Omics Data Analysis and Integration in Complex Diseases, 2nd Edition

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Biomedical Engineering and Materials".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 3490

Special Issue Editors


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Guest Editor
1. Department of Statistics, Faculty of Medicine, University of Granada, 18071 Granada, Spain
2. Centre for Genomics and Oncological Research, Pfizer-University of Granada-Andalusian Regional Government, 18016 Granada, Spain
Interests: bioinformatics and biostatistics; computational biomedicine; omics data analysis
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Centre for Genomics and Oncological Research, Pfizer-University of Granada-Andalusian Regional Government, 18016 Granada, Spain
Interests: bioinformatics; autoimmune diseases; computational biology
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Data Science for Health Research Unit, Fondazione Bruno Kessler, 38123 Trento, Italy
Interests: bioinformatics; computational biology; omics data analysis; machine learning

Special Issue Information

Dear Colleagues,

The emergence of omics technologies has revolutionized research in biomedicine, allowing us to analyze molecular mechanisms of complex diseases at an unprecedented scale. The analysis of omics data offers enormous possibilities for applications in biomarker discovery, patient stratification and disease classification or drug discovery, and they are fueling precision medicine strategies.

In this context, the development of statistical and computational methods to properly analyze and extract knowledge from large and heterogeneous omics datasets has become a major focus of research. Additionally, the availability of studies that generate multi-omics data from the same cohort of patients has opened new challengences in the field, as the integration of multi-omics data can provide more accurate and robust results than the analysis of a single type of omics data.

In this Special Issue, we will focus on new statistical and computational methods for omics data analysis and integration in complex diseases, new software and bioinformatics pipelines to analyze omics data and applications in biomarker discovery, disease classification, patient stratification, drug repurposing, and drug discovery. Validation experiments are required.

Dr. Pedro Carmona-Sáez
Dr. Daniel Toro-Domínguez
Dr. Jordi Martorell-Marugán
Guest Editors

Manuscript Submission Information

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Keywords

  • omics data analysis
  • omics data integration
  • biomarker discovery
  • disease classification
  • precision medicine
  • machine learning
  • bioinformatics
  • biostatistics

Published Papers (1 paper)

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Research

22 pages, 3414 KiB  
Article
Machine Learning Data Analysis Highlights the Role of Parasutterella and Alloprevotella in Autism Spectrum Disorders
by Daniele Pietrucci, Adelaide Teofani, Marco Milanesi, Bruno Fosso, Lorenza Putignani, Francesco Messina, Graziano Pesole, Alessandro Desideri and Giovanni Chillemi
Biomedicines 2022, 10(8), 2028; https://doi.org/10.3390/biomedicines10082028 - 19 Aug 2022
Cited by 13 | Viewed by 2930
Abstract
In recent years, the involvement of the gut microbiota in disease and health has been investigated by sequencing the 16S gene from fecal samples. Dysbiotic gut microbiota was also observed in Autism Spectrum Disorder (ASD), a neurodevelopmental disorder characterized by gastrointestinal symptoms. However, [...] Read more.
In recent years, the involvement of the gut microbiota in disease and health has been investigated by sequencing the 16S gene from fecal samples. Dysbiotic gut microbiota was also observed in Autism Spectrum Disorder (ASD), a neurodevelopmental disorder characterized by gastrointestinal symptoms. However, despite the relevant number of studies, it is still difficult to identify a typical dysbiotic profile in ASD patients. The discrepancies among these studies are due to technical factors (i.e., experimental procedures) and external parameters (i.e., dietary habits). In this paper, we collected 959 samples from eight available projects (540 ASD and 419 Healthy Controls, HC) and reduced the observed bias among studies. Then, we applied a Machine Learning (ML) approach to create a predictor able to discriminate between ASD and HC. We tested and optimized three algorithms: Random Forest, Support Vector Machine and Gradient Boosting Machine. All three algorithms confirmed the importance of five different genera, including Parasutterella and Alloprevotella. Furthermore, our results show that ML algorithms could identify common taxonomic features by comparing datasets obtained from countries characterized by latent confounding variables. Full article
(This article belongs to the Special Issue Omics Data Analysis and Integration in Complex Diseases, 2nd Edition)
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