Diagnostic biomarkers |
Transcriptomic biomarker |
Random Forest |
Transcriptomics data from blood samples of patients with a particular disease and healthy controls. |
Random Forest was used to predict disease status based on the expression levels of transcripts. Several transcripts were found to be associated with disease status, and these transcripts were used as potential transcriptomic biomarkers for disease diagnosis |
(Huseby et al., 2022Huseby CJ, Delvaux E, Brokaw DL, Coleman PD. Blood Transcript Biomarkers Selected by Machine Learning Algorithm Classify Neurodegenerative Diseases including Alzheimer’s Disease. Biomolecules. 2022;12(11):1592.) |
Metabolomic biomarker |
K-Nearest Neighbors |
Metabolomics data from urine samples of patients with a particular disease and healthy controls. |
K-Nearest Neighbours was used to predict disease status based on the levels of metabolites. Several metabolites were found to be associated with disease status, and these metabolites were used as potential metabolomic biomarkers for disease diagnosis |
(Gowda et al., 2008Gowda GA, Zhang S, Gu H, Asiago V, Shanaiah N, Raftery D. Metabolomics-based methods for early disease diagnostics. Expert Rev Mol Diagn. 2008;8(5):617-633.) |
Diagnostic biomarkers |
Proteomic biomarker |
Support Vector Machines |
Proteomics data from serum samples of patients with a particular disease and healthy controls. |
Support Vector Machines was used to predict disease status depending on the number of proteins. Several proteins were found to be associated with disease status, and these proteins were used as potential proteomic biomarkers chemotherapy resistance prediction in small cell lung cancer. |
(Han et al., 2012Han M, Dai J, Zhang Y, Lin Q, Jiang M, Xu X, et al. Support vector machines coupled with proteomics approaches for detecting biomarkers predicting chemotherapy resistance in small cell lung cancer. Oncology Reports. 2012;28:2233-2238.) |
Prognostic biomarkers |
Transcriptomic biomarker |
Artificial Neural Network |
Transcriptomics data from tissue samples of patients with a particular disease and healthy controls. |
Artificial Neural Network was used to predict disease progression based on the expression levels of transcripts. Several transcripts were found to be associated with breast cancer progression, and these transcripts were used as potential transcriptomic biomarkers for breast cancer prognosis. |
(Chen et al., 2021Chen X, Chen DG, Zhao Z, Balko JM, Chen J. Artificial image objects for classification of breast cancer biomarkers with transcriptome sequencing data and convolutional neural network algorithms. Breast Cancer Res. 2021;23(1):96.) |
Metabolomic biomarker |
Logistic Regression |
Metabolomics data from blood samples of Alzheimer’s patients and healthy controls. |
The study found that levels of certain metabolites, such as choline and lactate, were significantly different between Alzheimer’s patients and healthy controls. Logistic Regression was used to build a model that could determine Alzheimer’s disease based on these metabolites. The model was able to accurately predict Alzheimer’s disease with high sensitivity and specificity. |
(Wang et al., 2021Wang YY, Sun YP, Luo YM, Peng DH, Li X, Yang BY, et al. Biomarkers for the Clinical Diagnosis of Alzheimer’s Disease: Metabolomics Analysis of Brain Tissue and Blood. Front Pharmacol. 2021;12.) |
Transcriptomic biomarker |
Random Forest |
Transcriptomics data from blood samples of patients with breast cancer and healthy controls. |
The study found that levels of certain transcripts were significantly different between breast cancer patients and healthy controls. Random Forest was used to build a model that could predict breast cancer prognosis based on these transcripts. This was able to accurately determine the prognosis of breast cancer accurately. |
(Zare, Postovit, Githaka, 2021Zare A, Postovit LM, Githaka JM. Robust inflammatory breast cancer gene signature using nonparametric random forest analysis. Breast Cancer Res. 2021;23(1):92.) |
Predictive biomarkers |
Transcriptomic biomarker |
Random Forest |
Transcriptomics data from biopsy samples of patients with a particular disease and healthy controls. |
Random Forest was used to predict treatment response based on the expression levels of genes. Several genes were found to be associated with treatment response, and these genes were used as potential transcriptomic biomarkers for rheumatoid arthritis. |
(Rychkov et al., 2021Rychkov D, Neely J, Oskotsky T, Yu S, Perlmutter N, Nititham J, et al. Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis. Front Immunol. 2021;12:638066.) |
Metabolomic biomarker |
Support Vector Machines (SVM) |
Metabolomics data from blood samples of patients with a particular disease and healthy controls. |
SVM was used to predict treatment response based on the levels of metabolites. Several metabolites were found to be associated with treatment response, and these metabolites were used as potential metabolomic biomarkers for treatment prediction. |
(Liu et al., 2022Liu J, Huang L, Shi X, Gu C, Xu H, Liu S. Clinical parameters and metabolomic biomarkers that predict in-hospital outcomes in patients with ST-segment elevated myocardial infarctions. Frontiers Physiol. 2022; 12:820240.) |
Epigenetic biomarker |
Logistic Regression |
Data of DNA methylation from blood mononuclear cells of patients with a particular disease and healthy controls. |
Logistic Regression was used to predict treatment response based on the levels of DNA methylation. Several regions of DNA methylation were found to be associated with treatment response, and these regions were used as potential epigenetic biomarkers for treatment prediction. |
(Cappozzo et al., 2022Cappozzo A, McCrory C, Robinson O, Freni Sterrantino A, Sacerdote C, Krogh V, et al. A blood DNA methylation biomarker for predicting short-term risk of cardiovascular events. Clinical Epigenetics. 2022;14(1):121.) |