AI Medical Compendium Topic:
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Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample.

Genome biology
BACKGROUND: Accurate detection of somatic mutations is challenging but critical in understanding cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network-based somatic mutation detecti...

Deep-Learning-Based Cancer Profiles Classification Using Gene Expression Data Profile.

Journal of healthcare engineering
The quantity of data required to give a valid analysis grows exponentially as machine learning dimensionality increases. In a single experiment, microarrays or gene expression profiling assesses and determines gene expression levels and patterns in v...

PanClassif: Improving pan cancer classification of single cell RNA-seq gene expression data using machine learning.

Genomics
Cancer is one of the major causes of human death per year. In recent years, cancer identification and classification using machine learning have gained momentum due to the availability of high throughput sequencing data. Using RNA-seq, cancer researc...

Extendable and explainable deep learning for pan-cancer radiogenomics research.

Current opinion in chemical biology
Radiogenomics is a field where medical images and genomic profiles are jointly analyzed to answer critical clinical questions. Specifically, people want to identify non-invasive imaging biomarkers that are associated with both genomic features and cl...

DEGnext: classification of differentially expressed genes from RNA-seq data using a convolutional neural network with transfer learning.

BMC bioinformatics
BACKGROUND: A limitation of traditional differential expression analysis on small datasets involves the possibility of false positives and false negatives due to sample variation. Considering the recent advances in deep learning (DL) based models, we...

Registries, Databases and Repositories for Developing Artificial Intelligence in Cancer Care.

Clinical oncology (Royal College of Radiologists (Great Britain))
Modern artificial intelligence techniques have solved some previously intractable problems and produced impressive results in selected medical domains. One of their drawbacks is that they often need very large amounts of data. Pre-existing datasets i...

A survey on graph-based deep learning for computational histopathology.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
With the remarkable success of representation learning for prediction problems, we have witnessed a rapid expansion of the use of machine learning and deep learning for the analysis of digital pathology and biopsy image patches. However, learning ove...

Morphological features of single cells enable accurate automated classification of cancer from non-cancer cell lines.

Scientific reports
Accurate cancer detection and diagnosis is of utmost importance for reliable drug-response prediction. Successful cancer characterization relies on both genetic analysis and histological scans from tumor biopsies. It is known that the cytoskeleton is...

Artificial intelligence-based identification of octenidine as a Bcl-xL inhibitor.

Biochemical and biophysical research communications
Apoptosis plays an essential role in maintaining cellular homeostasis and preventing cancer progression. Bcl-xL, an anti-apoptotic protein, is an important modulator of the mitochondrial apoptosis pathway and is a promising target for anticancer ther...