Research Topic

Artificial Intelligence Linking Phenotypes to Genomic Features: Volume 2 - Humans

About this Research Topic

This Research Topic is part of the series Artificial Intelligence Linking Phenotypes to Genomic Features: Towards a Convergence Research: Volume 1 - Plants

One of the central goals in plant and human biology is to understand how the phenotype of an organism is encoded in its genome. Agricultural and medical genetics share a dependence on the phenotype-genotype map (G-P map). Phenomics and genomics, the comprehensive study of phenotypes and genotypes, are therefore essential to understanding biology. Despite the advances in knowledge that sequencing technologies and analysis platforms have brought to plant and human biology, awareness is growing that many phenotypes are highly polygenic and susceptible to genetic and environmental interactions. Prime examples are human diseases and plant responses to environmental stress. Therefore, our understanding of all the genetic factors that influence these traits remains incomplete. The integration of phenomic data is critically needed, yet it adds a new level of complexity.

To overcome these barriers and integrate genomic and phenomic big data, new Artificial intelligence (AI) tools will take center stage. AI, which encompasses machine learning (ML), deep learning (DL) reinforcement learning (RL) and ensemble learning (EL), is the scientific discipline that uses computer algorithms to learn from large, highly heterogenous and complex data, to help identify patterns in data, and make predictions and interpretations.

The use of AI is becoming increasingly attractive to the plant and forest tree breeding, precision agriculture/forestry and precision medicine industry. AI and exascale biology are no longer concepts confined to the pages of futuristic science fiction novels; they are here now and are advancing rapidly. Globally, researchers must work collectively to provide insights into the genetic factors that influence common and rare human diseases and plant environmental stress responses and ensure breakthroughs in research and innovation that will help reach agriculture and health Sustainable Development Goals.

This Research Topic addresses these challenges from a multitude of perspectives. This includes insights into the genetic bases underlying quantitative phenotypic differences in plant and humans and what is needed to understand the genotype-to-phenotype problem on a broader scale by applying AI.

The following topics are therefore covered here:

Human and healthcare

- Big data, ML, DL, RL, EL for image recognition and healthcare research. How will phenomic data be combined and used with genomic and other layers of omics data to better understand and treat rare and common human disease by applying AI?
- ML, DL, RL and EL multiomics data mining to enable efficient genome editing.
- ML, DL, RL and EL for genomic prediction of complex human traits.
- AI for better understanding of the genetic causes of rare and common human diseases and ways to treat them.
- How will phenomic data be combined and used with genomic and other layers of omics data to better understand and treat human disease?
- Design models that are inherently interpretable.
- Explainable AI applications where interpretable models could potentially replace black box models in healthcare and clinical medicine.


Keywords: Explainable Artificial Intelligence, Precision Medicine, Sustainability, Multiomics Big Data, Intelligent Agriculture/Forestry


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.

This Research Topic is part of the series Artificial Intelligence Linking Phenotypes to Genomic Features: Towards a Convergence Research: Volume 1 - Plants

One of the central goals in plant and human biology is to understand how the phenotype of an organism is encoded in its genome. Agricultural and medical genetics share a dependence on the phenotype-genotype map (G-P map). Phenomics and genomics, the comprehensive study of phenotypes and genotypes, are therefore essential to understanding biology. Despite the advances in knowledge that sequencing technologies and analysis platforms have brought to plant and human biology, awareness is growing that many phenotypes are highly polygenic and susceptible to genetic and environmental interactions. Prime examples are human diseases and plant responses to environmental stress. Therefore, our understanding of all the genetic factors that influence these traits remains incomplete. The integration of phenomic data is critically needed, yet it adds a new level of complexity.

To overcome these barriers and integrate genomic and phenomic big data, new Artificial intelligence (AI) tools will take center stage. AI, which encompasses machine learning (ML), deep learning (DL) reinforcement learning (RL) and ensemble learning (EL), is the scientific discipline that uses computer algorithms to learn from large, highly heterogenous and complex data, to help identify patterns in data, and make predictions and interpretations.

The use of AI is becoming increasingly attractive to the plant and forest tree breeding, precision agriculture/forestry and precision medicine industry. AI and exascale biology are no longer concepts confined to the pages of futuristic science fiction novels; they are here now and are advancing rapidly. Globally, researchers must work collectively to provide insights into the genetic factors that influence common and rare human diseases and plant environmental stress responses and ensure breakthroughs in research and innovation that will help reach agriculture and health Sustainable Development Goals.

This Research Topic addresses these challenges from a multitude of perspectives. This includes insights into the genetic bases underlying quantitative phenotypic differences in plant and humans and what is needed to understand the genotype-to-phenotype problem on a broader scale by applying AI.

The following topics are therefore covered here:

Human and healthcare

- Big data, ML, DL, RL, EL for image recognition and healthcare research. How will phenomic data be combined and used with genomic and other layers of omics data to better understand and treat rare and common human disease by applying AI?
- ML, DL, RL and EL multiomics data mining to enable efficient genome editing.
- ML, DL, RL and EL for genomic prediction of complex human traits.
- AI for better understanding of the genetic causes of rare and common human diseases and ways to treat them.
- How will phenomic data be combined and used with genomic and other layers of omics data to better understand and treat human disease?
- Design models that are inherently interpretable.
- Explainable AI applications where interpretable models could potentially replace black box models in healthcare and clinical medicine.


Keywords: Explainable Artificial Intelligence, Precision Medicine, Sustainability, Multiomics Big Data, Intelligent Agriculture/Forestry


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.

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Submission Deadlines

30 June 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

30 June 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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