Abstract
In recent years, more and more omics data is generated. Even for the same samples, multiple levels of omics data can be measured in large scale. These multiscale and large-scale data could help in revealing the biological basis of complex diseases and optimizing the therapeutic strategies. Analysis of such data is very challenging since the data is inaccessible in the past and few methods are developed. In this special issue, we presented 17 novel studies about the analysis method of such complex data and their applications to interesting medical and biological questions.
Highlights
The identified common subnetworks across six brain regions suggested that inflammation of the brain nerves is one of the critical factors of Alzheimer’s disease and calcium imbalance may link several causative factors of Alzheimer’s disease
The proposed method was composed of cautious classification and data cleaning, where cautious classification was used to increase the accuracy by restricting predictions to high-confidence instances, whereas data cleaning was used to mitigate the influence of mislabeled training instances
The strategy for gene prioritization showed its superiority to conventional methods in discovering significant disease-related genes with several types of resources, while making good use of potential complementarities among available genetic resources
Summary
Editorial Integrated Analysis of Multiscale Large-Scale Biological Data for Investigating Human Disease Tao Huang,1 Lei Chen,2 Mingyue Zheng,3 and Jiangning Song4 These multiscale and large-scale data could help in revealing the biological basis of complex diseases and optimizing the therapeutic strategies. We presented 17 novel studies about the analysis method of such complex data and their applications to interesting medical and biological questions.
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