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AIME: Autoencoder-based integrative multi-omics data embedding that allows for confounder adjustments.
PLoS computational biology
Jan 26, 2022
In the integrative analyses of omics data, it is often of interest to extract data representation from one data type that best reflect its relations with another data type. This task is traditionally fulfilled by linear methods such as canonical corr...
Evaluation of deep convolutional neural networks for in situ hybridization gene expression image representation.
PloS one
Jan 24, 2022
High resolution in situ hybridization (ISH) images of the brain capture spatial gene expression at cellular resolution. These spatial profiles are key to understanding brain organization at the molecular level. Previously, manual qualitative scoring ...
A neural network-based method for exhaustive cell label assignment using single cell RNA-seq data.
Scientific reports
Jan 18, 2022
The fast-advancing single cell RNA sequencing (scRNA-seq) technology enables researchers to study the transcriptome of heterogeneous tissues at a single cell level. The initial important step of analyzing scRNA-seq data is usually to accurately annot...
Accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning.
Scientific reports
Jan 7, 2022
Atopic dermatitis (AD) is a common skin disease in childhood whose diagnosis requires expertise in dermatology. Recent studies have indicated that host genes-microbial interactions in the gut contribute to human diseases including AD. We sought to de...
sRNARFTarget: a fast machine-learning-based approach for transcriptome-wide sRNA target prediction.
RNA biology
Dec 31, 2021
Bacterial small regulatory RNAs (sRNAs) are key regulators of gene expression in many processes related to adaptive responses. A multitude of sRNAs have been identified in many bacterial species; however, their function has yet to be elucidated. A ke...
Identifying Cancer Subtypes Using a Residual Graph Convolution Model on a Sample Similarity Network.
Genes
Dec 27, 2021
Cancer subtype classification helps us to understand the pathogenesis of cancer and develop new cancer drugs, treatment from which patients would benefit most. Most previous studies detect cancer subtypes by extracting features from individual sample...
A Pan-Cancer Analysis of Predictive Methylation Signatures of Response to Cancer Immunotherapy.
Frontiers in immunology
Dec 9, 2021
Recently, tumor immunotherapy based on immune checkpoint inhibitors (ICI) has been introduced and widely adopted for various tumor types. Nevertheless, tumor immunotherapy has a few drawbacks, including significant uncertainty of outcome, the possibi...
WMLRR: A Weighted Multi-View Low Rank Representation to Identify Cancer Subtypes From Multiple Types of Omics Data.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 8, 2021
The identification of cancer subtypes is of great importance for understanding the heterogeneity of tumors and providing patients with more accurate diagnoses and treatments. However, it is still a challenge to effectively integrate multiple omics da...
An Enhanced Random Forests Approach to Predict Heart Failure From Small Imbalanced Gene Expression Data.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 8, 2021
Myocardial infarctions and heart failure are the cause of more than 17 million deaths annually worldwide. ST-segment elevation myocardial infarctions (STEMI) require timely treatment, because delays of minutes have serious clinical impacts. Machine l...
Unsupervised Learning Framework With Multidimensional Scaling in Predicting Epithelial-Mesenchymal Transitions.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 8, 2021
Clustering tumor metastasis samples from gene expression data at the whole genome level remains an arduous challenge, in particular, when the number of experimental samples is small and the number of genes is huge. We focus on the prediction of the e...
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