AIMC Topic: Gene Expression Profiling

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MetaKTSP: a meta-analytic top scoring pair method for robust cross-study validation of omics prediction analysis.

Bioinformatics (Oxford, England)
MOTIVATION: Supervised machine learning is widely applied to transcriptomic data to predict disease diagnosis, prognosis or survival. Robust and interpretable classifiers with high accuracy are usually favored for their clinical and translational pot...

Network stratification analysis for identifying function-specific network layers.

Molecular bioSystems
A major challenge of systems biology is to capture the rewiring of biological functions (e.g. signaling pathways) in a molecular network. To address this problem, we proposed a novel computational framework, namely network stratification analysis (Ne...

Integrative modeling of multi-omics data to identify cancer drivers and infer patient-specific gene activity.

BMC systems biology
BACKGROUND: High throughput technologies have been used to profile genes in multiple different dimensions, such as genetic variation, copy number, gene and protein expression, epigenetics, metabolomics. Computational analyses often treat these differ...

ZeitZeiger: supervised learning for high-dimensional data from an oscillatory system.

Nucleic acids research
Numerous biological systems oscillate over time or space. Despite these oscillators' importance, data from an oscillatory system is problematic for existing methods of regularized supervised learning. We present ZeitZeiger, a method to predict a peri...

Missing value imputation for microRNA expression data by using a GO-based similarity measure.

BMC bioinformatics
BACKGROUND: Missing values are commonly present in microarray data profiles. Instead of discarding genes or samples with incomplete expression level, missing values need to be properly imputed for accurate data analysis. The imputation methods can be...

Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model.

BMC bioinformatics
BACKGROUND: A living cell has a complex, hierarchically organized signaling system that encodes and assimilates diverse environmental and intracellular signals, and it further transmits signals that control cellular responses, including a tightly con...

The feature selection bias problem in relation to high-dimensional gene data.

Artificial intelligence in medicine
OBJECTIVE: Feature selection is a technique widely used in data mining. The aim is to select the best subset of features relevant to the problem being considered. In this paper, we consider feature selection for the classification of gene datasets. G...

Microarray expression profiling and gene ontology analysis of long non-coding RNAs in spontaneously hypertensive rats and their potential roles in the pathogenesis of hypertension.

Molecular medicine reports
Long non-coding RNAs (lncRNAs) have been demonstrated to be significant in numerous biological processes. Hypertension is a form of cardiovascular disease with at least one billion cases worldwide. The present study sought to compare the differential...