BACKGROUND: The topological landscape of gene interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, it is still a challenging task to aggregate heterogeneous biological information...
Myasthenia gravis (MG) is an autoimmune disease. In recent years, considerable evidence has indicated that Gene Ontology (GO) functions, especially GO-biological processes, have important effects on the mechanisms and treatments of different diseases...
MiRNAs and proteins play important roles in different stages of breast tumor development and serve as biomarkers for the early diagnosis of breast cancer. A new algorithm that combines machine learning algorithms and multilayer complex network analys...
Machine learning (ML) encompasses statistical methods that learn to identify patterns in complex datasets. Here, I review application areas in plant-pathogen interactions that have recently benefited from ML, such as disease monitoring, the discovery...
Systems biology requires not only genome-scale data but also methods to integrate these data into interpretable models. Previously, we developed approaches that organize omics data into a structured hierarchy of cellular components and pathways, call...
Measuring conditional relatedness between a pair of genes is a fundamental technique and still a significant challenge in computational biology. Such relatedness can be assessed by gene expression similarities while suffering high false discovery rat...
BACKGROUND: Rheumatoid arthritis (RA) and osteoarthritis (OA) were two major types of joint diseases. This study aimed to explore the mechanism underlying OA and RA and analyze their difference by integrated analysis of multiple gene expression data ...
IEEE/ACM transactions on computational biology and bioinformatics
Feb 4, 2019
Variable selection has attracted more attention in big data and machine learning fields. In high dimensional data analysis, many relevant variables or variable groups are widely found. For example, people pay more interests to biological pathway or r...
IEEE transactions on neural networks and learning systems
Jan 16, 2019
This paper deals with the finite-horizon quantized H state estimation problem for a class of discrete time-varying genetic regulatory networks with quantization effects under stochastic communication protocols (SCPs). To better reflect the data-drive...
The rapid advancements in high-throughput techniques have fueled large-scale production of biological data at very affordable costs. Some of these techniques are microarrays and next-generation sequencing that provide genome level insight of living c...
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