Wishing you a very Happy Birthday | Dr. Quan Zou

 

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Dr. Quan Zou

EDITOR-IN-CHIEF: Current Bioinformatics

 

University of Electronic Science and Technology of China
Chengdu
China

Aims and Scope | Current Bioinformatics

Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth/mini-reviews, research papers and guest edited thematic issues written by leaders in the field, covering a wide range of the integration of biology with computer and information science.

The journal focuses on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.

Current Bioinformatics is an essential journal for all academic and industrial researchers who want expert knowledge on all major advances in bioinformatics. To know more about the journal, please visit: https://benthamscience.com/journals/current-bioinformatics/

 

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Open Access Articles | Improving Self-interacting Proteins Prediction Accuracy Using Protein Evolutionary Information and Weighed-Extreme Learning Machine

Journal Name: Current Bioinformatics

Author(s): Ji-Yong An*, Yong Zhou, Lei Zhang, Qiang Niu, Da-Fu Wang.

 

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Background: Self Interacting Proteins (SIPs) play an essential role in various aspects of the structural and functional organization of the cell.

Objective: In the study, we presented a novelty sequence-based computational approach for predicting Self-interacting proteins using Weighed-Extreme Learning Machine (WELM) model combined with an Autocorrelation (AC) descriptor protein feature representation.

Method: The major advantage of the proposed method mainly lies in adopting an effective feature extraction method to represent candidate self-interacting proteins by using the evolutionary information embedded in PSI-BLAST-constructed Position Specific Scoring Matrix (PSSM); and then employing a reliable and effective WELM classifier to perform classify.

Result: In order to evaluate the performance, the proposed approach is applied to yeast and human SIP datasets. The experimental results show that our method obtained 93.43% and 98.15% prediction accuracies on yeast and human dataset, respectively. Extensive experiments are carried out to compare our approach with the SVM classifier and existing sequence-based method on yeast and human dataset. Experimental results show that the performance of our method is better than several other state-of-theart methods.

Conclusion: It is demonstrated that the proposed method is suitable for SIPs detection and can execute incredibly well for identifying Sips. In order to facilitate extensive studies for future proteomics research, we developed a freely available web server called WELM-AC-SIPs in Hypertext Preprocessor (PHP) for predicting SIPs. The web server including source code and the datasets are available at http://219.219.62.123:8888/WELMAC/. To read out more, please visit: http://www.eurekaselect.com/159690

Most Accessed Articles| Stroke Lesion Segmentation and Analysis using Entropy/Otsu’s Function – A Study with Social Group Optimization

Journal Name: Current Bioinformatics

Author(s): Suresh Chandra Satapathy*, Steven Lawrence Fernandes, Hong Lin.

 

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Background: Stroke is one of the major causes for the momentary/permanent disability in the human community. Usually, stroke will originate in the brain section because of the neurological deficit and this kind of brain abnormality can be predicted by scrutinizing the periphery of brain region. Magnetic Resonance Image (MRI) is the extensively considered imaging procedure to record the interior sections of the brain to support visual inspection process.

Objective: In the proposed work, a semi-automated examination procedure is proposed to inspect the province and the severity of the stroke lesion using the MRI.

Method: Recently discovered heuristic approach called the Social Group Optimization (SGO) algorithm is considered to pre-process the test image based on a chosen image multi-thresholding procedure. Later, a chosen segmentation procedure is considered in the post-processing section to mine the stroke lesion from the pre-processed image.

Results: In this paper, the pre-processing work is executed with the well known thresholding approaches, such as Shannon’s entropy, Kapur’s entropy and Otsu’s function. Similarly, the postprocessing task is executed using most successful procedures, such as level set, active contour and watershed algorithm.

Conclusion: The proposed procedure is experimentally inspected using the benchmark brain stroke database known as Ischemic Stroke Lesion Segmentation (ISLES 2015) challenge database. The results of this experimental work authenticates that, Shannon’s approach along with the LS segmentation offers superior average values compared with the other approaches considered in this research work. To read out more, please visit: http://www.eurekaselect.com/168477

Open Access Articles | Data Integration of Hybrid Microarray and Single Cell Expression Data to Enhance Gene Network Inference

Journal Name: Current Bioinformatics

Author(s): Wei Zhang, Wenchao Li, Jianming Zhang*, Ning Wang.

