Community detection algorithms in healthcare applications: a systematic review
Over the past few years, the number and volume of data sources in healthcare databases
has grown exponentially. Analyzing these voluminous medical data is both opportunity and …
has grown exponentially. Analyzing these voluminous medical data is both opportunity and …
Multi-modality artificial intelligence in digital pathology
In common medical procedures, the time-consuming and expensive nature of obtaining test
results plagues doctors and patients. Digital pathology research allows using computational …
results plagues doctors and patients. Digital pathology research allows using computational …
[HTML][HTML] Learning to predict RNA sequence expressions from whole slide images with applications for search and classification
Deep learning methods are widely applied in digital pathology to address clinical
challenges such as prognosis and diagnosis. As one of the most recent applications, deep …
challenges such as prognosis and diagnosis. As one of the most recent applications, deep …
[HTML][HTML] hist2RNA: An Efficient Deep Learning Architecture to Predict Gene Expression from Breast Cancer Histopathology Images
RK Mondol, EKA Millar, PH Graham, L Browne… - Cancers, 2023 - mdpi.com
Simple Summary Breast cancer diagnosis and treatment can be improved by understanding
the specific genetic makeup of a patient's tumour. Currently, this genetic information is …
the specific genetic makeup of a patient's tumour. Currently, this genetic information is …
[HTML][HTML] Contrastive multiple instance learning: An unsupervised framework for learning slide-level representations of whole slide histopathology images without …
Simple Summary Recent AI methods in the automated analysis of histopathological imaging
data associated with cancer have trended towards less supervision by humans. Yet, there …
data associated with cancer have trended towards less supervision by humans. Yet, there …
[HTML][HTML] CXCL1: A new diagnostic biomarker for human tuberculosis discovered using Diversity Outbred mice
D Koyuncu, MKK Niazi, T Tavolara, C Abeijon… - PLoS …, 2021 - journals.plos.org
More humans have died of tuberculosis (TB) than any other infectious disease and millions
still die each year. Experts advocate for blood-based, serum protein biomarkers to help …
still die each year. Experts advocate for blood-based, serum protein biomarkers to help …
[HTML][HTML] Deep learning-based prediction of molecular tumor biomarkers from H&E: a practical review
HD Couture - Journal of Personalized Medicine, 2022 - mdpi.com
Molecular and genomic properties are critical in selecting cancer treatments to target
individual tumors, particularly for immunotherapy. However, the methods to assess such …
individual tumors, particularly for immunotherapy. However, the methods to assess such …
[HTML][HTML] BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images
Breast cancer is the most common malignancy in women, with over 40,000 deaths annually
in the United States alone. Clinicians often rely on the breast cancer recurrence score …
in the United States alone. Clinicians often rely on the breast cancer recurrence score …
[HTML][HTML] Systems genetics uncover new loci containing functional gene candidates in Mycobacterium tuberculosis-infected Diversity Outbred mice
DM Gatti, AL Tyler, JM Mahoney, GA Churchill… - PLoS …, 2024 - journals.plos.org
Mycobacterium tuberculosis infects two billion people across the globe, and results in 8–9
million new tuberculosis (TB) cases and 1–1.5 million deaths each year. Most patients have …
million new tuberculosis (TB) cases and 1–1.5 million deaths each year. Most patients have …
[HTML][HTML] DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues
R Mahdi-Esferizi, B Haji Molla Hoseyni… - BMC …, 2023 - Springer
Background P4 medicine (predict, prevent, personalize, and participate) is a new approach
to diagnosing and predicting diseases on a patient-by-patient basis. For the prevention and …
to diagnosing and predicting diseases on a patient-by-patient basis. For the prevention and …