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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. Many studies prove that miRNAs have significant roles in diagnosing and treating complex human diseases. However, conventional biological experiments are too costly and time-consuming to identify unconfirmed m...

    Authors: Lei Zhang, Bailong Liu, Zhengwei Li, Xiaoyan Zhu, Zhizhen Liang and Jiyong An

    Citation: BMC Bioinformatics 2020 21:470

    Content type: Methodology article

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  2. MicroRNAs (miRNAs) are non-coding RNAs with regulatory functions. Many studies have shown that miRNAs are closely associated with human diseases. Among the methods to explore the relationship between the miRNA...

    Authors: Tian-Ru Wu, Meng-Meng Yin, Cui-Na Jiao, Ying-Lian Gao, Xiang-Zhen Kong and Jin-Xing Liu

    Citation: BMC Bioinformatics 2020 21:454

    Content type: Methodology article

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  3. Recent studies have shown that N6-methyladenosine (m6A) plays a critical role in numbers of biological processes and complex human diseases. However, the regulatory mechanisms of most methylation sites remain unc...

    Authors: Lin Zhang, Shutao Chen, Jingyi Zhu, Jia Meng and Hui Liu

    Citation: BMC Bioinformatics 2020 21:447

    Content type: Research article

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  4. As a machine learning method with high performance and excellent generalization ability, extreme learning machine (ELM) is gaining popularity in various studies. Various ELM-based methods for different fields ...

    Authors: Liang-Rui Ren, Ying-Lian Gao, Jin-Xing Liu, Junliang Shang and Chun-Hou Zheng

    Citation: BMC Bioinformatics 2020 21:445

    Content type: Methodology article

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  5. A typical task in bioinformatics consists of identifying which features are associated with a target outcome of interest and building a predictive model. Automated machine learning (AutoML) systems such as the...

    Authors: Elisabetta Manduchi, Weixuan Fu, Joseph D. Romano, Stefano Ruberto and Jason H. Moore

    Citation: BMC Bioinformatics 2020 21:430

    Content type: Methodology article

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  6. The treatment of complex diseases by taking multiple drugs becomes increasingly popular. However, drug-drug interactions (DDIs) may give rise to the risk of unanticipated adverse effects and even unknown toxic...

    Authors: Yue-Hua Feng, Shao-Wu Zhang and Jian-Yu Shi

    Citation: BMC Bioinformatics 2020 21:419

    Content type: Methodology article

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  7. A large number of experimental studies show that the mutation and regulation of long non-coding RNAs (lncRNAs) are associated with various human diseases. Accurate prediction of lncRNA-disease associations can...

    Authors: Yuan Zhang, Fei Ye, Dapeng Xiong and Xieping Gao

    Citation: BMC Bioinformatics 2020 21:377

    Content type: Research article

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  8. In this era of data science-driven bioinformatics, machine learning research has focused on feature selection as users want more interpretation and post-hoc analyses for biomarker detection. However, when ther...

    Authors: Samir Rachid Zaim, Colleen Kenost, Joanne Berghout, Wesley Chiu, Liam Wilson, Hao Helen Zhang and Yves A. Lussier

    Citation: BMC Bioinformatics 2020 21:374

    Content type: Methodology article

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  9. About 90% of patients who have diabetes suffer from Type 2 DM (T2DM). Many studies suggest using the significant role of lncRNAs to improve the diagnosis of T2DM. Machine learning and Data Mining techniques ar...

    Authors: Faranak Kazerouni, Azadeh Bayani, Farkhondeh Asadi, Leyla Saeidi, Nasrin Parvizi and Zahra Mansoori

    Citation: BMC Bioinformatics 2020 21:372

    Content type: Research article

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  10. Machine learning models for repeated measurements are limited. Using topological data analysis (TDA), we present a classifier for repeated measurements which samples from the data space and builds a network gr...

    Authors: Henri Riihimäki, Wojciech Chachólski, Jakob Theorell, Jan Hillert and Ryan Ramanujam

    Citation: BMC Bioinformatics 2020 21:336

    Content type: Research article

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  11. Gene expression signatures for the prediction of differential survival of patients undergoing anti-cancer therapies are of great interest because they can be used to prospectively stratify patients entering ne...