 

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Background: Gene Regulatory Network (GRN) inference algorithms aim to explore casual interactions between genes and transcriptional factors. High-throughput transcriptomics data including DNA microarray and single cell expression data contain complementary information in network inference.

Objective: To enhance GRN inference, data integration across various types of expression data becomes an economic and efficient solution.

Method: In this paper, a novel E-alpha integration rule-based ensemble inference algorithm is proposed to merge complementary information from microarray and single cell expression data. This paper implements a Gradient Boosting Tree (GBT) inference algorithm to compute importance scores for candidate gene-gene pairs. The proposed E-alpha rule quantitatively evaluates the credibility levels of each information source and determines the final ranked list. Read out full article here: http://www.eurekaselect.com/168772

Most Accessed Articles | Dysfunctional Mechanism of Liver Cancer Mediated by Transcription Factor and Non-coding RNA

Journal Name: Current Bioinformatics

Author(s): Wei Zeng, Fang Wang, Yu Ma, Xianchun Liang, Ping Chen*.

 

 

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Background: There have been numerous experiments and studies on liver cancer by biomedical scientists, while no comprehensive and systematic exploration has yet been conducted. Therefore, this study aimed to systematically dissect the transcriptional and non-coding RNAmediated mechanisms of liver cancer dysfunction.

Method: At first, we collected 974 liver cancer associated genes from the Online Mendelian Inheritance in Man (OMIM). Afterwards, their interactors were recruited from STRING database so as to identify 18 co-expression modules in liver cancer patient expression profile. Crosstalk analysis showed the interactive relationship between these modules. In addition, core drivers for modules were identified, including 111 transcription factors (STAT3, JUN and NFKB1, etc.) and 1492 ncRNAs (FENDRR and miR-340-5p, etc.) Read out full article here: http://www.eurekaselect.com/167479

New Issue :: Current Bioinformatics (Volume: 13, Issue: 5)

 

Current BioinformaticsCurrent Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth reviews, research papers and guest edited thematic issues written by leaders in the field, covering a wide range of the integration of biology with computer and information science.

 

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Articles from the journal Current Bioinformatics Volume 13, Issue 5:

 

For details on the articles, please visit this link ::  https://bit.ly/2DxeHHJ

EDITOR’S CHOICE ARTICLE – Low Rank Representation and Its Application in Bioinformatics

 

Journal Name: Current Bioinformatics

Author(s): Yuan You, Hongmin Cai*, Jiazhou Chen.

 

 

 

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Abstract:

Background: Sparse representation has achieved tremendous success recently. Low-rank representation is one of the successful methods. It is aimed to capture underlying low-dimensional structures of high dimensional data and attracted much attention in the area of the pattern recognition and signal processing. Such successful applications were mainly to its effectiveness in exploring lowdimensional manifolds embedded in data, which can be naturally characterized by low rankness of the data matrix.

Objective: In this paper, we review the theoretical and numerical models based on low rank representation and hope the review can attract more research in bioinformatics.

Method: Low rank representation is particularly well suited to big data analysis in bioinformatics. The first reason is that the interested objects are naturally sparse, like copy number variations. The second reason is that there exist strong correlations among various modalities for the same object, like DNA, RNA and methylation.

Results and Conclusion: Its applications in bioinformatics area, including mining of key genes subset, finding common patterns across various modalities and biomedical image analysis were categorically summarized.

 

For more details, please visit: http://www.eurekaselect.com/157515/article

WISHING A VERY HAPPY BIRTHDAY DR. YI – PING PHOEBE CHEN!

 

Dr  Yi - Ping Phoebe Chen

 

 

DR. YI – PING PHOEBE CHEN

Editor-in-Chief: Current Bioinformatics

Department of Computer Science and Information Technology,

La Trobe University,

Melbourne, Australia.

New Issue :: Current Bioinformatics (Volume: 13, Issue: 4)

Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth reviews, drug clinical trial studies and guest edited thematic issues written by leaders in the field, covering a wide range of the integration of biology with computer and information science.

The journal focuses on reviews on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy.

Articles from the journal Current Bioinformatics Volume 13, Issue 4:

 

For details on the articles, please visit this link :: https://bit.ly/2m4DJ5x

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