AIMC Topic: Transcriptome
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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...
A Convolutional Deep Clustering Framework for Gene Expression Time Series.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 8, 2021
The functional or regulatory processes within the cell are explicitly governed by the expression levels of a subset of its genes. Gene expression time series captures activities of individual genes over time and aids revealing underlying cellular dyn...
DMFLDA: A Deep Learning Framework for Predicting lncRNA-Disease Associations.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 8, 2021
A growing amount of evidence suggests that long non-coding RNAs (lncRNAs) play important roles in the regulation of biological processes in many human diseases. However, the number of experimentally verified lncRNA-disease associations is very limite...
Machine Learning and Bioinformatics Framework Integration to Potential Familial DCM-Related Markers Discovery.
Genes
Dec 2, 2021
OBJECTIVES: Dilated cardiomyopathy (DCM) is characterized by a specific transcriptome. Since the DCM molecular network is largely unknown, the aim was to identify specific disease-related molecular targets combining an original machine learning (ML) ...
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