AI Medical Compendium Topic:
Genomics

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Multifactorial Deep Learning Reveals Pan-Cancer Genomic Tumor Clusters with Distinct Immunogenomic Landscape and Response to Immunotherapy.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: Tumor genomic features have been of particular interest because of their potential impact on the tumor immune microenvironment and response to immunotherapy. Due to the substantial heterogeneity, an integrative approach incorporating diverse...

Harnessing big 'omics' data and AI for drug discovery in hepatocellular carcinoma.

Nature reviews. Gastroenterology & hepatology
Hepatocellular carcinoma (HCC) is the most common form of primary adult liver cancer. After nearly a decade with sorafenib as the only approved treatment, multiple new agents have demonstrated efficacy in clinical trials, including the targeted thera...

DeepMF: deciphering the latent patterns in omics profiles with a deep learning method.

BMC bioinformatics
BACKGROUND: With recent advances in high-throughput technologies, matrix factorization techniques are increasingly being utilized for mapping quantitative omics profiling matrix data into low-dimensional embedding space, in the hope of uncovering ins...

JCDB: a comprehensive knowledge base for Jatropha curcas, an emerging model for woody energy plants.

BMC genomics
BACKGROUND: Jatropha curcas is an oil-bearing plant, and has seeds with high oil content (~ 40%). Several advantages, such as easy genetic transformation and short generation duration, have led to the emergence of J. curcas as a model for woody energ...

Representation learning of genomic sequence motifs with convolutional neural networks.

PLoS computational biology
Although convolutional neural networks (CNNs) have been applied to a variety of computational genomics problems, there remains a large gap in our understanding of how they build representations of regulatory genomic sequences. Here we perform systema...

DeepSV: accurate calling of genomic deletions from high-throughput sequencing data using deep convolutional neural network.

BMC bioinformatics
BACKGROUND: Calling genetic variations from sequence reads is an important problem in genomics. There are many existing methods for calling various types of variations. Recently, Google developed a method for calling single nucleotide polymorphisms (...

Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review.

Psychiatry research
Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is th...

Discovery and annotation of novel microRNAs in the porcine genome by using a semi-supervised transductive learning approach.

Genomics
Despite the broad variety of available microRNA (miRNA) prediction tools, their application to the discovery and annotation of novel miRNA genes in domestic species is still limited. In this study we designed a comprehensive pipeline (eMIRNA) for miR...

Afann: bias adjustment for alignment-free sequence comparison based on sequencing data using neural network regression.

Genome biology
Alignment-free methods, more time and memory efficient than alignment-based methods, have been widely used for comparing genome sequences or raw sequencing samples without assembly. However, in this study, we show that alignment-free dissimilarity ca...