AI Medical Compendium Topic:
Genomics

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Avocado: a multi-scale deep tensor factorization method learns a latent representation of the human epigenome.

Genome biology
The human epigenome has been experimentally characterized by thousands of measurements for every basepair in the human genome. We propose a deep neural network tensor factorization method, Avocado, that compresses this epigenomic data into a dense, i...

Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration.

Genomics
Gene expression analysis plays a significant role for providing molecular insights in cancer. Various genetic and epigenetic factors (being dealt under multi-omics) affect gene expression giving rise to cancer phenotypes. A recent growth in understan...

Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data.

PloS one
Precision medicine in oncology aims at obtaining data from heterogeneous sources to have a precise estimation of a given patient's state and prognosis. With the purpose of advancing to personalized medicine framework, accurate diagnoses allow prescri...

Artificial intelligence in oncology.

Cancer science
Artificial intelligence (AI) has contributed substantially to the resolution of a variety of biomedical problems, including cancer, over the past decade. Deep learning, a subfield of AI that is highly flexible and supports automatic feature extractio...

Machine learning prediction of oncology drug targets based on protein and network properties.

BMC bioinformatics
BACKGROUND: The selection and prioritization of drug targets is a central problem in drug discovery. Computational approaches can leverage the growing number of large-scale human genomics and proteomics data to make in-silico target identification, r...

FHIR OWL: Transforming OWL ontologies into FHIR terminology resources.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The FHIR specification provides a mechanism to access clinical terminologies using a standard API, and many existing terminologies, such as SNOMED CT, are well supported. However, in areas such as genomics, terminologies from other domains are starti...

Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data.

Nature communications
Chromatin interaction studies can reveal how the genome is organized into spatially confined sub-compartments in the nucleus. However, accurately identifying sub-compartments from chromatin interaction data remains a challenge in computational biolog...

Big data in IBD: big progress for clinical practice.

Gut
IBD is a complex multifactorial inflammatory disease of the gut driven by extrinsic and intrinsic factors, including host genetics, the immune system, environmental factors and the gut microbiome. Technological advancements such as next-generation se...

Machine learning methods for microbiome studies.

Journal of microbiology (Seoul, Korea)
Researches on the microbiome have been actively conducted worldwide and the results have shown human gut bacterial environment significantly impacts on immune system, psychological conditions, cancers, obesity, and metabolic diseases. Thanks to the d...