Artificial intelligence applied to the study of human milk and breastfeeding: a scoping review by Sergio Agudelo-Pérez, Daniel Botero-Rosas, Laura Rodríguez-Alvarado, Julián Espitia-Angel, Lina Raigoso-Díaz

“…The thematic analysis revealed five major categories: 1. Prediction of exclusive breastfeeding patterns: AI models, such as decision trees and machine learning algorithms, identify factors influencing breastfeeding practices, including maternal experience, hospital policies, and social determinants, highlighting actionable predictors for intervention. 2. …”
Get full text

Deciphering the role of miR-71 in Echinococcus multilocularis early development in vitro. by Matías Gastón Pérez, Markus Spiliotis, Natalia Rego, Natalia Macchiaroli, Laura Kamenetzky, Nancy Holroyd, Marcela Alejandra Cucher, Klaus Brehm, Mara Cecilia Rosenzvit

“…Using genomic information and bioinformatic algorithms for miRNA binding prediction, we found a high number of potential miR-71 targets in E. multilocularis. …”
Get full text

Development of an immune-related gene signature applying Ridge method for improving immunotherapy responses and clinical outcomes in lung adenocarcinoma by Zhen Chen, Yongjun Zhang

“…The sequencing data of immune-related genes were comprehensively analyzed by introducing a new computational framework and 10 machine learning algorithms (a total of 101 combinations) to determine the prognostic genes, which were further combined to develop an immune prognostic signature (IMMPS) using the stepCox and Ridge methods. …”
Get full text

CLINICAL AND LABORATORY ASPECTS OF DETECTING SPECIFIC IgE ANTIBODIES TO COW’S MILK AND ITS COMPONENTS by N. A. Alkhutova, N. A. Kovyazina, O. L. Zhizhina

“…So far, however, we have no generally approved laboratory algorithms for diagnostics and monitoring of treatment efficiency in the cow milk allergy and its compomemts.We have performed a laboratory study of 187 children at the age of 3 months to 10 years. …”
Get full text

Oxidative stress gene expression in ulcerative colitis: implications for colon cancer biomarker discovery by Ting Yan, Ting Su, Miaomiao Zhu, Qiyuan Qing, Binjie Huang, Jun Liu, Tenghui Ma

“…The model may be beneficial in prognostic prediction and guiding treatment decisions.…”
Get full text

Machine vision and learning for evaluating different rancidity grades of Prunus mandshurica (Maxim.) Koehne by Yashun Wang, Huirong Chen, Jianting Gong, Yang Cui, Huiqin Zou, Yonghong Yan

“…Discrimination and prediction models based on color features combined with multiple machine learning algorithms were established using 10-fold cross-validation and external test set validation. …”
Get full text

Current Status of Application of Spaceborne GNSS-R Raw Intermediate-Frequency Signal Measurements: Comprehensive Review by Qiulan Wang, Jinwei Bu, Yutong Wang, Donglan Huang, Hui Yang, Xiaoqing Zuo

“…These research results have important application potential in fields such as environmental monitoring, climate change research, and weather prediction, and are expected to provide new technological means for global geophysical parameter retrieval.…”
Get full text

FrameBoost: Advanced Video Analytics With Inference Trigger Frame Selection via Tracking Error Estimation by Jin Mo Yang, Sunwook Hwang, Jeongjun Park, Saewoong Bahk

“…Beyond selecting critical frames for system accuracy, a major requirement for ITF selection algorithms is efficiency. They must be able to extract informative frames while imposing minimal computational overhead on the analytics pipeline. …”
Get full text

Machine Learning–Based Analysis of Lifestyle Risk Factors for Atherosclerotic Cardiovascular Disease: Retrospective Case-Control Study by Hye-Jin Kim, Heeji Choi, Hyo-Jung Ahn, Seung-Ho Shin, Chulho Kim, Sang-Hwa Lee, Jong-Hee Sohn, Jae-Jun Lee

“…MethodsUsing data from the Korea National Health and Nutrition Examination Survey, 5 ML algorithms were used for the prediction of high ASCVD risk: logistic regression (LR), support vector machine, random forest, extreme gradient boosting, and light gradient boosting models. …”
Get full text

Electricity Demand Forecasting Using Deep Polynomial Neural Networks and Gene Expression Programming During COVID-19 Pandemic by Cagatay Cebeci, Kasım Zor

“…The power-generation mix of future grids will be quite diversified with the ever-increasing share of renewable energy technologies. Therefore, the prediction of electricity demand will become crucial for resource optimization and grid stability. …”
Get full text

