algorithm to predict the effectiveness of inulin intervention against type II diabetes by Shuheng Yang, Ralf Weiskirchen, Wenjing Zheng, Xiangxu Hu, Aibiao Zou, Zhiguo Liu, Hualin Wang

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Predicting the reduction in heatstroke and heart disease-related mortality under urban modification scenarios using machine learning by Yukitaka Ohashi, Ko Nakajima, Yuya Takane, Yukihiro Kikegawa, Tomohiko Ihara, Kazutaka Oka

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Oscillating Mindfully: Using Machine Learning to Characterize Systems-Level Electrophysiological Activity During Focused Attention Meditation by Noga Aviad, Oz Moskovich, Ophir Orenstein, Etam Benger, Arnaud Delorme, Amit Bernstein

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Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening by Hao Li, Zhijian Liu, Kejun Liu, Zhien Zhang

“…Fortunately, knowledge-based machine learning can provide a fast and precise prediction method for SWH performance. …”
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All-Cause Mortality Prediction in Subjects with Diabetes Mellitus Using a Machine Learning Model and Shapley Values by Oana Mirea, Mostafa Ghelich Oghli, Oana Neagoe, Mihaela Berceanu, Eugen Țieranu, Liviu Moraru, Victor Raicea, Ionuț Donoiu

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Data-Driven Machine-Learning Model in District Heating System for Heat Load Prediction: A Comparison Study by Fisnik Dalipi, Sule Yildirim Yayilgan, Alemayehu Gebremedhin

“…We present our data-driven supervised machine-learning (ML) model to predict heat load for buildings in a district heating system (DHS). …”
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A comparative study on different machine learning approaches with periodic items for the forecasting of GPS satellites clock bias by Longjiang Song, Jiahao Liu, Leilei Wang, Ziyi Wang, Yibo Yuan

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