iT3SE-PX: Identification of Bacterial Type III Secreted Effectors Using PSSM Profiles and XGBoost Feature Selection

Ding, Chenchen; Han, Haitao; Li, Qianyue; Yang, Xiaoxia; Liu, Taigang

doi:10.1155/2021/6690299

Cited by 6 publications

(6 citation statements)

References 44 publications

(47 reference statements)

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“…Using the XGBoost algorithm to extract the features solely from PSSM profiles, Ding et al. introduced an SVM-based classifier-iT3SE-PX, to improve the prediction performance on T3SEs with only protein sequences ( 58 ). By integrating the advantages of multiple homology-based biological features and various machine learning algorithms, Hui et al.…”

Section: Discussionmentioning

confidence: 99%

DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework

Jing

Wen

Liao

et al. 2021

NAR Genomics and Bioinformatics

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Type III secretion systems (T3SSs) are bacterial membrane-embedded nanomachines that allow a number of humans, plant and animal pathogens to inject virulence factors directly into the cytoplasm of eukaryotic cells. Export of effectors through T3SSs is critical for motility and virulence of most Gram-negative pathogens. Current computational methods can predict type III secreted effectors (T3SEs) from amino acid sequences, but due to algorithmic constraints, reliable and large-scale prediction of T3SEs in Gram-negative bacteria remains a challenge. Here, we present DeepT3 2.0 (http://advintbioinforlab.com/deept3/), a novel web server that integrates different deep learning models for genome-wide predicting T3SEs from a bacterium of interest. DeepT3 2.0 combines various deep learning architectures including convolutional, recurrent, convolutional-recurrent and multilayer neural networks to learn N-terminal representations of proteins specifically for T3SE prediction. Outcomes from the different models are processed and integrated for discriminating T3SEs and non-T3SEs. Because it leverages diverse models and an integrative deep learning framework, DeepT3 2.0 outperforms existing methods in validation datasets. In addition, the features learned from networks are analyzed and visualized to explain how models make their predictions. We propose DeepT3 2.0 as an integrated and accurate tool for the discovery of T3SEs.

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Section: Discussionmentioning

confidence: 99%

DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework

Jing

Wen

Liao

et al. 2021

NAR Genomics and Bioinformatics

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“…According to the work of Li et al [ 48 ], the original PSSM profile (L × 20) could be reduced to a L × 10 matrix by merging some columns. RPSSM is obtained by exploring the local sequence information based on the L × 10 reduced PSSM [ 49 , 50 ]: and where represent the 20 columns in the original PSSM profile corresponding to the 20 amino acids. The re-PSSM is further transformed into a 10-dimensional vector: and …”

Section: Methodsmentioning

confidence: 99%

Section: Rpssmmentioning

confidence: 99%

PreAcrs: a machine learning framework for identifying anti-CRISPR proteins

Zhu

Wang

et al. 2022

BMC Bioinformatics

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Background Anti-CRISPR proteins are potent modulators that inhibit the CRISPR-Cas immunity system and have huge potential in gene editing and gene therapy as a genome-editing tool. Extensive studies have shown that anti-CRISPR proteins are essential for modifying endogenous genes, promoting the RNA-guided binding and cleavage of DNA or RNA substrates. In recent years, identifying and characterizing anti-CRISPR proteins has become a hot and significant research topic in bioinformatics. However, as most anti-CRISPR proteins fall short in sharing similarities to those currently known, traditional screening methods are time-consuming and inefficient. Machine learning methods could fill this gap with powerful predictive capability and provide a new perspective for anti-CRISPR protein identification. Results Here, we present a novel machine learning ensemble predictor, called PreAcrs, to identify anti-CRISPR proteins from protein sequences directly. Three features and eight different machine learning algorithms were used to train PreAcrs. PreAcrs outperformed other existing methods and significantly improved the prediction accuracy for identifying anti-CRISPR proteins. Conclusions In summary, the PreAcrs predictor achieved a competitive performance for predicting new anti-CRISPR proteins in terms of accuracy and robustness. We anticipate PreAcrs will be a valuable tool for researchers to speed up the research process. The source code is available at: https://github.com/Lyn-666/anti_CRISPR.git.

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“…Therefore, feature selection is one of the important steps while building a machine learning model, with the goal of finding the best possible subset of relevant features. A variety of feature selection techniques have been used for the identification of AOPs [ 17 – 19 ] and for other classification problems in bioinformatics [ 41 – 44 ]. In this study, the ANOVA method was adopted to perform the feature selection due to its simplicity and efficiency.…”

Section: Methodsmentioning

confidence: 99%

Accurate Identification of Antioxidant Proteins Based on a Combination of Machine Learning Techniques and Hidden Markov Model Profiles

Shen

Liu

2021

Computational and Mathematical Methods in Medicine

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Antioxidant proteins (AOPs) play important roles in the management and prevention of several human diseases due to their ability to neutralize excess free radicals. However, the identification of AOPs by using wet-lab experimental techniques is often time-consuming and expensive. In this study, we proposed an accurate computational model, called AOP-HMM, to predict AOPs by extracting discriminatory evolutionary features from hidden Markov model (HMM) profiles. First, auto cross-covariance (ACC) variables were applied to transform the HMM profiles into fixed-length feature vectors. Then, we performed the analysis of variance (ANOVA) method to reduce the dimensionality of the raw feature space. Finally, a support vector machine (SVM) classifier was adopted to conduct the prediction of AOPs. To comprehensively evaluate the performance of the proposed AOP-HMM model, the 10-fold cross-validation (CV), the jackknife CV, and the independent test were carried out on two widely used benchmark datasets. The experimental results demonstrated that AOP-HMM outperformed most of the existing methods and could be used to quickly annotate AOPs and guide the experimental process.

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iT3SE-PX: Identification of Bacterial Type III Secreted Effectors Using PSSM Profiles and XGBoost Feature Selection

Cited by 6 publications

References 44 publications

DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework

DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework

PreAcrs: a machine learning framework for identifying anti-CRISPR proteins

Accurate Identification of Antioxidant Proteins Based on a Combination of Machine Learning Techniques and Hidden Markov Model Profiles

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