Artificial Intelligence for Bioinformatics: Applications in Protein Folding Prediction

Staples, Max; Chan, Leong; Si, Dong; Johnson, Kasey; Whyte, Connor; Cao, Renzhi

doi:10.1101/561027

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…It is generally assumed that DL methods perform better than do other ML-based algorithms, 32 which has been widely applied in protein structure and function prediction. [33][34][35][36][37][38][39] However, SMD6A consistently outperformed the DL-based method, iDNA6mA, on both benchmark and independent datasets, further emphasizing that systematic selection of feature encodings and two-layer ensemble models are essential for improved prediction. Furthermore, McNemar's chi-square test was used to determine whether the differences between SDM6A and existing predictors were statistically significant.…”

Section: Comparing the Performance Of Sdm6a With That Of The Existing Predictormentioning

confidence: 92%

SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome

Basith

Manavalan

Shin

et al. 2019

Molecular Therapy - Nucleic Acids

140

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DNA N6-adenine methylation (6mA) is an epigenetic modification in prokaryotes and eukaryotes. Identifying 6mA sites in rice genome is important in rice epigenetics and breeding, but non-random distribution and biological functions of these sites remain unclear. Several machine-learning tools can identify 6mA sites but show limited prediction accuracy, which limits their usability in epigenetic research. Here, we developed a novel computational predictor, called the Sequence-based DNA N6-methyladenine predictor (SDM6A), which is a two-layer ensemble approach for identifying 6mA sites in the rice genome. Unlike existing methods, which are based on single models with basic features, SDM6A explores various features, and five encoding methods were identified as appropriate for this problem. Subsequently, an optimal feature set was identified from encodings, and corresponding models were developed individually using support vector machine and extremely randomized tree. First, all five single models were integrated via ensemble approach to define the class for each classifier. Second, two classifiers were integrated to generate a final prediction. SDM6A achieved robust performance on cross-validation and independent evaluation, with average accuracy and Matthews correlation coefficient (MCC) of 88.2% and 0.764, respectively. Corresponding metrics were 4.7%–11.0% and 2.3%–5.5% higher than those of existing methods, respectively. A user-friendly, publicly accessible web server (http://thegleelab.org/SDM6A) was implemented to predict novel putative 6mA sites in rice genome.

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Section: Comparing the Performance Of Sdm6a With That Of The Existing Predictormentioning

confidence: 92%

SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome

Basith

Manavalan

Shin

et al. 2019

Molecular Therapy - Nucleic Acids

140

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“…We also explore machine learning technique for this problem since it has been successfully and widely applied to solve problems in different fields, including image recognition, bioinformatics, voice recognition, drug discovery, etc. [6,7,8,9,10] 1 Mathematical models…”

Section: Introductionmentioning

confidence: 99%

High Definition image classification in Geoscience using Machine Learning

An,

Golden,

Wilcox

et al. 2020

Preprint

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High Definition (HD) digital photos taken with drones are widely used in the study of Geoscience. However, blurry images are often taken in collected data, and it takes a lot of time and effort to distinguish clear images from blurry ones. In this work, we apply Machine learning techniques, such as Support Vector Machine (SVM) and Neural Network (NN) to classify HD images in Geoscience as clear and blurry, and therefore automate data cleaning in Geoscience. We compare the results of classification based on features abstracted from several mathematical models. Some of the implementation of our machine learning tool is freely available at: https://github.com/zachgolden/geoai.

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