Integration of solutions and services for multi-omics data analysis towards personalized medicine

Reska, Daniel; Czajkowski, Marcin; Jurczuk, Krzysztof; Boldak, Cezary; Kwedlo, Wojciech; Bauer, Witold; Koszelew, Jolanta; Krȩtowski, Marek

doi:10.1016/j.bbe.2021.10.005

Cited by 16 publications

(9 citation statements)

References 58 publications

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“…With the exception of the last method, all of the aforementioned algorithms are state-of-the-art MLMs. The IntelliOmics platform 71 , was used to prepare and transform the datasets used in the performed experiments. Next, the algorithms were optimized and tested using RapidMiner software, which is one of the most popular and well-established tool in data mining.…”

Section: Methodsmentioning

confidence: 99%

A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors

Godlewski

Czajkowski

Mojsak

et al. 2023

Sci Rep

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Metabolomics combined with machine learning methods (MLMs), is a powerful tool for searching novel diagnostic panels. This study was intended to use targeted plasma metabolomics and advanced MLMs to develop strategies for diagnosing brain tumors. Measurement of 188 metabolites was performed on plasma samples collected from 95 patients with gliomas (grade I–IV), 70 with meningioma, and 71 healthy individuals as a control group. Four predictive models to diagnose glioma were prepared using 10 MLMs and a conventional approach. Based on the cross-validation results of the created models, the F1-scores were calculated, then obtained values were compared. Subsequently, the best algorithm was applied to perform five comparisons involving gliomas, meningiomas, and controls. The best results were obtained using the newly developed hybrid evolutionary heterogeneous decision tree (EvoHDTree) algorithm, which was validated using Leave-One-Out Cross-Validation, resulting in an F1-score for all comparisons in the range of 0.476–0.948 and the area under the ROC curves ranging from 0.660 to 0.873. Brain tumor diagnostic panels were constructed with unique metabolites, which reduces the likelihood of misdiagnosis. This study proposes a novel interdisciplinary method for brain tumor diagnosis based on metabolomics and EvoHDTree, exhibiting significant predictive coefficients.

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Section: Methodsmentioning

confidence: 99%

A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors

Godlewski

Czajkowski

Mojsak

et al. 2023

Sci Rep

Self Cite

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“…Notably, integrative multiomics analyses have provided more comprehensive molecular signatures to identify cancer subtypes 139 , 140 . Recent advances in omics analysis and data archiving and processing have provided reliable data 141 . There have been global efforts to obtain new molecular information by which to treat and diagnose cancers by reproducibly obtaining and integrating omics information 142 .…”

Section: Emerging Technologiesmentioning

confidence: 99%

“…With conventional research methods that proceed from basic to clinical, we can fully understand living organisms; however, these methods have limitations in overcoming diseases 191 . Furthermore, academic fields have been self-focused and fragmented due to relatively slow technological advances in scientific fields, including intercommunication methods 141 . However, academic research fields are maturing and achieving success in applications based on robust fundamental backgrounds, such as electronic engineering.…”

Section: New Directions In Translational Researchmentioning

confidence: 99%

Paradigm shift required for translational research on the brain

Yoon,

Lee,

Lee

et al. 2024

Exp Mol Med

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Biomedical research on the brain has led to many discoveries and developments, such as understanding human consciousness and the mind and overcoming brain diseases. However, historical biomedical research on the brain has unique characteristics that differ from those of conventional biomedical research. For example, there are different scientific interpretations due to the high complexity of the brain and insufficient intercommunication between researchers of different disciplines owing to the limited conceptual and technical overlap of distinct backgrounds. Therefore, the development of biomedical research on the brain has been slower than that in other areas. Brain biomedical research has recently undergone a paradigm shift, and conducting patient-centered, large-scale brain biomedical research has become possible using emerging high-throughput analysis tools. Neuroimaging, multiomics, and artificial intelligence technology are the main drivers of this new approach, foreshadowing dramatic advances in translational research. In addition, emerging interdisciplinary cooperative studies provide insights into how unresolved questions in biomedicine can be addressed. This review presents the in-depth aspects of conventional biomedical research and discusses the future of biomedical research on the brain.

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“…To compile large amounts of data in a user-friendly environment, it is necessary to digitalize the paper reports in a database. During the MOBIT Project, the SmartBioBase-a platform for collecting, integrating, and analyzing clinical and omics data-was created [14]. This environment is a digital storage space with data processing and data mining applications.…”

Section: Mub Biobank's Organization Was Possible By Founding the Mobi...mentioning

confidence: 99%

Applied Molecular-Based Quality Control of Biobanked Samples for Multi-Omics Approach

Michalska-Falkowska,

Niklinski,

Juhl

et al. 2023

Cancers

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Biobanks are vital for high-throughput translational research, but the rapid development of novel molecular techniques, especially in omics assays, poses challenges to traditional practices and recommendations. In our study, we used biospecimens from oncological patients in Polish clinics and collaborated with the Indivumed Group. For serum/plasma samples, we monitored hemolysis, controlled RNA extraction, assessed cDNA library quality and quantity, and verified NGS raw data. Tissue samples underwent pathologic evaluation to confirm histology and determine tumor content. Molecular quality control measures included evaluating the RNA integrity number, assessing cDNA library quality and quantity, and analyzing NGS raw data. Our study yielded the creation of distinct workflows for conducting preanalytical quality control of serum/plasma and fresh-frozen tissue samples. These workflows offer customization options to suit the capabilities of different biobanking entities. In order to ensure the appropriateness of biospecimens for advanced research applications, we introduced molecular-based quality control methods that align with the demands of high-throughput assays. The novelty of proposed workflows, rooted in innovative molecular techniques, lies in the integration of these QC methods into a comprehensive schema specifically designed for high-throughput research applications.

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Integration of solutions and services for multi-omics data analysis towards personalized medicine

Cited by 16 publications

References 58 publications

A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors

A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors

Paradigm shift required for translational research on the brain

Applied Molecular-Based Quality Control of Biobanked Samples for Multi-Omics Approach

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