Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

Hajjo, Rima; Sabbah, Dima A.; Bardaweel, Sanaa K.; Tropsha, Alexander

doi:10.3390/diagnostics11050742

Cited by 16 publications

(7 citation statements)

References 220 publications

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“…These data can be categorized into different genomic, proteomic, and radiomic subcategories. Artificial intelligence, including machine learning (ML) and deep learning (DL) methods, have been implemented for various applications including biomarker discovery, and digital pathology (review [ 187 , 188 , 189 , 190 , 191 ]). In the case of cancer biomarkers, DL approaches have been utilized to detect ctDNA markers in cancer cases [ 192 , 193 ].…”

Section: Computational Analysis Of Ctdna and Sevsmentioning

confidence: 99%

Recent Advances in Device Engineering and Computational Analysis for Characterization of Cell-Released Cancer Biomarkers

Abouali

Hosseini

Purcell

et al. 2022

Cancers

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During cancer progression, tumors shed different biomarkers into the bloodstream, including circulating tumor cells (CTCs), extracellular vesicles (EVs), circulating cell-free DNA (cfDNA), and circulating tumor DNA (ctDNA). The analysis of these biomarkers in the blood, known as ‘liquid biopsy’ (LB), is a promising approach for early cancer detection and treatment monitoring, and more recently, as a means for cancer therapy. Previous reviews have discussed the role of CTCs and ctDNA in cancer progression; however, ctDNA and EVs are rapidly evolving with technological advancements and computational analysis and are the subject of enormous recent studies in cancer biomarkers. In this review, first, we introduce these cell-released cancer biomarkers and briefly discuss their clinical significance in cancer diagnosis and treatment monitoring. Second, we present conventional and novel approaches for the isolation, profiling, and characterization of these markers. We then investigate the mathematical and in silico models that are developed to investigate the function of ctDNA and EVs in cancer progression. We convey our views on what is needed to pave the way to translate the emerging technologies and models into the clinic and make the case that optimized next-generation techniques and models are needed to precisely evaluate the clinical relevance of these LB markers.

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Section: Computational Analysis Of Ctdna and Sevsmentioning

confidence: 99%

Recent Advances in Device Engineering and Computational Analysis for Characterization of Cell-Released Cancer Biomarkers

Abouali

Hosseini

Purcell

et al. 2022

Cancers

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“…In addition to microbiome-based biomarkers, there is also an emerging interest in mast cells (MCs) [ 82 , 83 , 84 , 85 ], microRNAs (miRNAs) [ 86 , 87 ], imaging, and machine-learning models [ 42 ] as non-invasive disease diagnostic and prognostic biomarkers that promise to shape the future of precision medicine. Sometimes, there is a crosstalk between the human microbiota and other genetic or chemical biomarkers.…”

Section: The Rationale For Microbiome-based Disease Biomarkersmentioning

confidence: 99%

“…The most frequently used biomarkers are derived from either biological materials or imaging data. More recently, machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers [ 42 ].…”

Section: Introductionmentioning

confidence: 99%

Unlocking the Potential of the Human Microbiome for Identifying Disease Diagnostic Biomarkers

2022

Self Cite

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The human microbiome encodes more than three million genes, outnumbering human genes by more than 100 times, while microbial cells in the human microbiota outnumber human cells by 10 times. Thus, the human microbiota and related microbiome constitute a vast source for identifying disease biomarkers and therapeutic drug targets. Herein, we review the evidence backing the exploitation of the human microbiome for identifying diagnostic biomarkers for human disease. We describe the importance of the human microbiome in health and disease and detail the use of the human microbiome and microbiota metabolites as potential diagnostic biomarkers for multiple diseases, including cancer, as well as inflammatory, neurological, and metabolic diseases. Thus, the human microbiota has enormous potential to pave the road for a new era in biomarker research for diagnostic and therapeutic purposes. The scientific community needs to collaborate to overcome current challenges in microbiome research concerning the lack of standardization of research methods and the lack of understanding of causal relationships between microbiota and human disease.

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“…Machine learning (ML) and artificial intelligence (AI) have been used successfully to extract insight from ‘big’ biological data [ 369 , 370 , 371 ]. Domain expertise from biology, genetics, elderly medicine, psychiatry, psychology, neurology, and neuroscience could be combined with new bioinformatics and statistical analytical tools to gain insight from multi-omics data.…”

Section: Artificial Intelligence and Machine Learning Approachesmentioning

confidence: 99%

A Review of the Recent Advances in Alzheimer’s Disease Research and the Utilization of Network Biology Approaches for Prioritizing Diagnostics and Therapeutics

et al. 2022

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Alzheimer’s disease (AD) is a polygenic multifactorial neurodegenerative disease that, after decades of research and development, is still without a cure. There are some symptomatic treatments to manage the psychological symptoms but none of these drugs can halt disease progression. Additionally, over the last few years, many anti-AD drugs failed in late stages of clinical trials and many hypotheses surfaced to explain these failures, including the lack of clear understanding of disease pathways and processes. Recently, different epigenetic factors have been implicated in AD pathogenesis; thus, they could serve as promising AD diagnostic biomarkers. Additionally, network biology approaches have been suggested as effective tools to study AD on the systems level and discover multi-target-directed ligands as novel treatments for AD. Herein, we provide a comprehensive review on Alzheimer’s disease pathophysiology to provide a better understanding of disease pathogenesis hypotheses and decipher the role of genetic and epigenetic factors in disease development and progression. We also provide an overview of disease biomarkers and drug targets and suggest network biology approaches as new tools for identifying novel biomarkers and drugs. We also posit that the application of machine learning and artificial intelligence to mining Alzheimer’s disease multi-omics data will facilitate drug and biomarker discovery efforts and lead to effective individualized anti-Alzheimer treatments.

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Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

Cited by 16 publications

References 220 publications

Recent Advances in Device Engineering and Computational Analysis for Characterization of Cell-Released Cancer Biomarkers

Recent Advances in Device Engineering and Computational Analysis for Characterization of Cell-Released Cancer Biomarkers

Unlocking the Potential of the Human Microbiome for Identifying Disease Diagnostic Biomarkers

A Review of the Recent Advances in Alzheimer’s Disease Research and the Utilization of Network Biology Approaches for Prioritizing Diagnostics and Therapeutics

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