Digital Quantitative Detection for Heterogeneous Protein and mRNA Expression Patterns in Circulating Tumor Cells by Hao Li, Jinze Li, Zhiqi Zhang, Qi Yang, Hong Du, Qiongzhu Dong, Zhen Guo, Jia Yao, Shuli Li, Dongshu Li, Nannan Pang, Chuanyu Li, Wei Zhang, Lianqun Zhou

“…Importantly, by integrating machine learning, d‐SCOUT allows clustering of CTC characteristics at the mRNA and protein levels, mapping normalized heterogeneous 2D molecular profiles to assess HCC metastatic risk. …”
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

Construction of a novel radioresistance-related signature for prediction of prognosis, immune microenvironment and anti-tumour drug sensitivity in non-small cell lung cancer by Yanliang Chen, Chan Zhou, Xiaoqiao Zhang, Min Chen, Meifang Wang, Lisha Zhang, Yanhui Chen, Litao Huang, Junjun Sun, Dandan Wang, Yong Chen

“…The biological functions exerted by the key gene LBH were verified by in vitro experiments.Results Ninety-nine RRRGs were screened by intersecting the results of DEGs and WGCNA, then 11 hub RRRGs associated with survival were identified using machine learning algorithms (LASSO and RSF). Subsequently, an eight-gene (APOBEC3B, DOCK4, IER5L, LBH, LY6K, RERG, RMDN2 and TSPAN2) risk score model was established and demonstrated to be an independent prognostic factor in NSCLC on the basis of Cox regression analysis. …”
Get full text

Explainable artificial intelligence in web phishing classification on secure IoT with cloud-based cyber-physical systems by Sultan Refa Alotaibi, Hend Khalid Alkahtani, Mohammed Aljebreen, Asma Alshuhail, Muhammad Kashif Saeed, Shouki A. Ebad, Wafa Sulaiman Almukadi, Moneerah Alotaibi

“…This research presents a novel approach to address this problem using machine learning (ML) methods for phishing website classification. …”
Get full text

Biomarkers troponin and procalcitonin in addition to CRB-65 enhance risk stratification in patients with community-acquired pneumonia by Imrana Farhat, Maciej Rosolowski, Katharina Ahrens, Jasmin Lienau, Peter Ahnert, Mathias Pletz, Gernot Rohde, Jan Rupp, Markus Scholz, Martin Witzenrath, the CAPNETZ Study Group

“…Methods Our study aimed to enhance the predictive capacity of the clinical CRB-65 score by evaluating eight candidate biomarkers: troponin T high-sensitive (TnT-hs), procalcitonin (PCT), N-terminal pro-brain natriuretic peptide, angiopoietin-2, copeptin, endothelin-1, lipocalin-2 and mid-regional pro-adrenomedullin. We utilised a machine-learning approach on 800 samples from the German CAPNETZ network (competence network for CAP) to refine risk prediction models combining these biomarkers with the CRB-65 score regarding our defined end-point: death or ICU admission during the current CAP episode within 28 days after study inclusion. …”
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

Constraints on triggered seismicity and its control on permeability evolution by Derek Elsworth, Ziyan Li, Pengliang Yu, Mengke An, Fengshou Zhang, Rui Huang, Zihan Sun, Guanglei Cui, Tianyu Chen, Quan Gan, Yixin Zhao, Jishan Liu, Shimin Liu

“…Although high-fidelity data sets are scarce, the EGS-Collab and Utah FORGE hydraulic stimulation field demonstration projects provide high-fidelity data sets that concurrently track permeability evolution and triggered seismicity. Machine learning deciphers the principal features of MEQs and the resulting permeability evolution that best track permeability changes – with transfer learning methods allowing robust predictions across multiple eological settings. …”
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

Genetic Biomarkers and Circulating White Blood Cells in Osteoarthritis: A Bioinformatics and Mendelian Randomization Analysis by Yimin Pan, Xiaoshun Sun, Jun Tan, Chao Deng, Changwu Wu, Georg Osterhoff, Nikolas Schopow

