Using Semantic Association to Extend and Infer Literature-Oriented Relativity Between Terms

Liang, Cheng; Li, Jie; Hu, Yang; Jiang, Yue; Liu, Yongzhuang; Chu, Yanshuo; Wang, Zhenxing; Wang, Yadong

doi:10.1109/tcbb.2015.2430289

Cited by 6 publications

(5 citation statements)

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“…One of the most frequently used algorithm to do this is EMI by Wren et al [ 48 ]. Here we downloaded the co-occurrence relationships of DO-BP term pairs in PubMed from the previous study [ 9 ], and then calculated the EMI similarity of DO-BP term pairs.…”

Section: Resultsmentioning

confidence: 99%

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InfAcrOnt: calculating cross-ontology term similarities using information flow by a random walk

Liang

Jiang

et al. 2018

BMC Genomics

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BackgroundSince the establishment of the first biomedical ontology Gene Ontology (GO), the number of biomedical ontology has increased dramatically. Nowadays over 300 ontologies have been built including extensively used Disease Ontology (DO) and Human Phenotype Ontology (HPO). Because of the advantage of identifying novel relationships between terms, calculating similarity between ontology terms is one of the major tasks in this research area. Though similarities between terms within each ontology have been studied with in silico methods, term similarities across different ontologies were not investigated as deeply. The latest method took advantage of gene functional interaction network (GFIN) to explore such inter-ontology similarities of terms. However, it only used gene interactions and failed to make full use of the connectivity among gene nodes of the network. In addition, all existent methods are particularly designed for GO and their performances on the extended ontology community remain unknown.ResultsWe proposed a method InfAcrOnt to infer similarities between terms across ontologies utilizing the entire GFIN. InfAcrOnt builds a term-gene-gene network which comprised ontology annotations and GFIN, and acquires similarities between terms across ontologies through modeling the information flow within the network by random walk. In our benchmark experiments on sub-ontologies of GO, InfAcrOnt achieves a high average area under the receiver operating characteristic curve (AUC) (0.9322 and 0.9309) and low standard deviations (1.8746e-6 and 3.0977e-6) in both human and yeast benchmark datasets exhibiting superior performance. Meanwhile, comparisons of InfAcrOnt results and prior knowledge on pair-wise DO-HPO terms and pair-wise DO-GO terms show high correlations.ConclusionsThe experiment results show that InfAcrOnt significantly improves the performance of inferring similarities between terms across ontologies in benchmark set.Electronic supplementary materialThe online version of this article (10.1186/s12864-017-4338-6) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

“…Especially, the relationships between terms of an ontology play an important role in clustering gene expression data for yielding biologically meaningful gene clusters [ 8 ], prioritizing disease genes for predicting novel disease-causing genes and etc. [ 9 – 11 ].…”

Section: Introductionmentioning

confidence: 99%

InfAcrOnt: calculating cross-ontology term similarities using information flow by a random walk

Liang

Jiang

et al. 2018

BMC Genomics

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“…To evaluate our method and the state-of-the-art methods for DTI prediction, we performed five-fold cross validation (Cheng et al, 2015; Chen et al, 2017; Lin et al, 2017; Wei et al, 2017a, 2018; Zeng et al, 2017b; Bu et al, 2018; Su et al, 2018; Xu et al, 2018b,c). All known DTIs were randomly divided into five subsets with equal size, and the same operation was applied to the unknown interactions (Liu et al, 2017; Zhang et al, 2017; Zeng et al, 2018).…”

Section: Experimental Evaluation and Discussionmentioning

confidence: 99%

Gradient Boosting Decision Tree-Based Method for Predicting Interactions Between Target Genes and Drugs

et al. 2019

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Determining the target genes that interact with drugs—drug–target interactions—plays an important role in drug discovery. Identification of drug–target interactions through biological experiments is time consuming, laborious, and costly. Therefore, using computational approaches to predict candidate targets is a good way to reduce the cost of wet-lab experiments. However, the known interactions (positive samples) and the unknown interactions (negative samples) display a serious class imbalance, which has an adverse effect on the accuracy of the prediction results. To mitigate the impact of class imbalance and completely exploit the negative samples, we proposed a new method, named DTIGBDT, based on gradient boosting decision trees, for predicting candidate drug–target interactions. We constructed a drug–target heterogeneous network that contains the drug similarities based on the chemical structures of drugs, the target similarities based on target sequences, and the known drug–target interactions. The topological information of the network was captured by random walks to update the similarities between drugs or targets. The paths between drugs and targets could be divided into multiple categories, and the features of each category of paths were extracted. We constructed a prediction model based on gradient boosting decision trees. The model establishes multiple decision trees with the extracted features and obtains the interaction scores between drugs and targets. DTIGBDT is a method of ensemble learning, and it effectively reduces the impact of class imbalance. The experimental results indicate that DTIGBDT outperforms several state-of-the-art methods for drug–target interaction prediction. In addition, case studies on Quetiapine, Clozapine, Olanzapine, Aripiprazole , and Ziprasidone demonstrate the ability of DTIGBDT to discover potential drug–target interactions.

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“…The PseKNC is a novel nucleotide sequence representation that have been applied to predict the attributes of DNA sequences [35]. It can also be applied to protein sequences [36].…”

Section: ) Pseknc Featuresmentioning

confidence: 99%

Predicting Sub-Golgi Apparatus Resident Protein With Primary Sequence Hybrid Features

Wang

Liu

et al. 2020

IEEE Access

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The Golgi apparatus is a significant membrane-bound organelle of eukaryotic cells that is made up of a series of flattened, stacked pouches (called cisternae). The Golgi apparatus packages proteins into membrane-bound vesicles, and so it is responsible for transporting, modifying, and packaging proteins and lipids into vesicles for delivery to targeted destinations. It belongs to the central organelle mediating system of eukaryotic cells. Functional defects of the Golgi apparatus are associated with many kinds of neurodegenerative diseases, such as Parkinson's and Alzheimer's diseases. Golgi-resident proteins play an important role in the Golgi apparatus' processing, which includes storing, packaging, and dispatching proteins. Identifying sub-Golgi protein types can help researchers to develop more effective therapies and drugs for diseases that result from disorders of Golgi-resident proteins. In this paper, we propose a computational model to discriminate cis-Golgi proteins from trans-Golgi proteins using a machine learning method. First, we use PseKNC, K-separated Bigrams, and PsePSSM as feature extraction techniques, and then we select the optimal features among those identified by PseKNC with the AdaBoost classifier. To create a balanced dataset out of the imbalanced set of Golgi proteins, we used the Random-SMOTE oversampling approach. Finally, we employed the SVM algorithm to distinguish cis-Golgi proteins from trans-Golgi proteins. The proposed method achieves promising performance, with accuracy of 96.5%, 96.5%, and 96.9% in the experiments with jackknife cross-validation, independent testing, and 10-fold cross-validation, respectively, which exceeds the performance of previous related work.

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Using Semantic Association to Extend and Infer Literature-Oriented Relativity Between Terms

Cited by 6 publications

References 31 publications

InfAcrOnt: calculating cross-ontology term similarities using information flow by a random walk

InfAcrOnt: calculating cross-ontology term similarities using information flow by a random walk

Gradient Boosting Decision Tree-Based Method for Predicting Interactions Between Target Genes and Drugs

Predicting Sub-Golgi Apparatus Resident Protein With Primary Sequence Hybrid Features

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