Relationship between stress hyperglycemia ratio and progression of non target coronary lesions: a retrospective cohort study by Shiqi Liu, Ziyang Wu, Gaoliang Yan, Yong Qiao, Yuhan Qin, Dong Wang, Chengchun Tang

“…Logistic regression models, restricted cubic spline analysis, and machine learning algorithms (LightGBM, decision tree, and XGBoost) were utilized to analyse the relationship of stress hyperglycemia ratio and non target lesion progression. …”
Get full text

Harmonizing population health data into OMOP common data model: a demonstration using COVID-19 sero-surveillance data from Nairobi Urban Health and Demographic Surveillance System by Michael Ochola, Sylvia Kiwuwa-Muyingo, Tathagata Bhattacharjee, David Amadi, Maureen Ng’etich, Damazo Kadengye, Henry Owoko, Boniface Igumba, Jay Greenfield, Jim Todd, Agnes Kiragga, Agnes Kiragga

Get full text

MLinvitroTox reloaded for high-throughput hazard-based prioritization of high-resolution mass spectrometry data by Katarzyna Arturi, Eliza J. Harris, Lilian Gasser, Beate I. Escher, Georg Braun, Robin Bosshard, Juliane Hollender

“…MLinvitroTox is a machine learning (ML) framework comprising 490 independent XGBoost classifiers trained on molecular fingerprints from chemical structures and target-specific endpoints from the ToxCast/Tox21 invitroDBv4.1 database. …”
Get full text

Intraoperative haemodynamic optimisation using the Hypotension Prediction Index and its impact on tissular perfusion: a protocol for a randomised controlled trial by Javier Ripollés-Melchor, Ane Abad-motos, Juan Victor Lorente, Ignacio Jimenez, Alejandra Becerra, Wilbert Wesselink, Francesca Reguant, Irene Mojarro, Maria de los Angeles Fuentes, Elizabeth Agudelo, Francisco Herrero-Machancoses, Paula Callejo, Joan Bosch, Manuel Ignacio Monge

“…The Hypotension Prediction Index (HPI) developed using machine learning techniques, allows the prediction of arterial hypotension analysing the arterial pressure waveform. …”
Get full text

New Insights into the Role of Inflammatory Pathways and Immune Cell Infiltration in Sleep Deprivation-Induced Atrial Fibrillation: An Integrated Bioinformatics and Experimental Stu... by Liang J, Tang B, Shen J, Rejiepu M, Guo Y, Wang X, Shao S, Guo F, Wang Q, Zhang L

“…The application of machine learning uncovered four crucial genes—CDC5L, MAPK14, RAB5A, and YBX1—with YBX1 becoming the predominant gene in diagnostic processes. …”
Get full text

Personalized prediction of anticancer potential of non-oncology drugs through learning from genome derived molecular pathways by Xiaobao Dong, Huanhuan Liu, Ting Tong, Liuxing Wu, Jianhua Wang, Tianyi You, Yongjian Wei, Xianfu Yi, Hongxi Yang, Jie Hu, Haitao Wang, Xiaoyan Wang, Mulin Jun Li

“…Herein we present CHANCE, a supervised machine learning model designed to predict the anticancer activities of non-oncology drugs for specific patients by simultaneously considering personalized coding and non-coding mutations. …”
Get full text

Comprehensive pan-cancer analysis reveals NTN1 as an immune infiltrate risk factor and its potential prognostic value in SKCM by Fuxiang Luan, Yuying Cui, Ruizhe Huang, Zhuojie Yang, Shishi Qiao

“…To further elucidate the influence of genes on tumors, we utilized a variety of machine learning techniques and found that NTN1 is strongly linked to multiple cancer types, suggesting it as a potential therapeutic target. …”
Get full text

5G Networks Security Mitigation Model: An ANN-ISM Hybrid Approach by Rafiq Ahmad Khan, Habib Ullah Khan, Hathal Salamah Alwageed, Hussein Al Hashimi, Ismail Keshta

“…The proposed model includes state-of-the-art machine learning with traditional information security paradigms to offer an integrated solution to the emerging complex security issues related to 5G. …”
Get full text

Patients with Age-related Macular Degeneration Have Increased Susceptibility to Valvular Heart Disease by Natan Lishinsky-Fischer, Itay Chowers, MD, PhD, Yahel Shwartz, MSc, Jaime Levy, MD

“…Moreover, a supervised machine learning model successfully detected the presence of AMD based solemnly on the patient’s history of VHD. …”
Get full text

Experimental study and model prediction of the influence of different factors on the mechanical properties of saline clay by Hui Cheng, Lingkai Zhang, Chong Shi, Pei Pei Fan

“…The boundary point of the 2% salt content divides the effect of salt ions from promoting free water flow to blocking seepage channels, with the proportion of micropores being the primary influencing factor. (4) Employing statistical theory and machine learning algorithms, dry density, water content, and salinity are used to predict mechanical index values. …”
Get full text

Ratio of Skeletal Muscle Mass to Visceral Fat Area Is a Useful Marker for Assessing Left Ventricular Diastolic Dysfunction among Koreans with Preserved Ejection Fraction: An Analys... by Jin Kyung Oh, Yuri Seo, Wonmook Hwang, Sami Lee, Yong-Hoon Yoon, Kyupil Kim, Hyun Woong Park, Jae-Hyung Roh, Jae-Hwan Lee, Minsu Kim

