Its2vec: Fungal Species Identification Using Sequence Embedding and Random Forest Classification

Wang, Chao; Zhang, Ying; Han, Soonhung

doi:10.1155/2020/2468789

Cited by 11 publications

(7 citation statements)

References 87 publications

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“…The application of representation learning to biological sequences is not new. For instance, word2vec, a widely used natural language processing (NLP) technique 48,49 , has been applied to obtain embeddings from sequences of the human genome 50 , as well as solve problems such as species identification 51,52 , methyladenosine site prediction 53,54 , and MHC binding site prediction 55,56 . However, word2vec is based on the encoding of local contextual information of sequence segments and have no provision for representing global information of entire sequences.…”

Section: Discussionmentioning

confidence: 99%

Coding nucleic acid sequences with graph convolutional network

WANG

Zhang

et al. 2022

Preprint

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Phage mining yields a large number of phage genome sequences. There is an urgent need for tools to analyze the obtained phage sequences reliably. Neural network-based methods are prime candidates for these tasks due to their applicability to large, diverse datasets where little knowledge is available. However, the highly variable lengths of nucleic acid sequences pose a major obstacle to the sequence presentation as neural network input. Also, genetic variations further complicate the sequence comparison. Here we propose a graph representation of nucleic acid sequence, where all subsequences of the represented sequence can be matched to gapped patterns formed by paths of the graph, and various distribution information of these subsequences are encoded as features of the graph elements. These graphs, which we call gapped pattern graphs, can be transformed through a Graph Convolutional Network (GCN) to form lower-dimensional embedding for downstream tasks. Based on the gapped pattern graph, we introduced Graphage, an implementation of a neural network model, and compared it with equivalent models based on other forms of input (e.g., k-mer distribution, word2vec) in performing four downstream tasks: phage and ICE discrimination, phage integration site prediction, phage lifestyle prediction, and phage host prediction. We also compared Graphage with the current state-of-the-art tools on these tasks, where such tools are available. Our results show that Graphage consistently outperformed all the other tools and methods under various metrics on all four tasks. With Graphage, we identified distinct gapped pattern signatures with regard to phage phenotypes.

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Section: Discussionmentioning

confidence: 99%

Coding nucleic acid sequences with graph convolutional network

WANG

Zhang

et al. 2022

Preprint

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“…In this paper, we utilized various ensemble learning classification algorithms to develop identification models, which contain random forest ( Ru et al, 2019 ; Wang et al, 2020b ; Ao et al, 2021 ), AdaBoost, Gradient Boost Decision Tree ( Yu et al, 2020b ), LightGBM, and XGBoost. In addition, we also tried some traditional machine learning classification algorithms, such as logistic regression and Naïve Bayes.…”

Section: Methodsmentioning

confidence: 99%

iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest

Zhao

Teng

et al. 2021

Front. Genet.

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Recently, several anti-inflammatory peptides (AIPs) have been found in the process of the inflammatory response, and these peptides have been used to treat some inflammatory and autoimmune diseases. Therefore, identifying AIPs accurately from a given amino acid sequences is critical for the discovery of novel and efficient anti-inflammatory peptide-based therapeutics and the acceleration of their application in therapy. In this paper, a random forest-based model called iAIPs for identifying AIPs is proposed. First, the original samples were encoded with three feature extraction methods, including g-gap dipeptide composition (GDC), dipeptide deviation from the expected mean (DDE), and amino acid composition (AAC). Second, the optimal feature subset is generated by a two-step feature selection method, in which the feature is ranked by the analysis of variance (ANOVA) method, and the optimal feature subset is generated by the incremental feature selection strategy. Finally, the optimal feature subset is inputted into the random forest classifier, and the identification model is constructed. Experiment results showed that iAIPs achieved an AUC value of 0.822 on an independent test dataset, which indicated that our proposed model has better performance than the existing methods. Furthermore, the extraction of features for peptide sequences provides the basis for evolutionary analysis. The study of peptide identification is helpful to understand the diversity of species and analyze the evolutionary history of species.

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“…Five commonly used metrics, ACC, Specificity (SP), Sensitivity (SN), Matthews correlation coefficient (MCC) and AUC [ 51 – 68 ] were used for model performance evaluation. They are calculated as follows:

A C C = \frac{T P + T N}{T P + T N + F P + F N}

S N = \frac{T P}{T P + F N}, S P = \frac{T N}{T N + F P}

\begin{matrix} M C C \\ = \frac{T P \times T N ‐ F P \times F N}{\sqrt{(F P + T P) (F N + T P) (F P + T N) (F N + T N)}} \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

A Machine Learning Method for Differentiating and Predicting Human‐Infective Coronavirus Based on Physicochemical Features and Composition of the Spike Protein

Chao

Quan

2021

Chinese J of Electronics

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Several Coronaviruses (CoVs) are epidemic pathogens that cause severe respiratory syndrome and are associated with significant morbidity and mortality. In this paper, a machine learning method was developed for predicting the risk of human infection posed by CoVs as an early warning system. The proposed Spike‐SVM (Support vector machine) model achieved an accuracy of 97.36% for Human‐infective CoV (HCoV) and Nonhuman‐infective CoV (Non‐HCoV) classification. The top informative features that discriminate HCoVs and Non‐HCoVs were identified. Spike‐SVM is anticipated to be a useful bioinformatics tool for predicting the infection risk posed by CoVs to humans.

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Its2vec: Fungal Species Identification Using Sequence Embedding and Random Forest Classification

Cited by 11 publications

References 87 publications

Coding nucleic acid sequences with graph convolutional network

Coding nucleic acid sequences with graph convolutional network

iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest

A Machine Learning Method for Differentiating and Predicting Human‐Infective Coronavirus Based on Physicochemical Features and Composition of the Spike Protein

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