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Machine Learning and Artificial Intelligence in Bioinformatics

Section edited by Jean-Philippe Vert

This section covers recent advances in machine learning and artificial intelligence methods, including their applications to problems in bioinformatics. It considers manuscripts describing novel computational techniques to analyse high throughput data such as sequences and gene/protein expressions, as well as machine learning techniques such as graphical models, neural networks or kernel methods.

  1. Content type: Methodology article

    Predicting meaningful miRNA-disease associations (MDAs) is costly. Therefore, an increasing number of researchers are beginning to focus on methods to predict potential MDAs. Thus, prediction methods with impr...

    Authors: Ying-Lian Gao, Zhen Cui, Jin-Xing Liu, Juan Wang and Chun-Hou Zheng

    Citation: BMC Bioinformatics 2019 20:353

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  2. Content type: Methodology article

    Modern genomic and proteomic profiling methods produce large amounts of data from tissue and blood-based samples that are of potential utility for improving patient care. However, the design of precision medic...

    Authors: Joanna Roder, Carlos Oliveira, Lelia Net, Maxim Tsypin, Benjamin Linstid and Heinrich Roder

    Citation: BMC Bioinformatics 2019 20:325

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  3. Content type: Methodology article

    Although various machine learning-based predictors have been developed for estimating protein–protein interactions, their performances vary with dataset and species, and are affected by two primary aspects: ch...

    Authors: Kuan-Hsi Chen, Tsai-Feng Wang and Yuh-Jyh Hu

    Citation: BMC Bioinformatics 2019 20:308

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  4. Content type: Methodology article

    Representation learning provides new and powerful graph analytical approaches and tools for the highly valued data science challenge of mining knowledge graphs. Since previous graph analytical methods have mos...

    Authors: Zheng Gao, Gang Fu, Chunping Ouyang, Satoshi Tsutsui, Xiaozhong Liu, Jeremy Yang, Christopher Gessner, Brian Foote, David Wild, Ying Ding and Qi Yu

    Citation: BMC Bioinformatics 2019 20:306

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  5. Content type: Methodology article

    Modern molecular profiling techniques are yielding vast amounts of data from patient samples that could be utilized with machine learning methods to provide important biological insights and improvements in pa...

    Authors: Heinrich Roder, Carlos Oliveira, Lelia Net, Benjamin Linstid, Maxim Tsypin and Joanna Roder

    Citation: BMC Bioinformatics 2019 20:273

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  6. Content type: Research article

    Automatic extraction of chemical-disease relations (CDR) from unstructured text is of essential importance for disease treatment and drug development. Meanwhile, biomedical experts have built many highly-struc...

    Authors: Huiwei Zhou, Chengkun Lang, Zhuang Liu, Shixian Ning, Yingyu Lin and Lei Du

    Citation: BMC Bioinformatics 2019 20:260

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  7. Content type: Research article

    Computational approaches for the determination of biologically-active/native three-dimensional structures of proteins with novel sequences have to handle several challenges. The (conformation) space of possibl...

    Authors: Ahmed Bin Zaman and Amarda Shehu

    Citation: BMC Bioinformatics 2019 20:211

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  8. Content type: Research article

    Long non-coding RNAs play an important role in human complex diseases. Identification of lncRNA-disease associations will gain insight into disease-related lncRNAs and benefit disease diagnoses and treatment. ...

    Authors: Xiao-Nan Fan, Shao-Wu Zhang, Song-Yao Zhang, Kunju Zhu and Songjian Lu

    Citation: BMC Bioinformatics 2019 20:87

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2018 Journal Metrics

  • Citation Impact
    2.511 - 2-year Impact Factor
    2.970 - 5-year Impact Factor
    0.855 - Source Normalized Impact per Paper (SNIP)
    1.374 - SCImago Journal Rank (SJR)

    Usage 
    4,129,368 downloads

    Social Media Impact
    4446 mentions

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