Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest

Liao, Zhijun; Ju, Ying; Zou, Quan

doi:10.1155/2016/8309253

Cited by 40 publications

(38 citation statements)

References 71 publications

(63 reference statements)

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“…For example, in contrast to phylogenetic analysis, methods like K-means analysis [40] and random forest [41] could also be applied to classify the proteins and perform taxonomy. However, it is out of the scope of this study.…”

Section: Resultsmentioning

confidence: 99%

An Alignment-Free Algorithm in Comparing the Similarity of Protein Sequences Based on Pseudo-Markov Transition Probabilities among Amino Acids

Song

Yang

et al. 2016

PLoS ONE

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In this paper, we have proposed a novel alignment-free method for comparing the similarity of protein sequences. We first encode a protein sequence into a 440 dimensional feature vector consisting of a 400 dimensional Pseudo-Markov transition probability vector among the 20 amino acids, a 20 dimensional content ratio vector, and a 20 dimensional position ratio vector of the amino acids in the sequence. By evaluating the Euclidean distances among the representing vectors, we compare the similarity of protein sequences. We then apply this method into the ND5 dataset consisting of the ND5 protein sequences of 9 species, and the F10 and G11 datasets representing two of the xylanases containing glycoside hydrolase families, i.e., families 10 and 11. As a result, our method achieves a correlation coefficient of 0.962 with the canonical protein sequence aligner ClustalW in the ND5 dataset, much higher than those of other 5 popular alignment-free methods. In addition, we successfully separate the xylanases sequences in the F10 family and the G11 family and illustrate that the F10 family is more heat stable than the G11 family, consistent with a few previous studies. Moreover, we prove mathematically an identity equation involving the Pseudo-Markov transition probability vector and the amino acids content ratio vector.

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Section: Resultsmentioning

confidence: 99%

An Alignment-Free Algorithm in Comparing the Similarity of Protein Sequences Based on Pseudo-Markov Transition Probabilities among Amino Acids

Song

Yang

et al. 2016

PLoS ONE

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“…First, we extracted the feature vectors from the positive and negative protein sequence dataset by a previously developed novel machine learning method727374. We transformed all the positive and negative datasets into the corresponding protein family information (Pfam number files).…”

Section: Methodsmentioning

confidence: 99%

Identification of DEP domain-containing proteins by a machine learning method and experimental analysis of their expression in human HCC tissues

Liao

Wang

Zeng³

et al. 2016

Sci Rep

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The Dishevelled/EGL-10/Pleckstrin (DEP) domain-containing (DEPDC) proteins have seven members. However, whether this superfamily can be distinguished from other proteins based only on the amino acid sequences, remains unknown. Here, we describe a computational method to segregate DEPDCs and non-DEPDCs. First, we examined the Pfam numbers of the known DEPDCs and used the longest sequences for each Pfam to construct a phylogenetic tree. Subsequently, we extracted 188-dimensional (188D) and 20D features of DEPDCs and non-DEPDCs and classified them with random forest classifier. We also mined the motifs of human DEPDCs to find the related domains. Finally, we designed experimental verification methods of human DEPDC expression at the mRNA level in hepatocellular carcinoma (HCC) and adjacent normal tissues. The phylogenetic analysis showed that the DEPDCs superfamily can be divided into three clusters. Moreover, the 188D and 20D features can both be used to effectively distinguish the two protein types. Motif analysis revealed that the DEP and RhoGAP domain was common in human DEPDCs, human HCC and the adjacent tissues that widely expressed DEPDCs. However, their regulation was not identical. In conclusion, we successfully constructed a binary classifier for DEPDCs and experimentally verified their expression in human HCC tissues.

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“…The Random Forest (RF) algorithm is also a popular learning algorithm and has been successfully employed in dealing with various biological prediction problems [ 105 , 106 , 107 , 108 ]. The principle of RF is based on the training of multiple decision trees.…”

Section: Prediction Algorithmsmentioning

confidence: 99%

Recent Advances in Conotoxin Classification by Using Machine Learning Methods

Dao

Yang

et al. 2017

Molecules

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Conotoxins are disulfide-rich small peptides, which are invaluable peptides that target ion channel and neuronal receptors. Conotoxins have been demonstrated as potent pharmaceuticals in the treatment of a series of diseases, such as Alzheimer’s disease, Parkinson’s disease, and epilepsy. In addition, conotoxins are also ideal molecular templates for the development of new drug lead compounds and play important roles in neurobiological research as well. Thus, the accurate identification of conotoxin types will provide key clues for the biological research and clinical medicine. Generally, conotoxin types are confirmed when their sequence, structure, and function are experimentally validated. However, it is time-consuming and costly to acquire the structure and function information by using biochemical experiments. Therefore, it is important to develop computational tools for efficiently and effectively recognizing conotoxin types based on sequence information. In this work, we reviewed the current progress in computational identification of conotoxins in the following aspects: (i) construction of benchmark dataset; (ii) strategies for extracting sequence features; (iii) feature selection techniques; (iv) machine learning methods for classifying conotoxins; (v) the results obtained by these methods and the published tools; and (vi) future perspectives on conotoxin classification. The paper provides the basis for in-depth study of conotoxins and drug therapy research.

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Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest

Cited by 40 publications

References 71 publications

An Alignment-Free Algorithm in Comparing the Similarity of Protein Sequences Based on Pseudo-Markov Transition Probabilities among Amino Acids

An Alignment-Free Algorithm in Comparing the Similarity of Protein Sequences Based on Pseudo-Markov Transition Probabilities among Amino Acids

Identification of DEP domain-containing proteins by a machine learning method and experimental analysis of their expression in human HCC tissues

Recent Advances in Conotoxin Classification by Using Machine Learning Methods

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