- Using Machine Learning to Leverage Biomarker Change and ...🔍
- a machine learning|guided biomarker discovery study using data ...🔍
- Transparent Exploration of Machine Learning for Biomarker ...🔍
- Artificial intelligence for proteomics and biomarker discovery🔍
- Disease prediction with multi|omics and biomarkers empowers case ...🔍
- Deep learning facilitates multi|data type analysis and predictive ...🔍
- Leveraging machine learning predictive biomarkers to augment the ...🔍
- Interpretable Machine Learning Leverages Proteomics to Improve ...🔍
Using Machine Learning to Leverage Biomarker Change and ...
Using Machine Learning to Leverage Biomarker Change and ...
A flexible, machine learning approach that incorporated longitudinal CEA information yielded a simple and high-performing model for predicting recurrence on ...
Using Machine Learning to Leverage Biomarker Change and ...
The risk of colorectal cancer (CRC) recurrence after primary treatment varies across individuals and over time. Using patients' most up-to-date ...
Using Machine Learning to Leverage Biomarker Change and ...
Request PDF | Using Machine Learning to Leverage Biomarker Change and Predict Colorectal Cancer Recurrence | Purpose: The risk of colorectal cancer (CRC) ...
a machine learning-guided biomarker discovery study using data ...
Broad-capture proteomic technologies have the potential to improve disease prediction, enabling targeted prevention and management, but studies have so far ...
Transparent Exploration of Machine Learning for Biomarker ... - NCBI
Machine learning (ML) has become a promising tool for this purpose. However, it is sometimes applied in an opaque manner and generally requires ...
Artificial intelligence for proteomics and biomarker discovery
Machine learning has also become central to biomarker discovery from proteomics data, which now starts to outperform existing best-in-class assays. Finally, we ...
(PDF) Leveraging machine learning predictive biomarkers to ...
Radiomic models, which leverage complex imaging patterns and machine learning, are increasingly accurate in predicting patient response to ...
Disease prediction with multi-omics and biomarkers empowers case ...
Here, we present an ensemble machine-learning framework (machine learning with phenotype associations, MILTON) utilizing a range of biomarkers ...
Deep learning facilitates multi-data type analysis and predictive ...
The heterogeneity, epigenetic changes, and high degree of polymorphism observed in oncogenes demand biomarker-assisted personalized medication schemes. Deep ...
Leveraging machine learning predictive biomarkers to augment the ...
In this study, we show that radiomic models, which leverage complex brain MRI patterns and machine learning, can be utilized in clinical trials ...
Interpretable Machine Learning Leverages Proteomics to Improve ...
Providing accurate disease risk predictions and identifying genes associated with CVD are crucial for prevention, early intervention, and the ...
machine learning approaches for biomarker identification and ...
Machine learning methods are widely used to infer patterns from biological data, identify biomarkers, and make predictions. The objective of this research is to ...
Editorial: Leveraging machine learning for omics-driven biomarker ...
Here, we organized a Research Topic on “Leveraging Machine Learning for Omics-driven Biomarker Discovery.” In total, about 12 outstanding works were presented.
Machine Learning Techniques for Developing Remotely Monitored ...
Machine Learning (ML) techniques can process and engineer mHealth data into a precise and multidimensional biomarker of disease activity. Objective: This ...
Methodology for biomarker discovery with reproducibility in ...
With the advancements in omics technologies and AI, research focused on the discovery for potential biomarkers in the human microbiome using ...
Biomarker discovery using machine learning in the psychosis ...
Many of these discoveries resulted from pursuits of objective and quantifiable biomarkers in tandem with the application of analytical tools such as machine ...
Ten quick tips for biomarker discovery and validation analyses using ...
Enrico Glaab · A first step in the preparation of biomarker signature discovery studies is to define the scientific objective and scope clearly ...
Interpretable Machine Learning on Metabolomics Data Reveals ...
Here, we report an interpretable neural network (NN) framework to accurately predict disease and identify significant biomarkers using whole ...
cell assay platforms may be leveraged using AI/ML (e.g., computational modeling and ... how the drug effect will change with time when a certain dose or dosing ...
Explainable artificial intelligence (XAI) to find optimal in-silico ...
Despite advancements in machine learning applications for this purpose, the specific contribution of in-silico biomarkers to toxicity risk ...