AIMC Topic: Genomics

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A Cancer Survival Prediction Method Based on Graph Convolutional Network.

IEEE transactions on nanobioscience
BACKGROUND AND OBJECTIVE: Cancer, as the most challenging part in the human disease history, has always been one of the main threats to human life and health. The high mortality of cancer is largely due to the complexity of cancer and the significant...

Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network.

BMC bioinformatics
BACKGROUND: Understanding the phenotypic drug response on cancer cell lines plays a vital role in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data for researchers in phenotypic...

Visualizing complex feature interactions and feature sharing in genomic deep neural networks.

BMC bioinformatics
BACKGROUND: Visualization tools for deep learning models typically focus on discovering key input features without considering how such low level features are combined in intermediate layers to make decisions. Moreover, many of these methods examine ...

Beyond the hype of big data and artificial intelligence: building foundations for knowledge and wisdom.

BMC medicine
Big data, coupled with the use of advanced analytical approaches, such as artificial intelligence (AI), have the potential to improve medical outcomes and population health. Data that are routinely generated from, for example, electronic medical reco...

PolyCRACKER, a robust method for the unsupervised partitioning of polyploid subgenomes by signatures of repetitive DNA evolution.

BMC genomics
BACKGROUND: Our understanding of polyploid genomes is limited by our inability to definitively assign sequences to a specific subgenome without extensive prior knowledge like high resolution genetic maps or genome sequences of diploid progenitors. In...

Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models.

BMC bioinformatics
BACKGROUND: In spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance bet...

Estimation of allele-specific fitness effects across human protein-coding sequences and implications for disease.

Genome research
A central challenge in human genomics is to understand the cellular, evolutionary, and clinical significance of genetic variants. Here, we introduce a unified population-genetic and machine-learning model, called inear llele-pecific election nferenc ...

Block Forests: random forests for blocks of clinical and omics covariate data.

BMC bioinformatics
BACKGROUND: In the last years more and more multi-omics data are becoming available, that is, data featuring measurements of several types of omics data for each patient. Using multi-omics data as covariate data in outcome prediction is both promisin...