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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. Diverse microbiome communities drive biogeochemical processes and evolution of animals in their ecosystems. Many microbiome projects have demonstrated the power of using metagenomics to understand the structur...

    Authors: Eliza Dhungel, Yassin Mreyoud, Ho-Jin Gwak, Ahmad Rajeh, Mina Rho and Tae-Hyuk Ahn

    Citation: BMC Bioinformatics 2021 22:25

    Content type: Software

    Published on:

  3. Querying drug-induced gene expression profiles with machine learning method is an effective way for revealing drug mechanism of actions (MOAs), which is strongly supported by the growth of large scale and high...

    Authors: Shengqiao Gao, Lu Han, Dan Luo, Gang Liu, Zhiyong Xiao, Guangcun Shan, Yongxiang Zhang and Wenxia Zhou

    Citation: BMC Bioinformatics 2021 22:17

    Content type: Research article

    Published on:

  5. One of the current directions of precision medicine is the use of computational methods to aid in the diagnosis, prognosis, and treatment of disease based on data driven approaches. For instance, in oncology, ...

    Authors: Joshua D. Mannheimer, Ashok Prasad and Daniel L. Gustafson

    Citation: BMC Bioinformatics 2021 22:15

    Content type: Research article

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  7. Accurate prediction of binding between class I human leukocyte antigen (HLA) and neoepitope is critical for target identification within personalized T-cell based immunotherapy. Many recent prediction tools de...

    Authors: Yilin Ye, Jian Wang, Yunwan Xu, Yi Wang, Youdong Pan, Qi Song, Xing Liu and Ji Wan

    Citation: BMC Bioinformatics 2021 22:7

    Content type: Research article

    Published on:

  8. Protein phosphoglycerylation, the addition of a 1,3-bisphosphoglyceric acid (1,3-BPG) to a lysine residue of a protein and thus to form a 3-phosphoglyceryl-lysine, is a reversible and non-enzymatic post-transl...

    Authors: Kai-Yao Huang, Fang-Yu Hung, Hui-Ju Kao, Hui-Hsuan Lau and Shun-Long Weng

    Citation: BMC Bioinformatics 2020 21:568

    Content type: Research article

    Published on:

  10. The large-scale availability of whole-genome sequencing profiles from bulk DNA sequencing of cancer tissues is fueling the application of evolutionary theory to cancer. From a bulk biopsy, subclonal deconvolut...

    Authors: Giulio Caravagna, Guido Sanguinetti, Trevor A. Graham and Andrea Sottoriva

    Citation: BMC Bioinformatics 2020 21:531

    Content type: Software

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  12. Protein kinases are a large family of druggable proteins that are genomically and proteomically altered in many human cancers. Kinase-targeted drugs are emerging as promising avenues for personalized medicine ...

    Authors: Liang-Chin Huang, Wayland Yeung, Ye Wang, Huimin Cheng, Aarya Venkat, Sheng Li, Ping Ma, Khaled Rasheed and Natarajan Kannan

    Citation: BMC Bioinformatics 2020 21:520

    Content type: Research article

    Published on:

  14. The formation of contacts among protein secondary structure elements (SSEs) is an important step in protein folding as it determines topology of protein tertiary structure; hence, inferring inter-SSE contacts ...

    Authors: Qi Zhang, Jianwei Zhu, Fusong Ju, Lupeng Kong, Shiwei Sun, Wei-Mou Zheng and Dongbo Bu

    Citation: BMC Bioinformatics 2020 21:503

    Content type: Methodology

    Published on:

  15. The use of predictive gene signatures to assist clinical decision is becoming more and more important. Deep learning has a huge potential in the prediction of phenotype from gene expression profiles. However, ...

    Authors: Blaise Hanczar, Farida Zehraoui, Tina Issa and Mathieu Arles

    Citation: BMC Bioinformatics 2020 21:501

    Content type: Research article

    Published on:

  17. 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

    Published on:

  18. 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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  19. 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

    Published on:

  20. 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

    Published on:

  21. 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

    Published on:

  23. 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

    Published on:

  26. 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

    Published on:

  27. 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

    Published on:

    The Correction to this article has been published in BMC Bioinformatics 2020 21:495

  28. 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

    Published on:

  29. 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

    Published on:

  30. 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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  34. 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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  35. 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

    Published on:

  37. 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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  38. 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

    Published on:

  39. 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

    Published on:

  42. 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

    Published on:

  44. 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

    Published on:

  45. 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

    Published on:

  46. 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

    Published on:

  47. 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

    Published on:

  49. 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

    Published on:

  50. 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

    Published on:

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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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BMC Bioinformatics

ISSN: 1471-2105

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