State-of-the-art hypothesis-driven systems pharmacology and artificial intelligence approaches to decipher disease complexity

The etiology of most human diseases is complex. For example, in cancer, metabolic disorders, and neurodegenerative diseases rely on intricate gene-gene interactions to exert their pathological outcomes. This complexity means the penetrance of most disease genes is both limited and variable. Moreover, the output of disease genes is context-dependent and disease etiology is far beyond the activities of individual disease genes. Further, the identification and targeting of individual disease genes is not enough to explain and effectively identify therapeutic targets to cure disease. With the burgeoning of high throughput sequencing technologies, including single cell sequencing, the past decade has provided unprecedented opportunities to unravel the interplay between genes across different omics layers and investigate their role in disease development.

As more and more multi-omics, biological and metadata are generated, there is an urgent need for developing innovative hypothesis-driven systems biology and artificial intelligence (AI) pipelines that identify and analyze disease etiology from a new lens. Using hypothesis-driven systems biology and/or artificial intelligence (AI) algorithms combined with data integration can shed new light on how the same disease genes play different pathological roles and assess their relative pharmacological importance in individual patients. Other pertinent challenges include how drug candidates can be repurposed to the right patient and to advance therapeutic designs with sustained treatment outcomes.

This research topic aims to stimulate 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.

We welcome submissions of original research, perspective, review, and mini-review that focus on pharmacological science, disease models, disease gene or pathway characterization, and drug discovery that harness the power of state-of-the-art hypothesis-driven systems biology, network biology, and AI algorithms to decipher the complexity of disease etiology and identification of targetable disease genes. Topics of interest include but are not limited to:

• Innovation of novel hypothesis-driven systems biology or AI algorithms to facilitate the understanding of disease development, target identification, and drug discovery

• Novel perspectives that provide a paradigm shift in medical sciences, including disease etiology, drug discovery, and treatment design

• Development of disease models or systems that incorporate mechanistic conceptual models to promote individualized and precision medicine

• Novel systems biology or AI perspectives that enable drug repurposing and/or individualized medicine

• Generation of data or experimental resources based on hypothesis-driven experimental design or modeling to enable AI-based studies in the near future

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.