Bioinformatics and Machine Learning Applications in Precision Medicine: Exploring the Landscape

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Personalized Therapy and Drug Delivery".

Deadline for manuscript submissions: closed (25 November 2024) | Viewed by 1999

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Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
Interests: medical imaging; computational hemodynamics; simulation modeling experience
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Special Issue Information

Dear Colleagues,

In the field of precision medicine, the synergy of bioinformatics and machine learning (ML) has emerged as a powerful force, revolutionizing healthcare by harnessing advanced algorithms to analyze extensive datasets, encompassing genomic information, clinical records, and patient data, and comprehensively addressing individual healthcare needs. This research explores the amalgamation of bioinformatics and ML techniques, aiming to pave the way for personalized healthcare decision-making and improved medical outcomes.

Machine learning (ML) plays a pivotal role in predicting disease risks, personalizing treatment recommendations, aiding drug discovery, decoding genetic data, and optimizing healthcare operations. By customizing healthcare decisions based on unique genetic and clinical attributes, ML empowers precision medicine, leading to enhanced healthcare outcomes and efficiency. We invite authors to contribute their expertise and research in this transformative field, encouraging collaborative efforts to further advance our understanding and applications in precision medicine.

The integration of bioinformatics and machine learning holds the potential to revolutionize precision medicine. By analyzing data from previous patients, machine learning models can identify future patients who may benefit from specific treatments. Furthermore, machine learning algorithms can scrutinize electronic health records to pinpoint risk factors for diseases, enabling personalized treatment plans tailored to an individual's genetic makeup, lifestyle, and environment. Bioinformatics applications extend to drug discovery, personalized medicine, preventative medicine, and gene therapy, guiding the exploration of the precision medicine landscape. This proposal aims to create a Special Issue that delves deeper into this vital and transformative intersection of bioinformatics and machine learning in precision medicine. We therefore invite authors to collaborate and contribute their expertise in bioinformatics and ML for precision medicine research. We anticipate that this initiative will lead to significant advancements in healthcare outcomes, offering a tailored approach to medical care and improving patient well-being.

Prof. Dr. Kelvin K. L. Wong
Guest Editor

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Keywords

  • machine learning
  • bioinformatics
  • precision medicine

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Published Papers (1 paper)

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Research

17 pages, 1562 KiB  
Article
Machine Learning for the Identification of Key Predictors to Bayley Outcomes: A Preterm Cohort Study
by Petra Grđan Stevanović, Nina Barišić, Iva Šunić, Ann-Marie Malby Schoos, Branka Bunoza, Ruža Grizelj, Ana Bogdanić, Ivan Jovanović and Mario Lovrić
J. Pers. Med. 2024, 14(9), 922; https://doi.org/10.3390/jpm14090922 - 30 Aug 2024
Viewed by 1529
Abstract
Background: The aim of this study was to understand how neurological development of preterm infants can be predicted at earlier stages and explore the possibility of applying personalized approaches. Methods: Our study included a cohort of 64 preterm infants, between 24 and 34 [...] Read more.
Background: The aim of this study was to understand how neurological development of preterm infants can be predicted at earlier stages and explore the possibility of applying personalized approaches. Methods: Our study included a cohort of 64 preterm infants, between 24 and 34 weeks of gestation. Linear and nonlinear models were used to evaluate feature predictability to Bayley outcomes at the corrected age of 2 years. The outcomes were classified into motor, language, cognitive, and socio-emotional categories. Pediatricians’ opinions about the predictability of the same features were compared with machine learning. Results: According to our linear analysis sepsis, brain MRI findings and Apgar score at 5th minute were predictive for cognitive, Amiel-Tison neurological assessment at 12 months of corrected age for motor, while sepsis was predictive for socio-emotional outcome. None of the features were predictive for language outcome. Based on the machine learning analysis, sepsis was the key predictor for cognitive and motor outcome. For language outcome, gestational age, duration of hospitalization, and Apgar score at 5th minute were predictive, while for socio-emotional, gestational age, sepsis, and duration of hospitalization were predictive. Pediatricians’ opinions were that cardiopulmonary resuscitation is the key predictor for cognitive, motor, and socio-emotional, but gestational age for language outcome. Conclusions: The application of machine learning in predicting neurodevelopmental outcomes of preterm infants represents a significant advancement in neonatal care. The integration of machine learning models with clinical workflows requires ongoing education and collaboration between data scientists and healthcare professionals to ensure the models’ practical applicability and interpretability. Full article
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