AIMC Topic: Neoplasms

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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...

Artificial intelligence-aided clinical annotation of a large multi-cancer genomic dataset.

Nature communications
To accelerate cancer research that correlates biomarkers with clinical endpoints, methods are needed to ascertain outcomes from electronic health records at scale. Here, we train deep natural language processing (NLP) models to extract outcomes for p...

DNN-Boost: Somatic mutation identification of tumor-only whole-exome sequencing data using deep neural network and XGBoost.

Journal of bioinformatics and computational biology
Detection of somatic mutation in whole-exome sequencing data can help elucidate the mechanism of tumor progression. Most computational approaches require exome sequencing for both tumor and normal samples. However, it is more common to sequence exome...