A Simple Machine Learning-Based Quantitative Structure–Activity Relationship Model for Predicting pIC₅₀ Inhibition Values of FLT3 Tyrosine Kinase by Jackson J. Alcázar, Ignacio Sánchez, Cristian Merino, Bruno Monasterio, Gaspar Sajuria, Diego Miranda, Felipe Díaz, Paola R. Campodónico

“…This study aimed to develop a robust and user-friendly machine learning-based quantitative structure–activity relationship (QSAR) model to predict the inhibitory potency (pIC₅₀ values) of FLT3 inhibitors, addressing the limitations of previous models in dataset size, diversity, and predictive accuracy. …”
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Logistic Regression Model in a Machine Learning Application to Predict Elderly Kidney Transplant Recipients with Worse Renal Function One Year after Kidney Transplant: Elderly KTbot by Ubiracé Fernando Elihimas Júnior, Jamila Pinho Couto, Wallace Pereira, Michel Pompeu Barros de Oliveira Sá, Eduardo Eriko Tenório de França, Filipe Carrilho Aguiar, Diogo Buarque Cordeiro Cabral, Saulo Barbosa Vasconcelos Alencar, Saulo José da Costa Feitosa, Thais Oliveira Claizoni dos Santos, Helen Conceição dos Santos Elihimas, Emilly Pereira Alves, Marcio José de Carvalho Lima, Frederico Castelo Branco Cavalcanti, Paulo Adriano Schwingel

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Implementasi Sensor Polar H10 dan Raspberry Pi dalam Pemantauan dan Klasifikasi Detak Jantung Beberapa Individu Secara Simultan dengan Pendekatan Machine Learning  by eko sakti pramukantoro, Kasyful Amron, Viera Wardhani, Putri Annisa Kamila

“…Data tersebut kemudian diprediksi menggunakan model machine learning berbasis random forest yang berjalan pada Raspberry Pi untuk prediksi 5 jenis detak jantung. …”
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Integrating single cell analysis and machine learning methods reveals stem cell-related gene S100A10 as an important target for prediction of liver cancer diagnosis and immunotherapy by Shenjun Huang, Tingting Tu

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Novel machine learning-driven comparative analysis of CSP, STFT, and CSP-STFT fusion for EEG data classification across multiple meditation and non-meditation sessions in BCI pipeline by Nalinda D. Liyanagedera, Corinne A. Bareham, Heather Kempton, Hans W. Guesgen

Subjects: “…Machine learning…”
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Machine learning models predicting risk of revision or secondary knee injury after anterior cruciate ligament reconstruction demonstrate variable discriminatory and accuracy performance: a systematic review by Benjamin Blackman, Prushoth Vivekanantha, Rafay Mughal, Ayoosh Pareek, Anthony Bozzo, Kristian Samuelsson, Darren de SA

Subjects: “…Machine learning…”
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Machine Learning para la Clasificación y Análisis de los Índices de Biomasa y su relación con el Cambio Climático, Desierto de Atacama by Santos Gómez, Edwin Pino-Vargas, Germán Huayna, Jorge Espinoza-Molina, Karina Acosta-Caipa, Fredy Cabrera-Olivera4

Subjects: “…cambio climático; índices de biomasa; desierto atacama; machine learning.…”
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Enhancing individual glomerular filtration rate assessment: can we trust the equation? Development and validation of machine learning models to assess the trustworthiness of estimated GFR compared to measured GFR by Antoine Lanot, Anna Akesson, Felipe Kenji Nakano, Celine Vens, Jonas Björk, Ulf Nyman, Anders Grubb, Per-Ola Sundin, Björn O. Eriksen, Toralf Melsom, Andrew D. Rule, Ulla Berg, Karin Littmann, Kajsa Åsling-Monemi, Magnus Hansson, Anders Larsson, Marie Courbebaisse, Laurence Dubourg, Lionel Couzi, Francois Gaillard, Cyril Garrouste, Lola Jacquemont, Nassim Kamar, Christophe Legendre, Lionel Rostaing, Natalie Ebert, Elke Schaeffner, Arend Bökenkamp, Christophe Mariat, Hans Pottel, Pierre Delanaye

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Identification of patient demographic, clinical, and SARS-CoV-2 genomic factors associated with severe COVID-19 using supervised machine learning: a retrospective multicenter study by Kuganya Nirmalarajah, Patryk Aftanas, Shiva Barati, Emily Chien, Gloria Crowl, Amna Faheem, Lubna Farooqi, Alainna J. Jamal, Saman Khan, Jonathon D. Kotwa, Angel X. Li, Mohammad Mozafarihashjin, Jalees A. Nasir, Altynay Shigayeva, Winfield Yim, Lily Yip, Xi Zoe Zhong, Kevin Katz, Robert Kozak, Andrew G. McArthur, Nick Daneman, Finlay Maguire, Allison J. McGeer, Venkata R. Duvvuri, Samira Mubareka

