Skip to main content

Big Data and Machine Learning in Bioinformatics and Medical Informatics

This collection comprises articles presenting novel developments in artificial intelligence, big data analysis and cloud computing in both biology and medicine, and their applications to the analysis of high-throughput biological and medical data, including image and microbiome data, as well methodologies and tools to manage complex electronic health information and records.New Content Item © NicoElNino (iStock)

This includes, but is not restricted to, the design, implementation, use and evaluation of artificial intelligence and machine learning methods, software packages and novel webservers that allow big data analysis.

Please email Alison Cuff, the inhouse editor for BMC Bioinformatics, ( or Piero Lo Monaco, the inhouse editor for BMC Medical Informatics and Decision Making ( if you would like more information about this collection.

Submission Status: Closed

Meet the Guest Editors

Frank Eisenhaber (BMC Bioinformatics)

New Content ItemFrank Eisenhaber studied at the Humboldt-University in Berlin, at the Pirogov Medical University in Moscow and received the PhD in molecular biology from the Engelhardt Institute of Molecular Biology in Moscow 1988. He worked at the EMBL in Heidelberg (1991-1999), at the Institute of Molecular Pathology (IMP) in Vienna (1999-2007) and at A*STAR Singapore (since 2007). During 2007-2020, he was the Executive Director of the Bioinformatics Institute of A*STAR. Frank Eisenhaber’s research interest is focused on the discovery of new biomolecular mechanisms from biological and medical data and the functional characterization of yet uncharacterized genes and pathways. Frank Eisenhaber is one of the scientists credited with the discovery of the SET domain methyltransferases, ATGL, kleisins, many new protein domain functions (for example in the GPI lipid anchor biosynthsis pathway), with the development of accurate prediction tools for posttranslational modifications and subcellular localizations and with algorithms for omics data analysis. 

Somali Chaterji (BMC Bioinformatics)

New Content ItemSomali Chaterji (pronounced shoh-MAH-lee CHA-ter-jee) is an Assistant Professor in the Department of Agricultural and Biological Engineering at Purdue University, where she leads the Innovatory for Cells and Neural Machines [ICAN]. ICAN specializes in developing algorithms and statistical models for IoT and edge computing on the one hand and genome engineering on the other. ICAN is driven to learn and inter-change tools between these two thrusts with the goal to create new algorithms to understand the genome of living cells, on the one hand, and to enable IoT sensors to perceive and actuate on the other. 

Khanh N.Q. Le (BMC Bioinformatics)

New Content ItemKhanh N.Q. Le is an assistant professor with the College of Medicine, Taipei Medical University (TMU), Taiwan. Prior to joining TMU, he was a Research Fellow at the School of Humanities, Nanyang Technological University (NTU), Singapore. His research interests are to apply AI (machine learning, deep learning, and natural language processing) in multidisciplinary studies, especially Bioinformatics, Computational Biology, Biomedical Informatics, and Radiomics.

Placide Poba-Nzaou (BMC Medical Informatics and Decision Making)

New Content ItemDr. Placide Poba-Nzaou is a Professor of Information Systems and Human Resources at the University of Quebec in Montreal. His research interests include Digital Health, Information Systems and Human Resources.

  1. Long non-coding RNA (lncRNA) closely associates with numerous biological processes, and with many diseases. Therefore, lncRNA-disease association prediction helps obtain relevant biological information and und...

    Authors: Hua Zhong, Jing Luo, Lin Tang, Shicheng Liao, Zhonghao Lu, Guoliang Lin, Robert W. Murphy and Lin Liu
    Citation: BMC Bioinformatics 2023 24:234
  2. Clinical studies have shown that miRNAs are closely related to human health. The study of potential associations between miRNAs and diseases will contribute to a profound understanding of the mechanism of dise...

    Authors: Min Chen, Yingwei Deng, Zejun Li, Yifan Ye and Ziyi He
    Citation: BMC Bioinformatics 2023 24:229
  3. Epilepsy is a neurological disorder that is usually detected by electroencephalogram (EEG) signals. Since manual examination of epilepsy seizures is a laborious and time-consuming process, lots of automatic ep...

    Authors: Wenna Chen, Yixing Wang, Yuhao Ren, Hongwei Jiang, Ganqin Du, Jincan Zhang and Jinghua Li
    Citation: BMC Medical Informatics and Decision Making 2023 23:96
  4. Accurately classifying complex diseases is crucial for diagnosis and personalized treatment. Integrating multi-omics data has been demonstrated to enhance the accuracy of analyzing and classifying complex dise...

    Authors: Yating Zhong, Yuzhong Peng, Yanmei Lin, Dingjia Chen, Hao Zhang, Wen Zheng, Yuanyuan Chen and Changliang Wu
    Citation: BMC Medical Informatics and Decision Making 2023 23:82
  5. The identification of disease-related genes is of great significance for the diagnosis and treatment of human disease. Most studies have focused on developing efficient and accurate computational methods to pr...

    Authors: Linlin Zhang, Dianrong Lu, Xuehua Bi, Kai Zhao, Guanglei Yu and Na Quan
    Citation: BMC Bioinformatics 2023 24:162
  6. Intraoperative blood transfusion is associated with adverse events. We aimed to establish a machine learning model to predict the probability of intraoperative blood transfusion during intracranial aneurysm su...

    Authors: Shugen Xiao, Fan Liu, Liyuan Yu, Xiaopei Li, Xihong Ye and Xingrui Gong
    Citation: BMC Medical Informatics and Decision Making 2023 23:71
  7. Bronchopulmonary Dysplasia (BPD) has a high incidence and affects the health of preterm infants. Cuproptosis is a novel form of cell death, but its mechanism of action in the disease is not yet clear. Machine ...

    Authors: Mingxuan Jia, Jieyi Li, Jingying Zhang, Ningjing Wei, Yating Yin, Hui Chen, Shixing Yan and Yong Wang
    Citation: BMC Medical Informatics and Decision Making 2023 23:69
  8. Identification of hot spots in protein–DNA binding interfaces is extremely important for understanding the underlying mechanisms of protein–DNA interactions and drug design. Since experimental methods for iden...

    Authors: Yu Sun, Hongwei Wu, Zhengrong Xu, Zhenyu Yue and Ke Li
    Citation: BMC Bioinformatics 2023 24:129
  9. Using visual, biological, and electronic health records data as the sole input source, pretrained convolutional neural networks and conventional machine learning methods have been heavily employed for the iden...

    Authors: Mpho Mokoatle, Vukosi Marivate, Darlington Mapiye, Riana Bornman and Vanessa. M. Hayes
    Citation: BMC Bioinformatics 2023 24:112
  10. Drug‒drug interactions (DDIs) are reactions between two or more drugs, i.e., possible situations that occur when two or more drugs are used simultaneously. DDIs act as an important link in both drug developmen...

    Authors: Zihao Yang, Kuiyuan Tong, Shiyu Jin, Shiyan Wang, Chao Yang and Feng Jiang
    Citation: BMC Bioinformatics 2023 24:110
  11. Although research on non-coding RNAs (ncRNAs) is a hot topic in life sciences, the functions of numerous ncRNAs remain unclear. In recent years, researchers have found that ncRNAs of the same family have simil...

    Authors: Kai Chen, Xiaodong Zhu, Jiahao Wang, Lei Hao, Zhen Liu and Yuanning Liu
    Citation: BMC Bioinformatics 2023 24:68