Special Issue "Benchmarking Datasets in Bioinformatics"

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: 31 July 2020.

Special Issue Editor

Dr. Pufeng Du
E-Mail Website
Guest Editor
School of Computer Science and Technology, Tianjin University, Tianjin, China
Interests: big data analysis; biomedical data analysis; biomedical data management; pattern recognition; knowledge discovery

Special Issue Information

Dear Colleagues,

Over the last few years, computational predictions and identifications have become important methods in modern life science and medical science. Many efforts have been made in developing algorithms and computational models to identify molecular structures, functions, interactions, evolutions, and their relationships with complex disorders. To validate these methods, many benchmarking datasets have been constructed, applied, and released to the public domain. The benchmarking datasets are the basis of fair comparison and validation of computational methods. A thorough discussion and comparison of the datasets is necessary. In this Special Issue, we aim at providing deep insights into the construction procedures and the characters of different benchmarking datasets for the same or similar biological topics.

We expect manuscripts that can discuss different benchmarking datasets for a single bioinformatics topic or in a specific category of topics. The manuscripts can discuss and compare the constructions procedures, data sources, statistics of different datasets, as well as computational methods that are developed and evaluated on the datasets. There is no limit or fixed boundary of these comparisons. All kinds of discussions, comments, and comparisons are welcome. Particularly, a collection of different datasets for a single topic or similar topics are welcome, as this will facilitate further developments of computational methods. In general, all contributions regarding bioinformatics benchmarking datasets can be included in this Special Issue.

Dr. Pufeng 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. Data 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

  • Bioinformatics dataset
  • Dataset constructions
  • Dataset comparisons
  • Dataset qualities
  • Dataset comments
  • Dataset collections
  • Computational methods comparison based on datasets

Published Papers (2 papers)

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Open AccessData Descriptor
Matrix Metalloproteinases as Markers of Acute Inflammation Process in the Pulmonary Tuberculosis
Data 2019, 4(4), 137; https://doi.org/10.3390/data4040137 - 05 Oct 2019
Abstract
The main factors of pathogenesis in the pulmonary tuberculosis are not only the bacterial virulence and sensitivity of the host immune system to the pathogen, but also the degree of destruction of the lung tissue. Such destruction processes lead to the development of [...] Read more.
The main factors of pathogenesis in the pulmonary tuberculosis are not only the bacterial virulence and sensitivity of the host immune system to the pathogen, but also the degree of destruction of the lung tissue. Such destruction processes lead to the development of caverns, in most cases requiring surgical interventions besides the drug therapy. Identification of special biochemical markers allowing to assess the necessity of surgery or therapy prolongation remains a challenge. We consider promising markers—metalloproteinases—analyzing the data obtained from patients with pulmonary tuberculosis infected by different strains of Mycobacterium tuberculosis. We argue that the presence of drug-resistant strains in lungs leading to complicated clinical prognosis could be justified not only by the difference in medians of biomarkers concentration (as determined by the Mann–Whitney test for small samples), but also by the qualitative difference in their probability distributions (as detected by the Kolmogorov–Smirnov test). Our results and the provided raw data could be used for further development of precise biochemical data-based diagnostic and prognostic tools for pulmonary tuberculosis. Full article
(This article belongs to the Special Issue Benchmarking Datasets in Bioinformatics)
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Open AccessData Descriptor
Database for Gene Variants and Metabolic Networks Implicated in Familial Gastroschisis
Data 2019, 4(3), 97; https://doi.org/10.3390/data4030097 - 11 Jul 2019
Abstract
Gastroschisis is one of the most prevalent human birth defects concerning the ventral body wall development. Recent research has given a better understanding of gastroschisis pathogenesis through the identification of multiple novel pathogenetic pathways implicated in ventral body wall closure. Deciphering the underlying [...] Read more.
Gastroschisis is one of the most prevalent human birth defects concerning the ventral body wall development. Recent research has given a better understanding of gastroschisis pathogenesis through the identification of multiple novel pathogenetic pathways implicated in ventral body wall closure. Deciphering the underlying genetic factors segregating among familial gastroschisis allows better detection of novel susceptibility variants than the screening of pooled unrelated cases and controls, whereas bioinformatic-aided analysis can help to address new insights into human biology and molecular mechanisms involved in gastroschisis. Technological advances in DNA sequencing (Next Generation Sequencing), computing power, and machine learning techniques provide opportunities to the scientific communities to assess significant gaps in research and clinical practice. Thus, in an effort to study the role of gene variation in gastroschisis, we employed whole exome sequencing in a Mexican family with recurrence for gastroschisis. Stringent bioinformatic analyses were implemented to identify and predict pathogenetic networks comprised of potential gastroschisis predispositions. This is the first database for gene variants and metabolic networks implicated in familial gastroschisis. The dataset provides information on gastroschisis annotated genes, gene variants, and metabolic networks and constitutes a useful source to enhance further investigations in gastroschisis. Full article
(This article belongs to the Special Issue Benchmarking Datasets in Bioinformatics)
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