Machine learning and applications in microbiology (2021-03-16T00:00:00.000000Z)

TL;DR

The aim of this review is to identify key points required to start the journey of becoming an effective machine learning practitioner and evaluate how machine learning has been applied so far in a broad scope of real-life microbiology examples.

Abstract

ABSTRACT To understand the intricacies of microorganisms at the molecular level requires making sense of copious volumes of data such that it may now be humanly impossible to detect insightful data patterns without an artificial intelligence application called machine learning. Applying machine learning to address biological problems is expected to grow at an unprecedented rate, yet it is perceived by the uninitiated as a mysterious and daunting entity entrusted to the domain of mathematicians and computer scientists. The aim of this review is to identify key points required to start the journey of becoming an effective machine learning practitioner. These key points are further reinforced with an evaluation of how machine learning has been applied so far in a broad scope of real-life microbiology examples. This includes predicting drug targets or vaccine candidates, diagnosing microorganisms causing infectious diseases, classifying drug resistance against antimicrobial medicines, predicting disease outbreaks and exploring microbial interactions. Our hope is to inspire microbiologists and other related researchers to join the emerging machine learning revolution.

Authors

Stephen J. Goodswen

1 papers

Joel L. N. Barratt

1 papers

Paul J. Kennedy

2 papers

TL;DR

Abstract

Authors

References198 items

An introduction to statistical learning with applications in R

Retrospective analysis of the accuracy of predicting the alert level of COVID-19 in 202 countries using Google Trends and machine learning

Evaluation of an ensemble-based distance statistic for clustering MLST datasets using epidemiologically defined clusters of cyclosporiasis

Aspects

New machine learning method for image-based diagnosis of COVID-19

Machine learning-based analyses support the existence of species complexes for Strongyloides fuelleborni and Strongyloides stercoralis

Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study

Morphological Cell Profiling of SARS-CoV-2 Infection Identifies Drug Repurposing Candidates for COVID-19

Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review

Repositioning of 8565 Existing Drugs for COVID-19

COVID-Classifier: An automated machine learning model to assist in the diagnosis of COVID-19 infection in chest x-ray images

Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study

Detection of Intestinal Protozoa in Trichrome-Stained Stool Specimens by Use of a Deep Convolutional Neural Network

Smartphones as mobile microbiological laboratories.

COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning

AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data

Microbiome analyses of blood and tissues suggest cancer diagnostic approach

Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions

A Computer Modelling Approach To Evaluate the Accuracy of Microsatellite Markers for Classification of Recurrent Infections during Routine Monitoring of Antimalarial Drug Efficacy

t-Distributed Stochastic Neighbor Embedding (t-SNE): A tool for eco-physiological transcriptomic analysis.

A global genotyping survey of Strongyloides stercoralis and Strongyloides fuelleborni using deep amplicon sequencing

Increase Trichomonas vaginalis detection based on urine routine analysis through a machine learning approach

HIV drug resistance prediction with weighted categorical kernel functions

Analysis of Streptococcus pneumoniae using Fourier-transformed infrared spectroscopy allows prediction of capsular serotype

Genotyping genetically heterogeneous Cyclospora cayetanensis infections to complement epidemiological case linkage

Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis

Machine learning performance in a microbial molecular autopsy context: A cross-sectional postmortem human population study

Applications of machine learning in drug discovery and development

Transcriptional and Metabolomic Analyses Indicate that Cell Wall Properties are Associated with Drought Tolerance in Brachypodium distachyon

How to make more from exposure data? An integrated machine learning pipeline to predict pathogen exposure

Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets.

Review of classical dimensionality reduction and sample selection methods for large-scale data processing

Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads

Antigenic: An improved prediction model of protective antigens

Zoonotic Source Attribution of Salmonella enterica Serotype Typhimurium Using Genomic Surveillance Data, United States

A GIS-Based Artificial Neural Network Model for Spatial Distribution of Tuberculosis across the Continental United States

Artificial Intelligence and Black-Box Medical Decisions: Accuracy versus Explainability.

Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study

Dimensionality reduction for visualizing single-cell data using UMAP

Application of machine learning techniques to tuberculosis drug resistance analysis

Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance

Notes from the Field: Multiple Cyclosporiasis Outbreaks — United States, 2018

A unified framework for unconstrained and constrained ordination of microbiome read count data

Machine Learning for detection of viral sequences in human metagenomic datasets

Identification and capsular serotype sequetyping of Streptococcus pneumoniae strains

Machine learning for fast identification of bacteraemia in SIRS patients treated on standard care wards: a cohort study

Using an optimal set of features with a machine learning-based approach to predict effector proteins for Legionella pneumophila

Human Gut Microbiota Predicts Susceptibility to Vibrio cholerae Infection

A GLM‐based latent variable ordination method for microbiome samples

Viral taxonomy derived from evolutionary genome relationships

MetaCompare: a computational pipeline for prioritizing environmental resistome risk

Phenotypic Antimicrobial Susceptibility Testing with Deep Learning Video Microscopy.

Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images

EvoStruct-Sub: An accurate Gram-positive protein subcellular localization predictor using evolutionary and structural features.

A Machine Learning Application Based in Random Forest for Integrating Mass Spectrometry-Based Metabolomic Data: A Simple Screening Method for Patients With Zika Virus

Image analysis and machine learning for detecting malaria

A review of conceptual clustering algorithms

Machine Learning Reveals Missing Edges and Putative Interaction Mechanisms in Microbial Ecosystem Networks

Machine learning prioritizes synthesis of primaquine ureidoamides with high antimalarial activity and attenuated cytotoxicity.

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

A Survey of Methods for Explaining Black Box Models

Using machine-learning to optimize phase contrast in a low-cost cellphone microscope

Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data

A review of clustering techniques and developments

Capacity Development through the US President’s Malaria Initiative–Supported Antimalarial Resistance Monitoring in Africa Network

Automated Interpretation of Blood Culture Gram Stains by Use of a Deep Convolutional Neural Network

Detecting macroecological patterns in bacterial communities across independent studies of global soils

Predicting Ascospore Release of Monilinia vaccinii-corymbosi of Blueberry with Machine Learning.

Deep learning approach to bacterial colony classification

Tuftsin-Phosphorylcholine Maintains Normal Gut Microbiota in Collagen Induced Arthritic Mice

Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level

Malaria Parasite Detection From Peripheral Blood Smear Images Using Deep Belief Networks

Geminivirus data warehouse: a database enriched with machine learning approaches

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Interpretable Explanations of Black Boxes by Meaningful Perturbation

Prediction of lymph node parasite load from clinical data in dogs with leishmaniasis: An application of radial basis artificial neural networks.