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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.

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  1. MiRNAs play significant roles in many fundamental and important biological processes, and predicting potential miRNA-disease associations makes contributions to understanding the molecular mechanism of human d...

    Authors: Yuchong Gong, Yanqing Niu, Wen Zhang and Xiaohong Li

    Citation: BMC Bioinformatics 2019 20:468

    Content type: Research article

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  2. Although many of the genic features in Mycobacterium abscessus have been fully validated, a comprehensive understanding of the regulatory elements remains lacking. Moreover, there is little understanding of how t...

    Authors: Patrick M. Staunton, Aleksandra A. Miranda-CasoLuengo, Brendan J. Loftus and Isobel Claire Gormley

    Citation: BMC Bioinformatics 2019 20:466

    Content type: Research Article

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  3. Atomic force microscopy (AFM) allows the mechanical characterization of single cells and live tissue by quantifying force-distance (FD) data in nano-indentation experiments. One of the main problems when deali...

    Authors: Paul Müller, Shada Abuhattum, Stephanie Möllmert, Elke Ulbricht, Anna V. Taubenberger and Jochen Guck

    Citation: BMC Bioinformatics 2019 20:465

    Content type: Methodology article

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  4. The efficient biological production of industrially and economically important compounds is a challenging problem. Brute-force determination of the optimal pathways to efficient production of a target chemical...

    Authors: Leanne S. Whitmore, Bernard Nguyen, Ali Pinar, Anthe George and Corey M. Hudson

    Citation: BMC Bioinformatics 2019 20:461

    Content type: Software

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  5. Host immune response is coordinated by a variety of different specialized cell types that vary in time and location. While host immune response can be studied using conventional low-dimensional approaches, adv...

    Authors: Arezo Torang, Paraag Gupta and David J. Klinke II

    Citation: BMC Bioinformatics 2019 20:433

    Content type: Research article

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  6. With the advent of array-based techniques to measure methylation levels in primary tumor samples, systematic investigations of methylomes have widely been performed on a large number of tumor entities. Most of...

    Authors: Pascal David Johann, Natalie Jäger, Stefan M. Pfister and Martin Sill

    Citation: BMC Bioinformatics 2019 20:428

    Content type: Methodology article

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  7. Advances in medical technology have allowed for customized prognosis, diagnosis, and treatment regimens that utilize multiple heterogeneous data sources. Multiple kernel learning (MKL) is well suited for the i...

    Authors: Christopher M. Wilson, Kaiqiao Li, Xiaoqing Yu, Pei-Fen Kuan and Xuefeng Wang

    Citation: BMC Bioinformatics 2019 20:426

    Content type: Software

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  8. Since the number of known lncRNA-disease associations verified by biological experiments is quite limited, it has been a challenging task to uncover human disease-related lncRNAs in recent years. Moreover, con...

    Authors: Jingwen Yu, Zhanwei Xuan, Xiang Feng, Quan Zou and Lei Wang

    Citation: BMC Bioinformatics 2019 20:396

    Content type: Research article

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  9. Alkaloids, a class of organic compounds that contain nitrogen bases, are mainly synthesized as secondary metabolites in plants and fungi, and they have a wide range of bioactivities. Although there are thousan...

    Authors: Ryohei Eguchi, Naoaki Ono, Aki Hirai Morita, Tetsuo Katsuragi, Satoshi Nakamura, Ming Huang, Md. Altaf-Ul-Amin and Shigehiko Kanaya

    Citation: BMC Bioinformatics 2019 20:380

    Content type: Research article

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  10. Unsupervised machine learning methods (deep learning) have shown their usefulness with noisy single cell mRNA-sequencing data (scRNA-seq), where the models generalize well, despite the zero-inflation of the da...

    Authors: Savvas Kinalis, Finn Cilius Nielsen, Ole Winther and Frederik Otzen Bagger

    Citation: BMC Bioinformatics 2019 20:379

    Content type: Methodology article

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  11. In spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensional...

    Authors: Marina Esteban-Medina, María Peña-Chilet, Carlos Loucera and Joaquín Dopazo

    Citation: BMC Bioinformatics 2019 20:370

    Content type: Research article

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  12. 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

    Content type: Methodology article

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  13. 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

    Content type: Methodology article

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  14. 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

    Content type: Methodology article

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  15. 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

    Content type: Methodology article

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  16. 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

    Content type: Methodology article

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  17. 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

    Content type: Research article

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  18. 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

    Content type: Research article

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  19. 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

    Content type: Research article

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2019 Journal Metrics

  • Citation Impact
    3.242 - 2-year Impact Factor
    3.213 - 5-year Impact Factor
    1.156 - Source Normalized Impact per Paper (SNIP)
    1.626 - SCImago Journal Rank (SJR)

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