AIMC Topic: Neoplasms

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Deep learning powers cancer diagnosis in digital pathology.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Technological innovation has accelerated the pathological diagnostic process for cancer, especially in digitizing histopathology slides and incorporating deep learning-based approaches to mine the subvisual morphometric phenotypes for improving patho...

A meta-learning approach for genomic survival analysis.

Nature communications
RNA sequencing has emerged as a promising approach in cancer prognosis as sequencing data becomes more easily and affordably accessible. However, it remains challenging to build good predictive models especially when the sample size is limited and th...

Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images.

Nature communications
Histopathological images are a rich but incompletely explored data type for studying cancer. Manual inspection is time consuming, making it challenging to use for image data mining. Here we show that convolutional neural networks (CNNs) can be system...

How wide is the application of genetic big data in biomedicine.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
In the era of big data, massive genetic data, as a new industry, has quickly swept almost all industries, especially the pharmaceutical industry. As countries around the world start to build their own gene banks, scientists study the data to explore ...

ENNAACT is a novel tool which employs neural networks for anticancer activity classification for therapeutic peptides.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
The prevalence of cancer as a threat to human life, responsible for 9.6 million deaths worldwide in 2018, motivates the search for new anticancer agents. While many options are currently available for treatment, these are often expensive and impact t...

Machine Learning-Based Risk Assessment for Cancer Therapy-Related Cardiac Dysfunction in 4300 Longitudinal Oncology Patients.

Journal of the American Heart Association
Background The growing awareness of cardiovascular toxicity from cancer therapies has led to the emerging field of cardio-oncology, which centers on preventing, detecting, and treating patients with cardiac dysfunction before, during, or after cancer...

Simultaneously Detecting Monoamine Oxidase A and B in Disease Cell/Tissue Samples Using Paper-Based Devices.

ACS applied bio materials
As enzymes in the outer membrane of the mitochondrion, monoamine oxidases (MAOs) can catalyze the oxidative deamination of monoamines in the human body. According to different substrates, MAOs can be divided into MAO-A and MAO-B. The imbalance of the...

Artificial Intelligence Predicts Drug Response.

Cancer discovery
A new artificial intelligence-based predictive modeling framework called DrugCell could accurately predict effective drugs and treatment combinations based on tumor genotype, according to a proof-of-concept analysis.

Identifying transcriptomic correlates of histology using deep learning.

PloS one
Linking phenotypes to specific gene expression profiles is an extremely important problem in biology, which has been approached mainly by correlation methods or, more fundamentally, by studying the effects of gene perturbations. However, genome-wide ...

Deep learning in cancer pathology: a new generation of clinical biomarkers.

British journal of cancer
Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers. However, the growing number of these complex biomarkers tends to increase the cost and time for decision-making in routine daily oncology practice; furthermore, bi...