AIMC Topic: Neoplasms

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Improved prediction of protein-protein interactions by a modified strategy using three conventional docking software in combination.

International journal of biological macromolecules
Proteins play a crucial role in many biological processes, where their interaction with other proteins are integral. Abnormal protein-protein interactions (PPIs) have been linked to various diseases including cancer, and thus targeting PPIs holds pro...

A deep learning-based interpretable decision tool for predicting high risk of chemotherapy-induced nausea and vomiting in cancer patients prescribed highly emetogenic chemotherapy.

Cancer medicine
OBJECTIVE: This study aims to develop a risk prediction model for chemotherapy-induced nausea and vomiting (CINV) in cancer patients receiving highly emetogenic chemotherapy (HEC) and identify the variables that have the most significant impact on pr...

Understanding Trust Determinants in a Live Chat Service on Familial Cancer: Qualitative Triangulation Study With Focus Groups and Interviews in Germany.

Journal of medical Internet research
BACKGROUND: In dealing with familial cancer risk, seeking web-based health information can be a coping strategy for different stakeholder groups (ie, patients, relatives, and those suspecting an elevated familial cancer risk). In the vast digital lan...

Breast Cancer Histopathological Images Segmentation Using Deep Learning.

Sensors (Basel, Switzerland)
Hospitals generate a significant amount of medical data every day, which constitute a very rich database for research. Today, this database is still not exploitable because to make its valorization possible, the images require an annotation which rem...

A subcomponent-guided deep learning method for interpretable cancer drug response prediction.

PLoS computational biology
Accurate prediction of cancer drug response (CDR) is a longstanding challenge in modern oncology that underpins personalized treatment. Current computational methods implement CDR prediction by modeling responses between entire drugs and cell lines, ...

Global serum profiling: an opportunity for earlier cancer detection.

Journal of experimental & clinical cancer research : CR
The advances in cancer research achieved in the last 50 years have been remarkable and have provided a deeper knowledge of this disease in many of its conceptual and biochemical aspects. From viewing a tumor as a 'simple' aggregate of mutant cells an...

Deep learning integrates histopathology and proteogenomics at a pan-cancer level.

Cell reports. Medicine
We introduce a pioneering approach that integrates pathology imaging with transcriptomics and proteomics to identify predictive histology features associated with critical clinical outcomes in cancer. We utilize 2,755 H&E-stained histopathological sl...

Computational pathology in cancer diagnosis, prognosis, and prediction - present day and prospects.

The Journal of pathology
Computational pathology refers to applying deep learning techniques and algorithms to analyse and interpret histopathology images. Advances in artificial intelligence (AI) have led to an explosion in innovation in computational pathology, ranging fro...

CaMeL-Net: Centroid-aware metric learning for efficient multi-class cancer classification in pathology images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cancer grading in pathology image analysis is a major task due to its importance in patient care, treatment, and management. The recent developments in artificial neural networks for computational pathology have demonstrated...