AIMC Topic: Transcriptome
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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...
A neural network based model effectively predicts enhancers from clinical ATAC-seq samples.
Scientific reports
Oct 30, 2018
Enhancers are cis-acting sequences that regulate transcription rates of their target genes in a cell-specific manner and harbor disease-associated sequence variants in cognate cell types. Many complex diseases are associated with enhancer malfunction...
Comparative Evaluation of Machine Learning Strategies for Analyzing Big Data in Psychiatry.
International journal of molecular sciences
Oct 29, 2018
The requirement of innovative big data analytics has become a critical success factor for research in biological psychiatry. Integrative analyses across distributed data resources are considered essential for untangling the biological complexity of m...
Heterogeneous Domain Adaptation for IHC Classification of Breast Cancer Subtypes.
IEEE/ACM transactions on computational biology and bioinformatics
Oct 24, 2018
Increasingly, multiple parallel omics datasets are collected from biological samples. Integrating these datasets for classification is an open area of research. Additionally, whilst multiple datasets may be available for the training samples, future ...
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