ArticleGenes2018
A Computational Method for Classifying Different Human Tissues with Quantitatively Tissue-Specific Expressed Genes.
Article in Genes, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
6 citing papers in PubMed.
- Article
- Article
- Identifying Cell-Type Specific Genes and Expression Rules Based on Single-Cell Transcriptomic Atlas Data.Frontiers in bioengineering and biotechnology · 2020Article
- Identifying Methylation Pattern and Genes Associated with Breast Cancer Subtypes.International journal of molecular sciences · 2019Article
- Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms.International journal of molecular sciences · 2019Article
- Identifying Microbiota Signature and Functional Rules Associated With Bacterial Subtypes in Human Intestine.Frontiers in genetics · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Tissue-specific gene expression has long been recognized as a crucial key for understanding tissue development and function. Efforts have been made in the past decade to identify tissue-specific expression profiles, such as the Human Proteome Atlas and FANTOM5. However, these studies mainly focused on "qualitatively tissue-specific expressed genes" which are highly enriched in one or a group of tissues but paid less attention to "quantitatively tissue-specific expressed genes", which are expressed in all or most tissues but with differential expression levels. In this study, we applied machine learning algorithms to build a computational method for identifying "quantitatively tissue-specific expressed genes" capable of distinguishing 25 human tissues from their expression patterns. Our results uncovered the expression of 432 genes as optimal features for tissue classification, which were obtained with a Matthews Correlation Coefficient (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.