Special Issue "Current Trends and Developments in Bioinformatics and Statistical Research from a Biomedical Aspect"

A special issue of BioMedInformatics (ISSN 2673-7426).

Deadline for manuscript submissions: 15 July 2022.

Special Issue Editor

Dr. Qian Du
E-Mail Website
Guest Editor
School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, NE, USA
Interests: omics and bioinformatics; statistical causal inference, artificial intelligence; immunology, radiomics, oncology

Special Issue Information

Dear Colleagues,

In the past decade, the advancement of next-generation sequencing technologies has produced a large number of different types of omics data, such as genomics, transcriptomics, radiomics, metabolomics, epigenomics, etc. More systematic ways to collect and store health and disease information from patients can also accumulate tons of informatics. The characterization of diseases, as well as patients, has never been so detailed. For example, the development of radiomics can extract far more disease information that is not visible to doctors.

The information explosion in the biomedical area makes it possible to provide tailored treatment to each patient instead of treating them as an average, which is the core value of precision medicine. At the same time, it has brought many challenges in the mining, processing, integrating, and further modelling of large-scale data from different levels of patients.

In order for biomedical data to be manipulated appropriately, and for physicians and researchers to have a rule of thumb to follow, Biomedinformatics introduces this Special Issue. We encourage contributions to the development and applications of bioinformatics and statistical methods in the context of biomedicine. Original studies, as well as insightful reviews, are very welcome to be published under this Special Issue.

Dr. Qian Du
Guest Editor


Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. BioMedInformatics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • genome and sequence analysis
  • machine learning and artificial intelligence in bioinformatics
  • clinical informatics
  • statistical genetics
  • computational bio-modeling
  • computational pharmacology

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
A Stochastic Multivariate Irregularly Sampled Time Series Imputation Method for Electronic Health Records
BioMedInformatics 2021, 1(3), 166-181; https://doi.org/10.3390/biomedinformatics1030011 - 16 Nov 2021
Viewed by 364
Abstract
Electronic health records (EHRs) can be very difficult to analyze since they usually contain many missing values. To build an efficient predictive model, a complete dataset is necessary. An EHR usually contains high-dimensional longitudinal time series data. Most commonly used imputation methods do [...] Read more.
Electronic health records (EHRs) can be very difficult to analyze since they usually contain many missing values. To build an efficient predictive model, a complete dataset is necessary. An EHR usually contains high-dimensional longitudinal time series data. Most commonly used imputation methods do not consider the importance of temporal information embedded in EHR data. Besides, most time-dependent neural networks such as recurrent neural networks (RNNs) inherently consider the time steps to be equal, which in many cases, is not appropriate. This study presents a method using the gated recurrent unit (GRU), neural ordinary differential equations (ODEs), and Bayesian estimation to incorporate the temporal information and impute sporadically observed time series measurements in high-dimensional EHR data. Full article
Show Figures

Figure 1

Article
Analyzing Large Microbiome Datasets Using Machine Learning and Big Data
BioMedInformatics 2021, 1(3), 138-165; https://doi.org/10.3390/biomedinformatics1030010 - 08 Nov 2021
Viewed by 396
Abstract
Metagenomics promises to provide new valuable insights into the role of microbiomes in eukaryotic hosts such as humans. Due to the decreasing costs for sequencing, public and private repositories for human metagenomic datasets are growing fast. Metagenomic datasets can contain terabytes of raw [...] Read more.
Metagenomics promises to provide new valuable insights into the role of microbiomes in eukaryotic hosts such as humans. Due to the decreasing costs for sequencing, public and private repositories for human metagenomic datasets are growing fast. Metagenomic datasets can contain terabytes of raw data, which is a challenge for data processing but also an opportunity for advanced machine learning methods like deep learning that require large datasets. However, in contrast to classical machine learning algorithms, the use of deep learning in metagenomics is still an exception. Regardless of the algorithms used, they are usually not applied to raw data but require several preprocessing steps. Performing this preprocessing and the actual analysis in an automated, reproducible, and scalable way is another challenge. This and other challenges can be addressed by adjusting known big data methods and architectures to the needs of microbiome analysis and DNA sequence processing. A conceptual architecture for the use of machine learning and big data on metagenomic data sets was recently presented and initially validated to analyze the rumen microbiome. The same architecture can be used for clinical purposes as is discussed in this paper. Full article
Show Figures

Figure 1

Back to TopTop