About this Research Topic
Protein post translational modifications (PTMs) increase the functional diversity of the proteome by means of the covalent addition of functional groups or proteins, the regulation of proteolytic cleavage of subunits, or the degradation of the whole protein. These modifications - including phosphorylation, glycosylation, ubiquitination, nitrosylation, methylation, acetylation, lipidylation, and proteolysis - affect almost all aspects of normal cell biology and pathogenesis. Therefore, the identification and understanding of PTMs is of vital importance in cell biology and research into disease treatment and prevention. For instance, the analysis of proteins and their PTMs is particularly important for the study of heart disease, cancer, neurodegenerative diseases and diabetes.
With the development of immunoprecipitation and mass spectrometry technologies for PTMs quantitative detection, the data of post-translational modification of proteins continues to grow. Computational methods for analyzing the ever-increasing data on PTMs is booming. Various machine learning methods have been used to compute and analyze PTMs. These methods however are subject to many challenges (e.g: data imbalance, simultaneous analysis and prediction of multiple modification sites, and association analysis of related diseases).
We welcome manuscripts dealing with PTMs and their association with disease using machine learning methods. The following topics will be considered for publication:
1. PTMs sites prediction.
2. Analysis and prediction of enzymes related to proteins chemical modification processes.
3. Proteomic analysis of PTMs
4. Analysis and prediction of properties related to PTMs, including folding, activity, and function.
5. The effects of genetic variation on PTMs.
6. Databases and software that can be expected to provide novel insights about PTMs.
7. Generation of generic and specialized high-quality benchmark datasets used for PTMs using machine learning methods.
8. New approached to address data imbalance in PTM analysis.
9. Changes in protein-protein interaction networks, protein-DNA interactions, and protein-RNA interactions due to PTMs.
10. Diseases and bio-markers associated with PTMs
11. Construction and interpretation of PTMs to inform precision cancer medicine and other medicines.
The Editors acknowledge Dr. Zezhong Ye, Harvard Medical School, who contributed to the preparation of the proposal of this Research Topic.
With the development of immunoprecipitation and mass spectrometry technologies for PTMs quantitative detection, the data of post-translational modification of proteins continues to grow. Computational methods for analyzing the ever-increasing data on PTMs is booming. Various machine learning methods have been used to compute and analyze PTMs. These methods however are subject to many challenges (e.g: data imbalance, simultaneous analysis and prediction of multiple modification sites, and association analysis of related diseases).
We welcome manuscripts dealing with PTMs and their association with disease using machine learning methods. The following topics will be considered for publication:
1. PTMs sites prediction.
2. Analysis and prediction of enzymes related to proteins chemical modification processes.
3. Proteomic analysis of PTMs
4. Analysis and prediction of properties related to PTMs, including folding, activity, and function.
5. The effects of genetic variation on PTMs.
6. Databases and software that can be expected to provide novel insights about PTMs.
7. Generation of generic and specialized high-quality benchmark datasets used for PTMs using machine learning methods.
8. New approached to address data imbalance in PTM analysis.
9. Changes in protein-protein interaction networks, protein-DNA interactions, and protein-RNA interactions due to PTMs.
10. Diseases and bio-markers associated with PTMs
11. Construction and interpretation of PTMs to inform precision cancer medicine and other medicines.
The Editors acknowledge Dr. Zezhong Ye, Harvard Medical School, who contributed to the preparation of the proposal of this Research Topic.
Keywords: Machine learning, cancer disease, protein chemical modification, data imbalance, protein function, protein-protein interaction, protein post-translational modification
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
