The development of Artificial Intelligence (AI) pushes the boundaries of new computing paradigms to become actual realities for many science and engineering challenges. The delicacy and agility of a computing paradigm do not mean anything when we cannot use it to create values and solve problems. Bioinformatics and drug repurposing provide excellent playgrounds and test fields for AI to exercise its maximum capacities. Bioinformatics is an interdisciplinary field that covers a broad spectrum of studies including the fields of biology, computer science, information science, and statistics. The nature of the field intrinsically attracts complicated research and application problems. Drug repurposing (DR), also known as drug reprofiling or drug reusing, has been a trending field of research since 2004, which attracts the attention of researchers from medicine, pharmacy, biochemistry, and other related fields. Drug reposition is a process of discovering novel uses and therapeutic applications of existing drugs.
This Research Topic aims to cover recent advancements in bioinformatics with a special focus on using artificial intelligence in service of the analysis and interpretation of DNA sequences, DNA structures, various genomic data, drug target affinity/interaction, and protein ligand interaction. The objective is to provide a comprehensive and the latest collection of research and experimental works in these fields. The collection endeavors to tackle artificial intelligence from a less conventional aspect. Rather than cover AI and machine learning capabilities in more typical fields like engineering, we would like to cover themes in some unique fields, i.e., computational genomics and bioinformatics drug repurposing.
Themes covered in this Research Topic include:
• Machine learning applications in:
o DNA structure analysis
o DNA sequence analysis
o Pattern finding in genomic data
• AI methods and applications in:
o Drug discovery
o Drug finding
o Drug molecule and disease protein mapping
o Various binding affinity prediction
o Computational biology
o Bioinformatics
• Innovative drug encapsulation and delivery methods and mechanisms
• DNA computing
• DNA-based computational models
The development of Artificial Intelligence (AI) pushes the boundaries of new computing paradigms to become actual realities for many science and engineering challenges. The delicacy and agility of a computing paradigm do not mean anything when we cannot use it to create values and solve problems. Bioinformatics and drug repurposing provide excellent playgrounds and test fields for AI to exercise its maximum capacities. Bioinformatics is an interdisciplinary field that covers a broad spectrum of studies including the fields of biology, computer science, information science, and statistics. The nature of the field intrinsically attracts complicated research and application problems. Drug repurposing (DR), also known as drug reprofiling or drug reusing, has been a trending field of research since 2004, which attracts the attention of researchers from medicine, pharmacy, biochemistry, and other related fields. Drug reposition is a process of discovering novel uses and therapeutic applications of existing drugs.
This Research Topic aims to cover recent advancements in bioinformatics with a special focus on using artificial intelligence in service of the analysis and interpretation of DNA sequences, DNA structures, various genomic data, drug target affinity/interaction, and protein ligand interaction. The objective is to provide a comprehensive and the latest collection of research and experimental works in these fields. The collection endeavors to tackle artificial intelligence from a less conventional aspect. Rather than cover AI and machine learning capabilities in more typical fields like engineering, we would like to cover themes in some unique fields, i.e., computational genomics and bioinformatics drug repurposing.
Themes covered in this Research Topic include:
• Machine learning applications in:
o DNA structure analysis
o DNA sequence analysis
o Pattern finding in genomic data
• AI methods and applications in:
o Drug discovery
o Drug finding
o Drug molecule and disease protein mapping
o Various binding affinity prediction
o Computational biology
o Bioinformatics
• Innovative drug encapsulation and delivery methods and mechanisms
• DNA computing
• DNA-based computational models
