Machine learning and artificial intelligence in the service of medicine: Necessity or potentiality?

Alsuliman, Tamim; Humaidan, Dania; Sliman, Layth

doi:10.1016/j.retram.2020.01.002

Cited by 67 publications

(33 citation statements)

References 66 publications

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“…Machine learning (ML) approaches have allowed biologists to uncover the underlying biology of large-scale omics datasets. ML has started to make its presence felt in the field of biomedical sciences, and it has helped scientists understand many human diseases (Ahmed et al, 2020;Alsuliman et al, 2020;Ben-Israel et al, 2020;Hofer et al, 2020;Wilkinson et al, 2020) ranging from cancers (Arif et al, 2020;Chaurasia and Pal, 2020;Jiang et al, 2020) to Ebola (Colubri et al, 2019) as well as to design drugs and study their resistance (Nápoles et al, 2014). ML has also been applied to understand the multifarious aspects of COVID-19 (Elaziz et al, 2020;Lalmuanawma et al, 2020;Togaçar et al, 2020;Vaid et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Proteomics and Machine Learning Approaches Reveal a Set of Prognostic Markers for COVID-19 Severity With Drug Repurposing Potential

et al. 2021

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The pestilential pathogen SARS-CoV-2 has led to a seemingly ceaseless pandemic of COVID-19. The healthcare sector is under a tremendous burden, thus necessitating the prognosis of COVID-19 severity. This in-depth study of plasma proteome alteration provides insights into the host physiological response towards the infection and also reveals the potential prognostic markers of the disease. Using label-free quantitative proteomics, we performed deep plasma proteome analysis in a cohort of 71 patients (20 COVID-19 negative, 18 COVID-19 non-severe, and 33 severe) to understand the disease dynamics. Of the 1200 proteins detected in the patient plasma, 38 proteins were identified to be differentially expressed between non-severe and severe groups. The altered plasma proteome revealed significant dysregulation in the pathways related to peptidase activity, regulated exocytosis, blood coagulation, complement activation, leukocyte activation involved in immune response, and response to glucocorticoid biological processes in severe cases of SARS-CoV-2 infection. Furthermore, we employed supervised machine learning (ML) approaches using a linear support vector machine model to identify the classifiers of patients with non-severe and severe COVID-19. The model used a selected panel of 20 proteins and classified the samples based on the severity with a classification accuracy of 0.84. Putative biomarkers such as angiotensinogen and SERPING1 and ML-derived classifiers including the apolipoprotein B, SERPINA3, and fibrinogen gamma chain were validated by targeted mass spectrometry-based multiple reaction monitoring (MRM) assays. We also employed an in silico screening approach against the identified target proteins for the therapeutic management of COVID-19. We shortlisted two FDA-approved drugs, namely, selinexor and ponatinib, which showed the potential of being repurposed for COVID-19 therapeutics. Overall, this is the first most comprehensive plasma proteome investigation of COVID-19 patients from the Indian population, and provides a set of potential biomarkers for the disease severity progression and targets for therapeutic interventions.

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Section: Introductionmentioning

confidence: 99%

Proteomics and Machine Learning Approaches Reveal a Set of Prognostic Markers for COVID-19 Severity With Drug Repurposing Potential

et al. 2021

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“…These efforts may also benefit from new technologies, including molecular biology and radiology techniques, but maybe also from artificial intelligence medical applications [84].…”

Section: Resultsmentioning

confidence: 99%

COVID-19 paraclinical diagnostic tools: Updates and future trends

Alsuliman

Sulaiman

Ismail

et al. 2020

Current Research in Translational Medicine

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Motivation: COVID-19 is one of the most widely affecting pandemics. As for many respiratory virusescaused diseases, diagnosis of COVID-19 relies on two main compartments: clinical and paraclinical diagnostic criteria. Rapid and accurate diagnosis is vital in such a pandemic. On one side, rapidity may enhance management effectiveness, while on the other, coupling efficiency and less costly procedures may permit more effective community-scale management. Methodology and main structure: In this review, we shed light on the most used and the most validated diagnostic tools. Furthermore, we intend to include few under-development techniques that may be potentially useful in this context. The practical intent of our work is to provide clinicians with a realistic summarized review of the essential elements in the applied paraclinical diagnosis of COVID-19.

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“…Artificial Intelligence is viewed as a revolution in this information era occurring all around the world and it possesses immense application. The introduction of information technology in the health sector has advanced the field in many aspects (Alsuliman et al, 2020). First, we will define the technical terms involved in the field of Artificial intelligence.…”

Section: Artificial Intelligence and Machine Learningmentioning

confidence: 99%

Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic

Elavarasan¹,

Pugazhendhi

2020

Science of The Total Environment

257

209

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The technology shift occurred in society during the COVID-19 pandemic and its influence. • Technology-based strategies in controlling the pandemic and to support the society. • Key technologies include Artificial Intelligence and Machine learning, supply chain, communication and surveillance. • Potential roles of IoT, Drone &Robotic technologies and mobile applications. • Influence of existing technologies, challenges and future impacts on society.

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Machine learning and artificial intelligence in the service of medicine: Necessity or potentiality?

Cited by 67 publications

References 66 publications

Proteomics and Machine Learning Approaches Reveal a Set of Prognostic Markers for COVID-19 Severity With Drug Repurposing Potential

Proteomics and Machine Learning Approaches Reveal a Set of Prognostic Markers for COVID-19 Severity With Drug Repurposing Potential

COVID-19 paraclinical diagnostic tools: Updates and future trends

Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic

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