Identification and experimental validation of biomarkers associated with mitochondrial and programmed cell death in major depressive disorder by Shengjie Xiong, Lixin Liao, Meng Chen, Qing Gan

“…The predictive nomogram and drug predictions offer valuable tools for MDD management.…”
Get full text

AI for rapid identification of major butyrate-producing bacteria in rhesus macaques (Macaca mulatta) by Annemiek Maaskant, Donghyeok Lee, Huy Ngo, Roy C. Montijn, Jaco Bakker, Jan A. M. Langermans, Evgeni Levin

“…In addition, to improve transparency, we employed explainability analysis to uncover the image features influencing the model’s predictions. Results By integrating fecal image data with corresponding metagenomic sequencing information, the deep learning (DL) and machine learning (ML) algorithms successfully predicted 16 individual bacterial genera (area under the curve (AUC) > 0.7) among the 50 most abundant genera in rhesus macaques (Macaca mulatta). …”
Get full text

Application of Energy Dispersive X-ray Fluorescence Spectroscopy in Analysis of Heavy Metals in Soil: A Review by Aosong JIANG, Longhua WU, Zhu LI

“…Therefore, the current mainstream focus is on combining the advantages of different algorithms for the preprocessing analysis of ED-XRF spectroscopy and the establishment of quantitative analysis models to improve the accuracy of ED-XRF detection. …”
Get full text

Drought Detection in Satellite Imagery: A Layered Ensemble Machine Learning Approach by Muhammad Owais Raza, Naeem Ahmed Mahoto, Mana Saleh Al Reshan, Ali Alqazzaz, Adel Rajab, Asadullah Shaikh

“…Abstract Drought has been a major calamity due to climate change in recent years. Predicting drought has grabbed the attention of meteorologists and climate scientists, who study and look for modern techniques. …”
Get full text

Multiparameter diagnostic model using S100A9, CCL5 and blood biomarkers for nasopharyngeal carcinoma by Lu Long, Ya Tao, Wenze Yu, Qizhuo Hou, Yunlai Liang, Kangkang Huang, Huidan Luo, Bin Yi

“…Variable selection was conducted using least absolute shrinkage and selection operator (LASSO) regression. NPC prediction models were developed using four machine-learning algorithms, and their performance was evaluated with ROC curves. …”
Get full text

Research progress on shale gas productivity evaluation: concepts, methods and future directions by ZHU Suyang, PENG Zhen, DI Yunting, PENG Xiaolong, LIU Dongchen, GUAN Wenjie

“…In addition, further work is needed to incorporate mechanism-informed constraints into machine learning algorithms, enhance model transparency through causal inference, and improve interpretability. …”
Get full text

Determinação do coeficiente convectivo de transferência de calor de figos submetidos ao resfriamento rápido Determination of the convective heat transfer coefficient of fig fruits... by Mariangela Amendola, Saul Dussán-Sarria, Anderson A. Rabello

“…Algorithms employing the finite differences and finite elements methods were implemented for the one-dimensional and three-dimensional models. …”
Get full text

Unveiling new insights into migraine risk stratification using machine learning models of adjustable risk factors by Yu-Chen Liu, Ye-Hai Liu, Hai-Feng Pan, Wei Wang

“…Second, we trained ensemble machine learning (ML) algorithms that incorporated these factors, with Shapley Additive exPlanations (SHAP) value analysis quantifying predictor importance. …”
Get full text

GRLGRN: graph representation-based learning to infer gene regulatory networks from single-cell RNA-seq data by Kai Wang, Yulong Li, Fei Liu, Xiaoli Luan, Xinglong Wang, Jingwen Zhou

“…Conclusions The experimental results and case studies illustrate the considerable performance of GRLGRN in predicting gene interactions and provide interpretability for the prediction tasks, such as identifying hub genes in the network and uncovering implicit links.…”
Get full text

Characterization and stratification of risk factors of stroke in people living with HIV: A theory-informed systematic review by Martins Nweke, Nombeko Mshunqane

“…Diabetes, atrial fibrillation, smoking habits, hypertension, age, and viral load demonstrated a high likelihood of association with stroke in PLWH and should be prioritized when constructing clinical prediction algorithms for HIV-related stroke. Conclusions The most important factors were hypertension and chronic kidney disease, followed by smoking, dyslipidemia, diabetes, HCV, HBV, CD4 count, use of ART, TB, and substance use (cocaine). …”
Get full text