Most human diseases are inherently complex and rely on intricate gene-gene interactions to exert their pathological outcomes. This complexity means the penetrance of most disease genes is limited and variable. Moreover, the output of disease genes is context-dependent and disease etiology is far beyond the activities of individual disease genes. Hence, there is an imminent need to devise state-of-the-art systems biology and Artificial Intelligence (AI) tools that have the power to unlock main mechanisms and key players in disease development by riding on the wave of the burgeoning of high throughput sequencing technologies, including single-cell sequencing and spatial transcriptomics.
This research topic aims to stimulate open discussion and research interest to develop novel hypotheses that illuminate our mechanistic understanding of disease etiology and design hypothesis-driven systems biology or AI algorithms, tools, models, and generation of resources to promote systems paradigm of pharmacological sciences for the advancement of individualized and precision medicine.
To delve deeper into this complex field, we are interested in contributions that explore a wide range of themes, including but not limited to:
1. Innovative AI or systems biology algorithms that elucidate disease mechanisms, target identification, and drug discovery.
2. State-of-the-art system biology or AI approaches in analyzing spatial transcriptomics data.
3. Generative AI approaches to generate proteins and chemical compounds for drug discovery.
4. Novel perspectives that shift paradigms in understanding disease etiology, drug responses, and
treatment strategies.
5. Development of disease models or systems that are tailored to enhance personalized and precision medicine.
6. Advanced systems biology or AI approaches that support drug repurposing and personalized
treatment plans.
7. Creation of new data or experimental resources that facilitate future AI-based studies, geared
towards a hypothesis-driven experimental and modeling approach.
Please note: If patient data are analyzed, a comprehensive description of the patients including sex, age, diagnostic criteria, inclusion and exclusion criteria, disease stage, therapy received, comorbidities as well as additional clinical information and assessment of clinical response/effects should be included. If genetic, proteomics, metabolomics, or other omics data are analyzed, a comprehensive description of the methods and the rationale for the selection of the specific data studied should be provided. Studies related to natural compounds, herbal extracts, or traditional medicine products, are outside the scope of this Research Topic and should instead be submitted to the specialty section Ethnopharmacology.
Most human diseases are inherently complex and rely on intricate gene-gene interactions to exert their pathological outcomes. This complexity means the penetrance of most disease genes is limited and variable. Moreover, the output of disease genes is context-dependent and disease etiology is far beyond the activities of individual disease genes. Hence, there is an imminent need to devise state-of-the-art systems biology and Artificial Intelligence (AI) tools that have the power to unlock main mechanisms and key players in disease development by riding on the wave of the burgeoning of high throughput sequencing technologies, including single-cell sequencing and spatial transcriptomics.
This research topic aims to stimulate open discussion and research interest to develop novel hypotheses that illuminate our mechanistic understanding of disease etiology and design hypothesis-driven systems biology or AI algorithms, tools, models, and generation of resources to promote systems paradigm of pharmacological sciences for the advancement of individualized and precision medicine.
To delve deeper into this complex field, we are interested in contributions that explore a wide range of themes, including but not limited to:
1. Innovative AI or systems biology algorithms that elucidate disease mechanisms, target identification, and drug discovery.
2. State-of-the-art system biology or AI approaches in analyzing spatial transcriptomics data.
3. Generative AI approaches to generate proteins and chemical compounds for drug discovery.
4. Novel perspectives that shift paradigms in understanding disease etiology, drug responses, and
treatment strategies.
5. Development of disease models or systems that are tailored to enhance personalized and precision medicine.
6. Advanced systems biology or AI approaches that support drug repurposing and personalized
treatment plans.
7. Creation of new data or experimental resources that facilitate future AI-based studies, geared
towards a hypothesis-driven experimental and modeling approach.
Please note: If patient data are analyzed, a comprehensive description of the patients including sex, age, diagnostic criteria, inclusion and exclusion criteria, disease stage, therapy received, comorbidities as well as additional clinical information and assessment of clinical response/effects should be included. If genetic, proteomics, metabolomics, or other omics data are analyzed, a comprehensive description of the methods and the rationale for the selection of the specific data studied should be provided. Studies related to natural compounds, herbal extracts, or traditional medicine products, are outside the scope of this Research Topic and should instead be submitted to the specialty section Ethnopharmacology.
