United Neighborhood Closeness Centrality and Orthology for Predicting Essential Proteins

Li, Gaoshi; Li, Min; Wang, Jianxin; Li, Yaohang; Pan, Yi

doi:10.1109/tcbb.2018.2889978

Cited by 68 publications

(54 citation statements)

References 68 publications

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“…This allows us to know the quantity of true essential proteins. Therefore, the sensitivity (SN ), specificity (SP ), F-measure (F ), and accuracy (ACC), positive predictive value (P P V ), negative predictive value (N P V ) can be calculated [28,29].…”

Section: Assessment Methodsmentioning

confidence: 99%

A mixed clustering coefficient centrality for identifying essential proteins

2020

Int. J. Mod. Phys. B

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Essential protein plays a crucial role in the process of cell life. The identification of essential proteins can not only promote the development of drug target technology, but also contribute to the mechanism of biological evolution. There are plenty of scholars who pay attention to discovering essential proteins according to the topological structure of protein network and biological information. The accuracy of protein recognition still demands to be improved. In this paper, we propose a method which integrate the clustering coefficient in protein complexes and topological properties to determine the essentiality of proteins. First, we give the definition of In-clustering coefficient (IC) to describe the properties of protein complexes. Then we propose a new method, complex edge and node clustering coefficient (CEN C) to identify essential proteins. Different Protein-Protein Interaction (PPI) networks of Saccharomyces cerevisiae, MIPS and DIP are used as experimental materials. Through some experiments of logistic regression model, the results show that the method of CEN C can promote the ability of recognizing essential proteins, by comparing with the existing methods DC, BC, EC, SC, LAC, N C and the recent method U C.

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Section: Assessment Methodsmentioning

confidence: 99%

A mixed clustering coefficient centrality for identifying essential proteins

2020

Int. J. Mod. Phys. B

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“…en we construct the attribute matrix P based on equation (5). irdly, we calculate the normalized attribute matrix R based on equations (6) and (7). Fourthly, we calculate the entropy e of each indicator based on…”

Section: Node Importance Rankingmentioning

confidence: 99%

“…ensum j � ensum j − p ij ln p ij ; (10) end (11) e j � ensum j /ln n (12) esum � esum + e j (13) end (14) for each I j in I do (15) (19) end (20) Rank the node list based on s i (21) return the ranked node list; ALGORITHM 1: Node importance ranking algorithm. 6 Complexity evaluate the node importance. e core idea of TOPSIS-RE is to construct a positive ideal object and a negative ideal object from the original data.…”

Section: Experiments Setupmentioning

confidence: 99%

“…However, they cannot accurately reflect the characteristics of the whole network. To consider more global characteristics of the whole network, closeness centrality [6], betweenness centrality [7], etc. are proposed.…”

Section: Introductionmentioning

confidence: 99%

“…erefore, v i can influence more nodes in the network and is more important.3.2.2. Closeness Centrality.Closeness centrality (CC)[6], is defined to represent the average distance of node v i to all other nodes in the network. Suppose l ij denotes the length of the shortest path from the source node v i to the destination node v j .…”

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An Entropy-Based Self-Adaptive Node Importance Evaluation Method for Complex Networks

2020

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Identifying important nodes in complex networks is essential in disease transmission control, network attack protection, and valuable information detection. Many evaluation indicators, such as degree centrality, betweenness centrality, and closeness centrality, have been proposed to identify important nodes. Some researchers assign different weight to different indicator and combine them together to obtain the final evaluation results. However, the weight is usually subjectively assigned based on the researcher’s experience, which may lead to inaccurate results. In this paper, we propose an entropy-based self-adaptive node importance evaluation method to evaluate node importance objectively. Firstly, based on complex network theory, we select four indicators to reflect different characteristics of the network structure. Secondly, we calculate the weights of different indicators based on information entropy theory. Finally, based on aforesaid steps, the node importance is obtained by weighted average method. The experimental results show that our method performs better than the existing methods.

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DeepFunc: A Deep Learning Framework for Accurate Prediction of Protein Functions from Protein Sequences and Interactions

et al. 2019

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Annotation of protein functions plays an important role in understanding lifeat the molecular level. High-throughput sequencing produces massive numbers of raw proteins sequences and only about 1% of them have been manually annotated with functions. Experimental annotations of functions are expensive, time-consuming and do not keep up with the rapid growth of the sequence numbers. This motivates the development of computational approaches that predict protein functions. A novel deep learning framework, DeepFunc, is proposed which accurately predicts protein functions from protein sequence-and network-derived information. More precisely, DeepFunc uses a long and sparse binary vector to encode information concerning domains, families, and motifs collected from the InterPro tool that is associated with the input protein sequence. This vector is processed with two neural layers to obtain a low-dimensional vector which is combined with topological information extracted from protein-protein interactions (PPIs) and functional linkages. The combined information is processed by a deep neural network that predicts protein functions. DeepFunc is empirically and comparatively tested on a benchmark testing dataset and the Critical Assessment of protein Function Annotation algorithms (CAFA) 3 dataset. The experimental results demonstrate that DeepFunc outperforms current methods on the testing dataset and that it secures the highest F max = 0.54 and AUC = 0.94 on the CAFA3 dataset.

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United Neighborhood Closeness Centrality and Orthology for Predicting Essential Proteins

Cited by 68 publications

References 68 publications

A mixed clustering coefficient centrality for identifying essential proteins

A mixed clustering coefficient centrality for identifying essential proteins

An Entropy-Based Self-Adaptive Node Importance Evaluation Method for Complex Networks

DeepFunc: A Deep Learning Framework for Accurate Prediction of Protein Functions from Protein Sequences and Interactions

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