AI Medical Compendium Topic:
Neoplasms

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A benchmark study of deep learning-based multi-omics data fusion methods for cancer.

Genome biology
BACKGROUND: A fused method using a combination of multi-omics data enables a comprehensive study of complex biological processes and highlights the interrelationship of relevant biomolecules and their functions. Driven by high-throughput sequencing t...

MatchMaker: A Deep Learning Framework for Drug Synergy Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Drug combination therapies have been a viable strategy for the treatment of complex diseases such as cancer due to increased efficacy and reduced side effects. However, experimentally validating all possible combinations for synergistic interaction e...

Improved prediction of gene expression through integrating cell signalling models with machine learning.

BMC bioinformatics
BACKGROUND: A key problem in bioinformatics is that of predicting gene expression levels. There are two broad approaches: use of mechanistic models that aim to directly simulate the underlying biology, and use of machine learning (ML) to empirically ...

Distinguishing tumor from healthy tissue in human liver ex vivo using machine learning and multivariate analysis of diffuse reflectance spectra.

Journal of biophotonics
The aim of this work was to evaluate the capability of diffuse reflectance spectroscopy to distinguish malignant liver tissues from surrounding tissues and to determine whether an extended wavelength range (450-1550 nm) offers any advantages over usi...

A systematic review on machine learning and deep learning techniques in cancer survival prediction.

Progress in biophysics and molecular biology
Cancer is a disease which is characterised by the unusual and uncontrollable growth of body cells. This usually happens asymptomatically and gets spread to other parts of the body. The major problem in treating cancer is that its progress is not moni...

Accurate somatic variant detection using weakly supervised deep learning.

Nature communications
Identification of somatic mutations in tumor samples is commonly based on statistical methods in combination with heuristic filters. Here we develop VarNet, an end-to-end deep learning approach for identification of somatic variants from aligned tumo...

Comparative analysis of high- and low-level deep learning approaches in microsatellite instability prediction.

Scientific reports
Deep learning-based approaches in histopathology can be largely divided into two categories: a high-level approach using an end-to-end model and a low-level approach using feature extractors. Although the advantages and disadvantages of both approach...

Machine learning-enabled quantitative ultrasound techniques for tissue differentiation.

Journal of medical ultrasonics (2001)
PURPOSE: Quantitative ultrasound (QUS) infers properties about tissue microstructure from backscattered radio-frequency ultrasound data. This paper describes how to implement the most practical QUS parameters using an ultrasound research system for t...

Analyzing the determinants to accept a virtual assistant and use cases among cancer patients: a mixed methods study.

BMC health services research
BACKGROUND: Technological progress in artificial intelligence has led to the increasing popularity of virtual assistants, i.e., embodied or disembodied conversational agents that allow chatting with a technical system in a natural language. However, ...

Applications of artificial intelligence multiomics in precision oncology.

Journal of cancer research and clinical oncology
Cancer is the second leading worldwide disease that depends on oncogenic mutations and non-mutated genes for survival. Recent advancements in next-generation sequencing (NGS) have transformed the health care sector with big data and machine learning ...