    Authors: Joachim Theilhaber, Marielle Chiron, Jennifer Dreymann, Donald Bergstrom and Jack Pollard

    Citation: BMC Bioinformatics 2020 21:333

    Content type: Methodology article

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  12. Drug repurposing aims to detect the new therapeutic benefits of the existing drugs and reduce the spent time and cost of the drug development projects. The synthetic repurposing of drugs may prove to be more u...

    Authors: Yosef Masoudi-Sobhanzadeh and Ali Masoudi-Nejad

    Citation: BMC Bioinformatics 2020 21:313

    Content type: Methodology article

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  13. Most biomedical information extraction focuses on binary relations within single sentences. However, extracting n-ary relations that span multiple sentences is in huge demand. At present, in the cross-sentence...

    Authors: Di Zhao, Jian Wang, Yijia Zhang, Xin Wang, Hongfei Lin and Zhihao Yang

    Citation: BMC Bioinformatics 2020 21:312

    Content type: Methodology article

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  14. Random forest based variable importance measures have become popular tools for assessing the contributions of the predictor variables in a fitted random forest. In this article we reconsider a frequently used ...

    Authors: Dries Debeer and Carolin Strobl

    Citation: BMC Bioinformatics 2020 21:307

    Content type: Methodology article

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  15. A common yet still manual task in basic biology research, high-throughput drug screening and digital pathology is identifying the number, location, and type of individual cells in images. Object detection meth...

    Authors: Jane Hung, Allen Goodman, Deepali Ravel, Stefanie C. P. Lopes, Gabriel W. Rangel, Odailton A. Nery, Benoit Malleret, Francois Nosten, Marcus V. G. Lacerda, Marcelo U. Ferreira, Laurent Rénia, Manoj T. Duraisingh, Fabio T. M. Costa, Matthias Marti and Anne E. Carpenter

    Citation: BMC Bioinformatics 2020 21:300

    Content type: Software

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  16. Even though we have established a few risk factors for metastatic breast cancer (MBC) through epidemiologic studies, these risk factors have not proven to be effective in predicting an individual’s risk of develo...

    Authors: Xia Jiang, Alan Wells, Adam Brufsky, Darshan Shetty, Kahmil Shajihan and Richard E. Neapolitan

    Citation: BMC Bioinformatics 2020 21:298

    Content type: Methodology article

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  17. Phytochemicals and other molecules in foods elicit positive health benefits, often by poorly established or unknown mechanisms. While there is a wealth of data on the biological and biophysical properties of d...

    Authors: Kenneth E. Westerman, Sean Harrington, Jose M. Ordovas and Laurence D. Parnell

    Citation: BMC Bioinformatics 2020 21:238

    Content type: Software

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  18. The interactions between proteins and aptamers are prevalent in organisms and play an important role in various life activities. Thanks to the rapid accumulation of protein-aptamer interaction data, it is nece...

    Authors: Jianwei Li, Xiaoyu Ma, Xichuan Li and Junhua Gu

    Citation: BMC Bioinformatics 2020 21:236

    Content type: Methodology article

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  19. The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are avai...

    Authors: Hesham ElAbd, Yana Bromberg, Adrienne Hoarfrost, Tobias Lenz, Andre Franke and Mareike Wendorff

    Citation: BMC Bioinformatics 2020 21:235

    Content type: Methodology article

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  20. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma and accounts for cancer-related deaths. Survival rates are very low when the tumor is discovered in the late-stage. Th...

    Authors: Fangjun Li, Mu Yang, Yunhe Li, Mingqiang Zhang, Wenjuan Wang, Dongfeng Yuan and Dongqi Tang

    Citation: BMC Bioinformatics 2020 21:232

    Content type: Research article

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  21. Inferring diseases related to the patient’s electronic medical records (EMRs) is of great significance for assisting doctor diagnosis. Several recent prediction methods have shown that deep learning-based meth...

    Authors: Tong Wang, Ping Xuan, Zonglin Liu and Tiangang Zhang

    Citation: BMC Bioinformatics 2020 21:230

    Content type: Methodology article

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  22. The latest works on CRISPR genome editing tools mainly employs deep learning techniques. However, deep learning models lack explainability and they are harder to reproduce. We were motivated to build an accura...

    Authors: Ali Haisam Muhammad Rafid, Md. Toufikuzzaman, Mohammad Saifur Rahman and M. Sohel Rahman

    Citation: BMC Bioinformatics 2020 21:223

    Content type: Methodology Article

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  23. Enzymatic and chemical reactions are key for understanding biological processes in cells. Curated databases of chemical reactions exist but these databases struggle to keep up with the exponential growth of th...

