Cancer Biomarkers: AI Special Issue
A new special issue of the journal Cancer Biomarkers has been published in Volume 33, Issue 2 (Feb 2022) on the topic "Applications of Artificial Intelligence in Biomarker Research" (guest editor: Karin Rodland, PhD). All content is currently openly available (only until Aug 31, 2022) and we are pleased to share information on this dedicated webpage!
Please note: The issue is no longer openly available. All the content was only freely downloadable until Aug 31, 2022.
Extract from the Editorial
"One of the most challenging areas for artificial intelligence is in application to biomedical problems – building highly accurate models that are both explainable and trustworthy. For biomarker discovery, recent advances in integrating multi-omics data with advanced feature selection and ranking are leading to better understanding of mechanisms driving disease. Further, integrating constraints into the models based on biological domain knowledge in a principled manner can improve both the accuracy and interpretation of models being applied to predict health outcomes, stratify patients for treatment, understand the underlying molecular mechanisms of disease, and many other tasks needed to realize the goals of personalized medicine. This special issue is devoted to the application of artificial intelligence and machine learning approaches to the unique challenges of biomarker discovery."
Special Issue Press Release
Artificial Intelligence and Machine Learning Show Promise in Cancer Diagnosis and Treatment
AI, deep learning (DL), and machine learning (ML) have transformed many industries and areas of science. Now, these tools are being applied to address the challenges of cancer biomarker discovery, where the analysis of vast amounts of imaging and molecular data is beyond the ability of traditional statistical analyses and tools.
“The biomarker field is blessed with a plethora of imaging and molecular-based data, and at the same time, plagued with so much data that no one individual can comprehend it all,” explains guest editor Karin Rodland, PhD, Pacific Northwest National Laboratory, Richland; and Oregon Health and Science University, Portland, OR, USA. “AI offers a solution to that problem, and it has the potential to uncover novel interactions that more accurately reflect the biology of cancer and other diseases.” Promising applications of AI, DL, and ML presented in this issue include identifying early-stage cancers, inferring the site of the specific cancer, aiding in the assignment of appropriate therapeutic options for each patient, characterizing the tumor microenvironment, and predicting the response to immunotherapy.
Special Issue Testimonials
In this special issue, researchers propose various approaches and explore some of the unique challenges of using AI, DL, and ML to improve the accuracy and predictive power of biomarkers for cancer and other diseases. We are pleased to share the insights from a number of noted experts about these areas.
Anirban Maitra, MBBS, MD Anderson Cancer Center
As the universe of cancer research and clinical care expands with the generation of ever larger datasets and integration of data across diverse platforms, it comes as no surprise that AI and ML are increasingly being adopted into oncology. For those of us familiar with the unfortunate phenomenon of “missed cancers” on serial imaging scans or biomarker assays, especially in high-risk individuals, AI/ML-based tools can be pivotal. This issue is highly timely and provides a sampling of the excitement permeating the field.
Kenneth W. Kinzler, PhD, Johns Hopkins Kimmel Cancer Center
Advances in machine learning are impinging on our daily lives in an ever-increasing manner. The same is true in biomedical research, especially in the area of cancer research where ML approaches are promising to improve our ability to detect cancer early and enhance patient management. This special issue demonstrates the ability of ML to improve cancer research in areas as diverse as early detection and electronic medical records.
Chris Amos, PhD, Baylor College of Medicine
This special issue brings together a wealth of new approaches for applying new technologies in machine learning and artificial intelligence with advances in high-throughput biomarker analysis to characterize patterns that identify individuals at high risk for developing cancer. It provides a great resource for computational scientists, researchers, and clinicians to understand these state-of-the-art developments.
Samir M. Hanash, MD, PhD, MD Anderson Cancer Center
Current interest in biomarkers spans the need for personalized cancer therapy and monitoring for disease progression and recurrence to cancer risk assessment and early detection. There is a wide world of platforms for biomarker discovery from genomics to proteomics and metabolomics, among others, that yield vast amounts of data that benefit from AI approaches to data analysis. This special issue is timely as it addresses the application of AI to cancer research and the contribution of AI for improving cancer detection and diagnosis through biomarker discovery.
View All Articles
The special issue is openly available until August 31, 2022 so you can read, download, and share all content!
Applications of artificial intelligence (AI) in ovarian cancer, pancreatic cancer, and image biomarker discovery
Mikdadi, Dina | O’Connell, Kyle A. | Meacham, Philip J. | Dugan, Madeleine A. | Ojiere, Michael O. | Carlson, Thaddeus B. | Klenk, Juergen A.
Optimal vocabulary selection approaches for privacy-preserving deep NLP model training for information extraction and cancer epidemiology
Yoon, Hong-Jun | Stanley, Christopher | Christian, J. Blair | Klasky, Hilda B. | Blanchard, Andrew E. | Durbin, Eric B. | Wu, Xiao-Cheng | Stroup, Antoinette | Doherty, Jennifer | Schwartz, Stephen M. | Wiggins, Charles | Damesyn, Mark | Coyle, Linda | Tourassi, Georgia D.
Personalized statistical learning algorithms to improve the early detection of cancer using longitudinal biomarkers
Tayob, Nabihah | Feng, Ziding
Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of pre-diagnostic computed tomography images
Qureshi, Touseef Ahmad | Gaddam, Srinivas | Wachsman, Ashley Max | Wang, Lixia | Azab, Linda | Asadpour, Vahid | Chen, Wansu | Xie, Yibin | Wu, Bechien | Pandol, Stephen Jacob | Li, Debiao
Machine learning analyses of highly-multiplexed immunofluorescence identifies distinct tumor and stromal cell populations in primary pancreatic tumors
Vance, Krysten | Alitinok, Alphan | Winfree, Seth | Jensen-Smith, Heather | Swanson, Benjamin J. | Grandgenet, Paul M. | Klute, Kelsey A. | Crichton, Daniel J. | Hollingsworth, Michael A.
miRNAs expression pattern and machine learning models elucidate risk for gastric GIST
Stefanou, Ioannis K. | Dovrolis, Nikolas | Gazouli, Maria | Theodorou, Dimitrios | Zografos, Georgios K. | Toutouzas, Konstantinos G.
Radiomics model of 18F-FDG PET/CT imaging for predicting disease-free survival of early-stage uterine cervical squamous cancer
Liu, Shuai | Li, Ruikun | Liu, Qiufang | Sun, Dazheng | Yang, Hongxing | Pan, Herong | Wang, Lisheng | Song, Shaoli
Multi-OMICs data analysis identifies molecular features correlating with tumor immunity in colon cancer
Elsayed, Inas | Elsayed, Nazik | Feng, Qiushi | Sheahan, Kieran | Moran, Bruce | Wang, Xiaosheng