Department of Computer Science, School of Mathematics, Statistics & Computer Science, College of Science, University of Tehran, 14155-6455, Tehran, Iran.
Department of Microbiology, School of Biology & Center of Excellence in Phylogeny of Living Organisms, College of Science, University of Tehran, 14155-6455, Tehran, Iran.
Future Microbiol. 2018 Mar;13:313-329. doi: 10.2217/fmb-2016-0096. Epub 2018 Feb 26.
To simplify the recognition of Actinobacteria, at different stages of the growth phase, from a mixed culture to facilitate the isolation of novel strains of these bacteria for drug discovery purposes.
MATERIALS & METHODS: A method was developed based on Gabor transform, and machine learning using k-Nearest Neighbors and Naive Bayes classifier, Logitboost, Bagging and Random Forest to automatically categorize the colonies.
A signature pattern was inferred by the model, making the differentiation of identical strains possible. Additionally, higher performance, compared with other classification methods was achieved.
This automated approach can contribute to the acceleration of the drug discovery process while it simultaneously can diminish the loss of budget due to the redundancy occurred by the inexperienced researchers.
简化混合培养物中不同生长阶段放线菌的识别，以方便分离这些细菌的新菌株，用于药物发现目的。
开发了一种基于 Gabor 变换和机器学习的方法，使用 k-最近邻和朴素贝叶斯分类器、Logitboost、Bagging 和随机森林自动对菌落进行分类。
模型推断出一种特征模式，使得同一菌株的分化成为可能。此外，与其他分类方法相比，该模型的性能更高。
这种自动化方法可以加速药物发现过程，同时减少由于缺乏经验的研究人员造成的冗余而导致的预算损失。
