Author: Goodswen, Stephen J; Barratt, Joel L N; Kennedy, Paul J; Kaufer, Alexa; Calarco, Larissa; Ellis, John T
Title: Machine learning and applications in microbiology Cord-id: izkaop6s Document date: 2021_3_16
ID: izkaop6s
Snippet: 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 comput
Document: 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.
Search related documents:
Co phrase search for related documents- absence presence and accurate assessment: 1
- absence presence and accurate diagnosis: 1, 2, 3, 4, 5, 6, 7
- absence presence and accurate identification: 1, 2, 3
- absence presence and accurate model: 1
- absence presence and accurate rapid: 1, 2, 3, 4
- absence presence and accurately diagnose: 1, 2
- absence presence and local information: 1
- absence presence and logistic regression: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63
- absence presence and logistic regression model: 1, 2, 3, 4, 5, 6, 7
- absence presence and loss function: 1, 2
- absence presence and loss value: 1
- absence presence and lr logistic regression: 1
- absence presence and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13
- absence presence and machine learning approach: 1
- absence presence and machine learning linear regression: 1
- absence presence and machine learning model: 1, 2, 3, 4
- absolute precision and accurate rapid: 1
Co phrase search for related documents, hyperlinks ordered by date