Cardiovascular disease and wound healing is one of the important pathophysiological processes that affect human health. Moreover, cardiovascular disease is a leading cause of death worldwide and a significant risk factor for impaired wound healing, which have a serious impact on the quality of life of patients. It is reported that healing may be delayed and even cause complications due to poor circulation in the affected areas without proper wound care. And oxygen and nutrients delivery to the wound site is critical in improved healing outcomes. Through bioinformatics and machine learning, cardiovascular diseases on wound healing can be examined from various perspectives, including genetics, epigenetics, signaling networks, clinical manifestation, and epidemiology. Therefore, patients with cardiovascular diseases have compromised blood supply which leads to delayed wound healing, and bioinformatics and machine learning have a uniquely advantageous role in boosting data-driven research and unraveling novel methods for the prognosis, prediction, and treatment of cardiovascular diseases on wound healing.
We would like to create a forum for current advances on the impact of cardiovascular diseases on wound healing and drug discovery through “omics” technologies, computational methods in bioinformatics and machine learning. It is becoming increasingly important to utilize multi-omics approaches including transcriptomics, epigenomics, metabolomics, and proteomics in order to solve the above challenges of cardiovascular diseases-wound healing interactions. In addition, we encourage the submission of manuscripts focusing on cardiovascular insufficiency, arterial ulcers and venous ulcers in animal models, investigating the mechanisms of circulation and ulcers at cellular levels, and exploring the therapeutic treatment for cardiovascular diseases and wound healing.
For this Research Topic, we particularly encourage the submission of manuscripts dealing with any aspect of bioinformatics analyses and machine learning methods for cardiovascular diseases on wound healing, including, but not limited to:
- Circulation and ulcers, ischemic ulcers, arterial ulcers and venous ulcers
- Cardiovascular diseases and/or wound healing subtype classification
- Gene regulation, prognosis, prediction and drug response for cardiovascular diseases and/or wound healing
- Transcriptomics, epigenomics, metabolomics, and proteomics on cardiovascular diseases and/or wound healing
- Multi-omics in combination with mechanistic pharmacological studies for cardiovascular diseases and/or wound healing
We welcome article types of original research, reviews, short communications, perspectives, and commentaries of the aforementioned topics and domains. Purely in silico analyses are not within the journal’s scope.
Keywords:
wound healing, cardiovascular diseases, drug response, omics, gene regulation, supervised learning, unsupervised learning
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.
Cardiovascular disease and wound healing is one of the important pathophysiological processes that affect human health. Moreover, cardiovascular disease is a leading cause of death worldwide and a significant risk factor for impaired wound healing, which have a serious impact on the quality of life of patients. It is reported that healing may be delayed and even cause complications due to poor circulation in the affected areas without proper wound care. And oxygen and nutrients delivery to the wound site is critical in improved healing outcomes. Through bioinformatics and machine learning, cardiovascular diseases on wound healing can be examined from various perspectives, including genetics, epigenetics, signaling networks, clinical manifestation, and epidemiology. Therefore, patients with cardiovascular diseases have compromised blood supply which leads to delayed wound healing, and bioinformatics and machine learning have a uniquely advantageous role in boosting data-driven research and unraveling novel methods for the prognosis, prediction, and treatment of cardiovascular diseases on wound healing.
We would like to create a forum for current advances on the impact of cardiovascular diseases on wound healing and drug discovery through “omics” technologies, computational methods in bioinformatics and machine learning. It is becoming increasingly important to utilize multi-omics approaches including transcriptomics, epigenomics, metabolomics, and proteomics in order to solve the above challenges of cardiovascular diseases-wound healing interactions. In addition, we encourage the submission of manuscripts focusing on cardiovascular insufficiency, arterial ulcers and venous ulcers in animal models, investigating the mechanisms of circulation and ulcers at cellular levels, and exploring the therapeutic treatment for cardiovascular diseases and wound healing.
For this Research Topic, we particularly encourage the submission of manuscripts dealing with any aspect of bioinformatics analyses and machine learning methods for cardiovascular diseases on wound healing, including, but not limited to:
- Circulation and ulcers, ischemic ulcers, arterial ulcers and venous ulcers
- Cardiovascular diseases and/or wound healing subtype classification
- Gene regulation, prognosis, prediction and drug response for cardiovascular diseases and/or wound healing
- Transcriptomics, epigenomics, metabolomics, and proteomics on cardiovascular diseases and/or wound healing
- Multi-omics in combination with mechanistic pharmacological studies for cardiovascular diseases and/or wound healing
We welcome article types of original research, reviews, short communications, perspectives, and commentaries of the aforementioned topics and domains. Purely in silico analyses are not within the journal’s scope.
Keywords:
wound healing, cardiovascular diseases, drug response, omics, gene regulation, supervised learning, unsupervised learning
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.