AIMC Topic: Transcriptome
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Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas.
PLoS computational biology
Feb 20, 2019
Based on morphology it is often challenging to distinguish between the many different soft tissue sarcoma subtypes. Moreover, outcome of disease is highly variable even between patients with the same disease. Machine learning on transcriptome sequenc...
Novel Regularization Method for Biomarker Selection and Cancer Classification.
IEEE/ACM transactions on computational biology and bioinformatics
Feb 4, 2019
Variable selection has attracted more attention in big data and machine learning fields. In high dimensional data analysis, many relevant variables or variable groups are widely found. For example, people pay more interests to biological pathway or r...
Predicting drug response of tumors from integrated genomic profiles by deep neural networks.
BMC medical genomics
Jan 31, 2019
BACKGROUND: The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent study screened for the response of a thousand human cancer ...
A Computational Framework for Genome-wide Characterization of the Human Disease Landscape.
Cell systems
Jan 23, 2019
A key challenge for the diagnosis and treatment of complex human diseases is identifying their molecular basis. Here, we developed a unified computational framework, URSA (Unveiling RNA Sample Annotation for Human Diseases), that leverages machine le...
De novo assembly of Agave sisalana transcriptome in response to drought stress provides insight into the tolerance mechanisms.
Scientific reports
Jan 23, 2019
Agave, monocotyledonous succulent plants, is endemic to arid regions of North America, exhibiting exceptional tolerance to their xeric environments. They employ various strategies to overcome environmental constraints, such as crassulacean acid metab...
Identification of tissue-specific tumor biomarker using different optimization algorithms.
Genes & genomics
Dec 8, 2018
BACKGROUND: Identification of differentially expressed genes, i.e., genes whose transcript abundance level differs across different biological or physiological conditions, was indeed a challenging task. However, the inception of transcriptome sequenc...
A deep learning based method for large-scale classification, registration, and clustering of in-situ hybridization experiments in the mouse olfactory bulb.
Journal of neuroscience methods
Dec 6, 2018
BACKGROUND: The Allen Mouse Brain Atlas allows study of the brain's molecular anatomy at cellular scale, for thousands genes. To fully leverage this resource, one must register histological images of brain tissue - a task made challenging by the brai...
Found In Translation: a machine learning model for mouse-to-human inference.
Nature methods
Nov 26, 2018
Cross-species differences form barriers to translational research that ultimately hinder the success of clinical trials, yet knowledge of species differences has yet to be systematically incorporated in the interpretation of animal models. Here we pr...
Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy.
Scientific reports
Nov 6, 2018
Precision or personalized cancer medicine is a clinical approach that strives to customize therapies based upon the genomic profiles of individual patient tumors. Machine learning (ML) is a computational method particularly suited to the establishmen...
Integrating network topology, gene expression data and GO annotation information for protein complex prediction.
Journal of bioinformatics and computational biology
Oct 30, 2018
The prediction of protein complexes based on the protein interaction network is a fundamental task for the understanding of cellular life as well as the mechanisms underlying complex disease. A great number of methods have been developed to predict p...
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