AIMC Topic: Ovarian Neoplasms
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Applications of artificial intelligence (AI) in ovarian cancer, pancreatic cancer, and image biomarker discovery.
Cancer biomarkers : section A of Disease markers
Jan 1, 2022
BACKGROUND: Artificial intelligence (AI), including machine learning (ML) and deep learning, has the potential to revolutionize biomedical research. Defined as the ability to "mimic" human intelligence by machines executing trained algorithms, AI met...
Integration and interplay of machine learning and bioinformatics approach to identify genetic interaction related to ovarian cancer chemoresistance.
Briefings in bioinformatics
Nov 5, 2021
Although chemotherapy is the first-line treatment for ovarian cancer (OCa) patients, chemoresistance (CR) decreases their progression-free survival. This paper investigates the genetic interaction (GI) related to OCa-CR. To decrease the complexity of...
Advances of Artificial Intelligence Application in Medical Imaging of Ovarian Cancers.
Chinese medical sciences journal = Chung-kuo i hsueh k'o hsueh tsa chih
Sep 30, 2021
Ovarian cancer is one of the three most common gynecological cancers in the world, and is regarded as a priority in terms of women's cancer. In the past few years, many researchers have attempted to develop and apply artificial intelligence (AI) tech...
XGSEA: CROSS-species gene set enrichment analysis via domain adaptation.
Briefings in bioinformatics
Sep 2, 2021
MOTIVATION: Gene set enrichment analysis (GSEA) has been widely used to identify gene sets with statistically significant difference between cases and controls against a large gene set. GSEA needs both phenotype labels and expression of genes. Howeve...
Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets.
Briefings in bioinformatics
May 20, 2021
The significance of pan-cancer categories has recently been recognized as widespread in cancer research. Pan-cancer categorizes a cancer based on its molecular pathology rather than an organ. The molecular similarities among multi-omics data found in...
Evaluation of Combined Cancer Markers With Lactate Dehydrogenase and Application of Machine Learning Algorithms for Differentiating Benign Disease From Malignant Ovarian Cancer.
Cancer control : journal of the Moffitt Cancer Center
Jan 1, 2021
BACKGROUND: The differential diagnosis of ovarian cancer is important, and there has been ongoing research to identify biomarkers with higher performance. This study aimed to evaluate the diagnostic utility of combinations of cancer markers classifie...
Feature Selection is Critical for 2-Year Prognosis in Advanced Stage High Grade Serous Ovarian Cancer by Using Machine Learning.
Cancer control : journal of the Moffitt Cancer Center
Jan 1, 2021
INTRODUCTION: Accurate prediction of patient prognosis can be especially useful for the selection of best treatment protocols. Machine Learning can serve this purpose by making predictions based upon generalizable clinical patterns embedded within le...
Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessment.
Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
Jan 1, 2021
OBJECTIVES: To develop and test the performance of computerized ultrasound image analysis using deep neural networks (DNNs) in discriminating between benign and malignant ovarian tumors and to compare its diagnostic accuracy with that of subjective a...
Management of ovarian cancer patients in affected areas during COVID-19 pandemic: Japan and Korea.
Journal of gynecologic oncology
May 1, 2020
Automated classification of multiphoton microscopy images of ovarian tissue using deep learning.
Journal of biomedical optics
Jun 1, 2018
Histopathological image analysis of stained tissue slides is routinely used in tumor detection and classification. However, diagnosis requires a highly trained pathologist and can thus be time-consuming, labor-intensive, and potentially risk bias. He...
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