“…This study investigated the association between the ratio of skeletal muscle mass to visceral fat area (SVR) and left ventricular diastolic dysfunction (LVDD) in patients with preserved ejection fraction using random forest machine learning. Methods : In total, 1,070 participants with preserved left ventricular ejection fraction who underwent comprehensive health examinations, including transthoracic echocardiography and bioimpedance body composition analysis, were enrolled. …”
Get full text

UAV Hyperspectral Remote Sensing Image Classification: A Systematic Review by Zhen Zhang, Lehao Huang, Qingwang Wang, Linhuan Jiang, Yemao Qi, Shunyuan Wang, Tao Shen, Bo-Hui Tang, Yanfeng Gu

“…This article provides an in-depth and systematic review of UAV HSI classification techniques, systematically examining the evolution from traditional machine learning approaches, such as sparse coding, compressed sensing, and kernel methods, to cutting-edge deep learning frameworks, including convolutional neural networks, Transformer models, recurrent neural networks, graph convolutional networks, generative adversarial networks, and hybrid models. …”
Get full text

Trends and Gaps in Digital Precision Hypertension Management: Scoping Review by Namuun Clifford, Rachel Tunis, Adetimilehin Ariyo, Haoxiang Yu, Hyekyun Rhee, Kavita Radhakrishnan

“…The most commonly used digital technologies were mobile phones (33/46, 72%), blood pressure monitors (18/46, 39%), and machine learning algorithms (11/46, 24%). In total, 45% (21/46) of the studies either did not report race or ethnicity data (14/46, 30%) or partially reported this information (7/46, 15%). …”
Get full text

KMT2C/KMT2D-dependent H3K4me1 mediates changes in DNA replication timing and origin activity during a cell fate transition by Deniz Gökbuget, Liana Goehring, Ryan M. Boileau, Kayla Lenshoek, Tony T. Huang, Robert Blelloch

“…The causal relationships between these features and DNA replication timing (RT), especially during cell fate changes, are largely unknown. Using machine learning, we quantify 21 chromatin features predicting local RT and RT changes during differentiation in embryonic stem cells (ESCs). …”
Get full text

Anti-TROVE2 Antibody Determined by Immune-Related Array May Serve as a Predictive Marker for Adalimumab Immunogenicity and Effectiveness in RA by Po-Ku Chen, Joung-Liang Lan, Yi-Ming Chen, Hsin-Hua Chen, Shih-Hsin Chang, Chia-Min Chung, Nurul H. Rutt, Ti-Myen Tan, Raja Nurashirin Raja Mamat, Nur Diana Anuar, Jonathan M. Blackburn, Der-Yuan Chen

“…The biomarkers were identified for predicting ADAb development and therapeutic response using the immune-related microarray and machine learning approach. ADAb-positive patients had lower drug levels at week 24 (median=0.024 μg/ml) compared with ADAb-negative patients (median=6.38 μg/ml, p<0.001). …”
Get full text

DeepCERES: A deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRI by Sergio Morell-Ortega, Marina Ruiz-Perez, Marien Gadea, Roberto Vivo-Hernando, Gregorio Rubio, Fernando Aparici, Maria de la Iglesia-Vaya, Gwenaelle Catheline, Boris Mansencal, Pierrick Coupé, José V. Manjón

“…We have also integrated deep learning with classical machine learning methods incorporating a priori knowledge from multi-atlas segmentation which improved precision and robustness. …”
Get full text

Aqueous foams in microgravity, measuring bubble sizes by Pasquet, Marina, Galvani, Nicolo, Pitois, Olivier, Cohen-Addad, Sylvie, Höhler, Reinhard, Chieco, Anthony T., Dillavou, Sam, Hanlan, Jesse M., Durian, Douglas J., Rio, Emmanuelle, Salonen, Anniina, Langevin, Dominique

“…Extracting the bubble size distribution from images of a foam surface is difficult so we have used three different procedures: manual analysis, automatic analysis with a customized Python script and machine learning analysis. Once various pitfalls were identified and taken into account, all the three procedures yield identical results within error bars. …”
Get full text

Are neurasthenia and depression the same disease entity? An electroencephalography study by Ge Dang, Lin Zhu, Chongyuan Lian, Silin Zeng, Xue Shi, Zian Pei, Xiaoyong Lan, Jian Qing Shi, Nan Yan, Yi Guo, Xiaolin Su

“…The demographic and clinical characteristics, EEG power spectral density, and functional connectivity were compared between the neurasthenia and MDD groups. Machine Learning methods such as random forest, logistic regression, support vector machines, and k nearest neighbors were also used for classification between groups to extend the identification that there is a significant different pattern between neurasthenia and MDD. …”
Get full text

Providing a General Model for the Successful Implementation of Digital Transformation in Organizations by Haidar Ahmadi, Najme Parsaei, Seyyed Hamed Hashemi, Hamidreza Nematollahi

“…Conclusion Digital transformation extends beyond the mere adoption of emerging technologies such as artificial intelligence and machine learning; it represents a paradigm shift in how traditional management and operational practices are conducted across various functions, including product development, engineering, marketing, sales, and service delivery. …”
Get full text

Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-dimensional Data-driven Priors for Inverse Problems by Gabriel Missael Barco, Alexandre Adam, Connor Stone, Yashar Hezaveh, Laurence Perreault-Levasseur

“…With the advent of machine learning, the use of data-driven population-level distributions (encoded, e.g., in a trained deep neural network) as priors is emerging as an appealing alternative to simple parametric priors in a variety of inverse problems. …”
Get full text