Computational Approaches for Disease Gene Identification
A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".
Deadline for manuscript submissions: closed (30 June 2018) | Viewed by 56208
Special Issue Editors
Interests: systems biology; bioinformatics; protein sequence; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: bioinformatics; genetics; genomics; machine learning; ceRNA network; predictive modeling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Identification of disease genes is the foundation of medicine. Many experimental approaches have been used to screen the candidate disease genes. Genome-wide association studies (GWAS) can establish the associations between single-nucleotide polymorphism (SNP) and disease phenotypes. Clustered regularly interspaced short palindromic repeats (CRISPR) can knockdown genes and by measuring the before and after knockdown gene expression profiles and observing the phenotype of cells, their downstream genes can be investigated and their functions can be inferred. But all these experimental approaches have insurmountable barriers. For example, many of the GWAS identified SNPs locate in intergenic region and cannot be annotated to specific genes. The CRISPR knockdown may affect many genes and the cells may exhibit many irrelevant phenotypes. If you do not know which phenotypes to look at, you may miss the actual important events. Beside the limitations of experimental approaches their self, the integration of multi-data is another key problem. If the GWAS results indicate a gene is disease associated but the CRISPR result on cells indicate otherwise, how to deicide? To overcome these problems, the computational methods, such as network based prioritization of GWAS candidates, expression Quantitative Trait Loci (eQTL) regulatory network construction and analysis, machine learning based integration of multi-omics data, should be incorporated with the experimental technologies to identify the disease genes. The aim of this Special Issue is to introduce the latest developments of interdisciplinary researches of disease gene identification. Any original research and review articles related to the described topics are welcomed.
Dr. Yudong Cai
Dr. Tao Huang
Dr. Lei Chen
Guest Editors
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Keywords
- disease gene prioritization
- expression quantitative trait loci (eQTL)
- deleterious single amino acid polymorphisms (SAP) identification
- decipher the effects of gene knockout
- multi-omics data integration
- deep learning biosystem modeling
- key driver analysis
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