[PDF][PDF] Artificial intelligence for multimodal data integration in oncology
In oncology, the patient state is characterized by a whole spectrum of modalities, ranging
from radiology, histology, and genomics to electronic health records. Current artificial …
from radiology, histology, and genomics to electronic health records. Current artificial …
Deep learning in cancer diagnosis, prognosis and treatment selection
Deep learning is a subdiscipline of artificial intelligence that uses a machine learning
technique called artificial neural networks to extract patterns and make predictions from …
technique called artificial neural networks to extract patterns and make predictions from …
The Matthews correlation coefficient (MCC) is more informative than Cohen's Kappa and Brier score in binary classification assessment
Even if measuring the outcome of binary classifications is a pivotal task in machine learning
and statistics, no consensus has been reached yet about which statistical rate to employ to …
and statistics, no consensus has been reached yet about which statistical rate to employ to …
The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification
Binary classification is a common task for which machine learning and computational
statistics are used, and the area under the receiver operating characteristic curve (ROC …
statistics are used, and the area under the receiver operating characteristic curve (ROC …
Machine learning methods for cancer classification using gene expression data: a review
F Alharbi, A Vakanski - Bioengineering, 2023 - mdpi.com
Cancer is a term that denotes a group of diseases caused by the abnormal growth of cells
that can spread in different parts of the body. According to the World Health Organization …
that can spread in different parts of the body. According to the World Health Organization …
A systematic review on biomarker identification for cancer diagnosis and prognosis in multi-omics: from computational needs to machine learning and deep learning
Biomarkers, also known as biological markers, are substances like transcripts,
deoxyribonucleic acid (DNA), genes, proteins, and metabolites that indicate whether a …
deoxyribonucleic acid (DNA), genes, proteins, and metabolites that indicate whether a …
Lung cancer survival period prediction and understanding: Deep learning approaches
Introduction Survival period prediction through early diagnosis of cancer has many benefits.
It allows both patients and caregivers to plan resources, time and intensity of care to provide …
It allows both patients and caregivers to plan resources, time and intensity of care to provide …
A statistical comparison between Matthews correlation coefficient (MCC), prevalence threshold, and Fowlkes–Mallows index
Even if assessing binary classifications is a common task in scientific research, no
consensus on a single statistic summarizing the confusion matrix has been reached so far …
consensus on a single statistic summarizing the confusion matrix has been reached so far …
Omics-based deep learning approaches for lung cancer decision-making and therapeutics development
Lung cancer has been the most common and the leading cause of cancer deaths globally.
Besides clinicopathological observations and traditional molecular tests, the advent of …
Besides clinicopathological observations and traditional molecular tests, the advent of …
[HTML][HTML] Machine learning application in personalised lung cancer recurrence and survivability prediction
Abstract Machine learning is an important artificial intelligence technique that is widely
applied in cancer diagnosis and detection. More recently, with the rise of personalised and …
applied in cancer diagnosis and detection. More recently, with the rise of personalised and …