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Machine Learning and Artificial Intelligence in Bioinformatics

  1. Content type: Methodology article

    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

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  2. Content type: Research article

    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

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  3. Content type: Research article

    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

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  4. Content type: Methodology article

    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

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  5. Content type: Research article

    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

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  6. 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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  7. 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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  8. 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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  9. 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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  10. 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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  11. 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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  12. 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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  13. 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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  14. Content type: Research article

    Modern plant taxonomy reflects phylogenetic relationships among taxa based on proposed morphological and genetic similarities. However, taxonomical relation is not necessarily reflected by close overall resemb...

    Authors: Marco Seeland, Michael Rzanny, David Boho, Jana Wäldchen and Patrick Mäder

    Citation: BMC Bioinformatics 2019 20:4

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  15. Content type: Methodology article

    Predicting prognosis in patients from large-scale genomic data is a fundamentally challenging problem in genomic medicine. However, the prognosis still remains poor in many diseases. The poor prognosis may be ...

    Authors: Jie Hao, Youngsoon Kim, Tae-Kyung Kim and Mingon Kang

    Citation: BMC Bioinformatics 2018 19:510

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