“…The bioinformatics methods utilized include the Limma package, WGCNA, PPI network analysis, and machine learning algorithms. Genetic variants were used as instrumental variables to evaluate the potential causal impact of circulating white blood cell (WBC) counts on OA. …”
Get full text

Using Quantitative Trait Locus Mapping and Genomic Resources to Improve Breeding Precision in Peaches: Current Insights and Future Prospects by Umar Hayat, Cao Ke, Lirong Wang, Gengrui Zhu, Weichao Fang, Xinwei Wang, Changwen Chen, Yong Li, Jinlong Wu

“…This work shows how combining genome-wide association studies and machine learning can improve the synthesis of multi-omics data and result in faster breeding cycles while preserving genetic diversity. …”
Get full text

Adaptive Hierarchical Multi-Headed Convolutional Neural Network With Modified Convolutional Block Attention for Aerial Forest Fire Detection by Md. Najmul Mowla, Davood Asadi, Shamsul Masum, Khaled Rabie

“…On the Fire Luminosity Airborne-based Machine Learning Evaluation (FLAME) dataset, the model attained accuracy rates of 99.83%, 99.10%, and 99.32%, with corresponding cKappa values of 99.66%, 98.20%, and 98.65%. …”
Get full text

BenthicNet: A global compilation of seafloor images for deep learning applications by Scott C. Lowe, Benjamin Misiuk, Isaac Xu, Shakhboz Abdulazizov, Amit R. Baroi, Alex C. Bastos, Merlin Best, Vicki Ferrini, Ariell Friedman, Deborah Hart, Ove Hoegh-Guldberg, Daniel Ierodiaconou, Julia Mackin-McLaughlin, Kathryn Markey, Pedro S. Menandro, Jacquomo Monk, Shreya Nemani, John O’Brien, Elizabeth Oh, Luba Y. Reshitnyk, Katleen Robert, Chris M. Roelfsema, Jessica A. Sameoto, Alexandre C. G. Schimel, Jordan A. Thomson, Brittany R. Wilson, Melisa C. Wong, Craig J. Brown, Thomas Trappenberg

“…The ability to collect seafloor imagery has outpaced our capacity to analyze it, hindering mobilization of this crucial environmental information. Machine learning approaches provide opportunities to increase the efficiency with which seafloor imagery is analyzed, yet large and consistent datasets to support development of such approaches are scarce. …”
Get full text

Metabolic reprogramming and macrophage expansion define ACPA-negative rheumatoid arthritis: insights from single-cell RNA sequencing by Yafeng Jiang, Zhaolan Hu, Roujie Huang, Kaying Ho, Pengfei Wang, Jin Kang, Jin Kang

“…By integrating single-cell RNA sequencing with machine learning, this study established a neural network model that robustly differentiates patients with ACPA− RA from healthy controls, highlighting promising diagnostic biomarkers and therapeutic targets centered on immune cell metabolism.…”
Get full text

Autonomous International Classification of Diseases Coding Using Pretrained Language Models and Advanced Prompt Learning Techniques: Evaluation of an Automated Analysis System Usin... by Yan Zhuang, Junyan Zhang, Xiuxing Li, Chao Liu, Yue Yu, Wei Dong, Kunlun He

“… BackgroundMachine learning models can reduce the burden on doctors by converting medical records into International Classification of Diseases (ICD) codes in real time, thereby enhancing the efficiency of diagnosis and treatment. …”
Get full text

Research Progress in Monitoring Technology of Cold Chain Logistics for Meat Products by Bin HAN, Dongmei LENG, Yuqian XU, Jianyang SHEN, Xin LI, Xiaochun ZHENG, Wei WANG, Dequan ZHANG, Chengli HOU

“…To analyze the future development of meat cold chain logistics monitoring technology combined with sensors, narrowband internet of things, machine learning, and blockchain, and to provide theoretical support for the research and application of China's cold chain logistics monitoring technology.…”
Get full text