2020-06-30 Review Note

tittle: Artificial intelligence and machine learning to fight COVID-19
author: Ahmad Alimadadi, Sachin Aryal, Ishan Manandhar, Patricia B. Munroe, Bina Joe, Xi Cheng
reference link: 10.1152/physiolgenomics.00029.2020

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Abstract

Within a short period of time since COVID-19 outbreak, advanced machine learing techniques have been used in several fields:

Figure from review: Appcation of artificial intelligence and machine learning in the fight against COVID-19.

1. Taxonomic classification of COVID-19 genomes

The paper (1) identifies an intrinsic COVID-19 virus genomic signature and uses it together with a machine learning-based alignment-free approach for an ultra-fast, scalable, and highly accurate classification of whole COVID-19 virus genomes.

2. CRISPR-based COVID-19 detection assay

A group (2) provide assay designs and experimental resources, for use with CRISPR-based nucleic acid detection, that could be valuable for ongoing surveillance.

3. Survival prediction of severe COVID-19 patients

A study (3) use machine learning tools selected three biomarkers that predict the survival of individual patients with more than 90% accuracy: lactic dehydrogenase (LDH), lymphocyte and high-sensitivity C-reactive protein (hs-CRP) to quickly predict patients at the highest risk.

4. Discovering potential drug candidates against COVID-19

AlphaFold (4), which a deep learning system developed by Google DeepMind, has released predicted protein structures associated with COVID-19, which can take months by traditional experimental approaches, serving as valuable information for COVID-19 vaccine formulation.

5. Large-scale screening of COVID-19 patients

Neural network classifiers were developed for large-scale screening of COVID-19 patients based on their distinct respiratory pattern (5).

6. Automated detection of COVID-19 patients based on CT images

A deep learningbased analysis system of thoracic CT images was constructed for automated detection and monitoring of COVID-19 patients over time (6).

 

Reference

(1) Randhawa GS, Soltysiak MPM, El Roz H, de Souza CPE, Hill KA, Kari L. Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study. bioRxiv, 2020.

(2) Metsky HC, Freije CA, Kosoko-Thoroddsen T-SF, Sabeti PC, Myhrvold C. CRISPR-based COVID-19 surveillance using a genomicallycomprehensive machine learning approach. bioRxiv, 2020. doi:10.1101/2020.02.26.967026.

(3) Yan L, Zhang H-T, Xiao Y, Wang M, Sun C, Liang J, Li S, Zhang M, Guo Y, Xiao Y. Prediction of survival for severe Covid-19 patients with three clinical features: development of a machine learning-based prognostic model with clinical data in Wuhan. medRxiv. 2020. doi:10.1101/2020.02.27.20028027.

(4) Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, Qin C, Žídek A, Nelson AWR, Bridgland A, Penedones H, Petersen S, Simonyan K, Crossan S, Kohli P, Jones DT, Silver D, Kavukcuoglu K, Hassabis D. Improved protein structure prediction using potentials from deep learning. Nature 577: 706–710, 2020. doi:10.1038/s41586-019-1923-7.

(5) Wang Y, Hu M, Li Q, Zhang X-P, Zhai G, Yao N. Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner. arXiv2002.05534. 2020.

(6) Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, Bernheim A, Siegel E. Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis. arXiv2003.05037. 2020.