    Authors: Emily K. Mallory, Matthieu de Rochemonteix, Alex Ratner, Ambika Acharya, Chris Re, Roselie A. Bright and Russ B. Altman

    Citation: BMC Bioinformatics 2020 21:217

    Content type: Research article

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  24. Semantic resources such as knowledge bases contains high-quality-structured knowledge and therefore require significant effort from domain experts. Using the resources to reinforce the information retrieval fr...

    Authors: Zhijing Li, Yuchen Lian, Xiaoyong Ma, Xiangrong Zhang and Chen Li

    Citation: BMC Bioinformatics 2020 21:213

    Content type: Methodology article

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  25. Apoptosis, also called programmed cell death, refers to the spontaneous and orderly death of cells controlled by genes in order to maintain a stable internal environment. Identifying the subcellular location o...

    Authors: Lei Du, Qingfang Meng, Yuehui Chen and Peng Wu

    Citation: BMC Bioinformatics 2020 21:212

    Content type: Methodology Article

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  26. The aim of gene expression-based clinical modelling in tumorigenesis is not only to accurately predict the clinical endpoints, but also to reveal the genome characteristics for downstream analysis for the purp...

    Authors: Yiru Zhao, Yifan Zhou, Yuan Liu, Yinyi Hao, Menglong Li, Xuemei Pu, Chuan Li and Zhining Wen

    Citation: BMC Bioinformatics 2020 21:195

    Content type: Methodology article

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  27. The necessity to analyze medium-throughput data in epidemiological studies with small sample size, particularly when studying biomedical data may hinder the use of classical statistical methods. Support vector...

    Authors: Hector Sanz, Ferran Reverter and Clarissa Valim

    Citation: BMC Bioinformatics 2020 21:193

    Content type: Methodology article

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    The Correction to this article has been published in BMC Bioinformatics 2020 21:371

  28. In addition to causing the pandemic influenza outbreaks of 1918 and 2009, subtype H1N1 influenza A viruses (IAVs) have caused seasonal epidemics since 1977. Antigenic property of influenza viruses are determin...

    Authors: Lei Li, Deborah Chang, Lei Han, Xiaojian Zhang, Joseph Zaia and Xiu-Feng Wan

    Citation: BMC Bioinformatics 2020 21:182

    Content type: Methodology article

    Published on:

  29. Recently, DNA methylation has drawn great attention due to its strong correlation with abnormal gene activities and informative representation of the cancer status. As a number of studies focus on DNA methylat...

    Authors: Joungmin Choi and Heejoon Chae

    Citation: BMC Bioinformatics 2020 21:181

    Content type: Research Article

    Published on:

  30. While clinical trials are considered the gold standard for detecting adverse events, often these trials are not sufficiently powered to detect difficult to observe adverse events. We developed a preliminary ap...

    Authors: Chathuri Daluwatte, Peter Schotland, David G. Strauss, Keith K. Burkhart and Rebecca Racz

    Citation: BMC Bioinformatics 2020 21:163

    Content type: Research article

    Published on:

  31. Recent years have witnessed an increasing interest in multi-omics data, because these data allow for better understanding complex diseases such as cancer on a molecular system level. In addition, multi-omics d...

    Authors: Amina Lemsara, Salima Ouadfel and Holger Fröhlich

    Citation: BMC Bioinformatics 2020 21:146

    Content type: Methodology article

    Published on:

  32. Feature selection in class-imbalance learning has gained increasing attention in recent years due to the massive growth of high-dimensional class-imbalanced data across many scientific fields. In addition to r...

    Authors: Guang-Hui Fu, Yuan-Jiao Wu, Min-Jie Zong and Jianxin Pan

    Citation: BMC Bioinformatics 2020 21:121

    Content type: Research Article

    Published on:

  33. The ability to confidently predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics. Yet, the goal of developing actionable, robust, and reproducible predictive signatu...

    Authors: Aaron M. Smith, Jonathan R. Walsh, John Long, Craig B. Davis, Peter Henstock, Martin R. Hodge, Mateusz Maciejewski, Xinmeng Jasmine Mu, Stephen Ra, Shanrong Zhao, Daniel Ziemek and Charles K. Fisher

    Citation: BMC Bioinformatics 2020 21:119

    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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