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Correlation between the white blood cell/platelet ratio and 28-day all-cause mortality in cardiac arrest patients: a retrospective cohort study based on machine learning by Huai Huang, Guangqin Ren, Shanghui Sun, Zhi Li, Yongtian Zheng, Lijuan Dong, Shaoliang Zhu, Xiaosheng Zhu, Wenyu Jiang

“…ObjectiveThis study aims to evaluate the association between the white blood cell-to-platelet ratio (WPR) and 28-day all-cause mortality among patients experiencing cardiac arrest.MethodsUtilizing data from 748 cardiac arrest patients in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) 2.2 database, machine learning algorithms, including the Boruta feature selection method, random forest modeling, and SHAP value analysis, were applied to identify significant prognostic biomarkers. …”
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Development of a Three-Stage Hybrid Model by Utilizing a Two-Stage Signal Decomposition Methodology and Machine Learning Approach to Predict Monthly Runoff at Swat River Basin, Pakistan by Muhammad Sibtain, Xianshan Li, Ghulam Nabi, Muhammad Imran Azam, Hassan Bashir

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A Supervised Explainable Machine Learning Model for Perioperative Neurocognitive Disorder in Liver-Transplantation Patients and External Validation on the Medical Information Mart for Intensive Care IV Database: Retrospective Study by Zhendong Ding, Linan Zhang, Yihan Zhang, Jing Yang, Yuheng Luo, Mian Ge, Weifeng Yao, Ziqing Hei, Chaojin Chen

“…ObjectiveThis study used machine learning (ML) algorithms with an aim to extract critical predictors and develop an ML model to predict PND among LT recipients. …”
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Detection and Classification of Teacher-Rated Children’s Activity Levels Using Millimeter-Wave Radar and Machine Learning: A Pilot Study in a Real Primary School Environment by Tianyi Wang, Takuya Sakamoto, Yu Oshima, Itsuki Iwata, Masaya Kato, Haruto Kobayashi, Manabu Wakuta, Masako Myowa, Tokomo Nishimura, Atsushi Senju

Subjects: “…Machine learning…”
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Study on postoperative survival prediction model for non-small cell lung cancer: application of radiomics technology workflow based on multi-organ imaging features and various machine learning algorithms by Hanlin Wang, Yuan Hong, Zimo Zhang, Kang Cheng, Bo Chen, Renquan Zhang

“…The study extracted radiomic features from the tumor, whole lung, and erector spinae muscles of the patients and applied 11 machine learning algorithms to construct prediction models. …”
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Associations between age, red cell distribution width and 180-day and 1-year mortality in giant cell arteritis patients: mediation analyses and machine learning in a cohort study by Si Chen, Rui Nie, Xiaoran Shen, Yan Wang, Haixia Luan, Xiaoli Zeng, Yanhua Chen, Hui Yuan

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Mid-infrared spectra of dried and roasted cocoa (Theobroma cacao L.): A dataset for machine learning-based classification of cocoa varieties and prediction of theobromine and caffeine contentMendeley Data by Gentil A. Collazos-Escobar, Andrés F. Bahamón-Monje, Nelson Gutiérrez-Guzmán

“…This dataset provides a basis for further research, enabling the integration of mid-infrared spectral data with HPLC (as a standard) to fine-tune machine learning and deep learning models that could be used to simultaneously predict the theobromine and caffeine content, as well as cocoa variety in both dried and roasted cocoa samples using a non-destructive approach based on spectral data. …”
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Identification of Staphylococcus aureus, Enterococcus faecium, Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii from Raman spectra by Artificial Intelligent Raman Detection and Identification System (AIRDIS) with machine learning by Yu-Tzu Lin, Hsiu-Hsien Lin, Chih-Hao Chen, Kun-Hao Tseng, Pang-Chien Hsu, Ya-Lun Wu, Wei-Cheng Chang, Nai-Shun Liao, Yi-Fan Chou, Chun-Yi Hsu, Yu-Hui Liao, Mao-Wang Ho, Shih-Sheng Chang, Po-Ren Hsueh, Der-Yang Cho

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Epidemiology and Ecology of Toscana Virus Infection and Its Global Risk Distribution by Xue-Geng Hong, Mei-Qi Zhang, Fang Tang, Si-Hui Song, Jia-Yi Wang, Zhen-Yu Hu, Li-Ming Liu, Xiao-Ai Zhang, Yi Sun, Li-Qun Fang, Wei Liu

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Generative adversarial networks based adaptive modulation and coding for next-generation 5G communication systems by A. Manikandan, Rakesh Thoppaen Suresh Babu, S Jai Ganesh, T. Sanjay

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Dual-market quantitative trading: The dynamics of liquidity and turnover in financial markets by Qing Zhu, Chenyu Han, Yuze Li

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