Artificial Intelligence–Aided Precision Medicine for COVID-19: Strategic Areas of Research and Development

Santus, Enrico; Marino, Nicola; Cirillo, Davide; Chersoni, Emmanuele; Montagud, Arnau; Chadha, Antonella Santuccione; Valencia, Alfonso; Hughes, Kevin S.; Lindvall, Charlotta

doi:10.2196/22453

Cited by 23 publications

(10 citation statements)

References 87 publications

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“…Multiple informatic approaches have been applied to the integration of population level, molecular and imaging data associated with SARS-CoV-2 infection. These include conventional interactome networks, associations with abundance trajectories with viral proteins, neural networks, deep and self-supervised learning, as well as machine learning of digital data [8,10,[16][17][18][19][20]. Each type of informatic approach has its advantages and disadvantages based on a priori knowledge, power and structure of the dataset, as well as the ability to develop de novo knowledge.…”

Section: Discussionmentioning

confidence: 99%

Bayesian Model Infers Drug Repurposing Candidates for Treatment of COVID-19

et al. 2021

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The emergence of COVID-19 progressed into a global pandemic that has functionally put the world at a standstill and catapulted major healthcare systems into an overburdened state. The dire need for therapeutic strategies to mitigate and successfully treat COVID-19 is now a public health crisis with national security implications for many countries. The current study employed Bayesian networks to a longitudinal proteomic dataset generated from Caco-2 cells transfected with SARS-CoV-2 (isolated from patients returning from Wuhan to Frankfurt). Two different approaches were employed to assess the Bayesian models, a titer-center topology analysis and a drug signature enrichment analysis. Topology analysis identified a set of proteins directly linked to the SAR-CoV2 titer, including ACE2, a SARS-CoV-2 binding receptor, MAOB and CHECK1. Aligning with the topology analysis, MAOB and CHECK1 were also identified within the enriched drug-signatures. Taken together, the data output from this network has identified nodal host proteins that may be connected to 18 chemical compounds, some already marketed, which provides an immediate opportunity to rapidly triage these assets for safety and efficacy against COVID-19.

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Section: Discussionmentioning

confidence: 99%

Bayesian Model Infers Drug Repurposing Candidates for Treatment of COVID-19

et al. 2021

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“…Automating data throughput in biomedical and clinical applications may also be useful even for relatively low demand tasks under certain circumstances; for example, automated RT-PCR data processing as implemented in (Automated RT-PCR analysis) turned out to be crucial for testing efforts during the COVID-19 pandemic ( 206 ). AI/ML modeling based on facilitated access data may indeed become a key tool to tackle with current and future pandemics ( 207 ).…”

Section: Challenges To Computational Learning In Precision Medicinementioning

confidence: 99%

Data Integration Challenges for Machine Learning in Precision Medicine

Martínez-García

Hernández‐Lemus

2022

Front. Med.

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A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.

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“…Nevertheless, this can be overcome by the use of more sophisticated diagnostic tools with superior sensitivity. In addition, using artificial intelligence we will be able to predict the optimum drug and doses required for a patient for a better effective outcome [ 76 ].…”

Section: Can We Adopt Personalized Medicine In a Pandemic?mentioning

confidence: 99%

Personalized Therapy: Can it Tame the COVID-19 Monster?

Arish

Naz

2021

Per. Med.

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SARS-CoV-2, a recently emerged zoonotic virus, has resulted in unstoppable high morbidity and mortality rates worldwide. However, due to a limited knowledge of the dynamics of the SARS-CoV-2 infection, it has been observed that the current COVID-2019 therapy has led to some clinical repercussions. We discuss the adverse effects of drugs for COVID-2019 primarily based on some clinical trials. As therapeutic efficacy and toxicity of therapy may vary due to different, genetic determinants, sex, age and the ethnic background of test subjects, hence biomarker-based personalized therapy could be more appropriate. We will share our thoughts on the current landscape of personalized therapy as a roadmap to fight against SARS-CoV-2 or another emerging pathogen.

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Artificial Intelligence–Aided Precision Medicine for COVID-19: Strategic Areas of Research and Development

Cited by 23 publications

References 87 publications

Bayesian Model Infers Drug Repurposing Candidates for Treatment of COVID-19

Bayesian Model Infers Drug Repurposing Candidates for Treatment of COVID-19

Data Integration Challenges for Machine Learning in Precision Medicine

Personalized Therapy: Can it Tame the COVID-19 Monster?

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