Addressing important clinical questions requires systematic knowledge management and analysis of the large volume of information. Understanding and successfully treating multi-genic diseases requires systems-oriented research approach focused on the implication of disease-perturbed molecular interaction networks and pathways. These networks represent crucial relationships among genes and proteins, their mutations, chromosomal aberrations, microRNA deregulation, and other epigenetic and metabolic changes.
Artificial intelligence research focuses on the development of diverse algorithms, their application in multiple areas, and system usability questions. Decision support systems in medicine need to be robust across assays, instruments, and cohorts, handling uncertainty and missing data gracefully. However, quality of data and literature-based evidence may be questionable, leading to low reproducibility and errors. Creating explainable models is essential to ensure trust in recommendations and decision support. Patient-centric, data-driven medicine requires high quality, comprehensive data sets, multiple levels of independent validation, and explainable models. Integration of multiple and diverse data sets and algorithms can improve confidence in findings, and reduce both type I and II errors. It is also essential to determine who and how will use the system and optimize it accordingly, as even useful applications may result in negative outcomes when used improperly or in an incorrect context.
When solving complex problems, one solution is usually not enough. Neither one method, nor one data set can cover the complexities. Integrating methods and data sets provides a better solution. Artificial intelligence, multimodal imaging, integrative computational biology, and wearable devices are transforming translational research. We are moving from diagnosing and treating disease to preventing it. Data mining and machine learning algorithms identify trends and interesting patterns in the cohorts, ensemble methods then apply it with confidence for each individual, and calibrated trends provide predicted preventive measures for each patient.
- Precision medicine
- Personalized medicine
- Integrative computational biology
- Artificial intelligence
- Data mining
- Machine learning
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Area under the curve
National Commission on Informatics and Liberty
Central processing unit
Diffuse large B cell lymphoma
Electrical impedance myography
Electronic patient record
Free and cued selective reminding test
Functional magnetic resonance imaging
Grey level co-occurrence matrix
Grey-level size zone matrix
Graphical processing unit
High grey-level zone emphasis
International classification of diseases
Integrated interactions database
Age-adjusted international prognostic index
- LASSI-L :
Loewenstein-Acevedo scale for semantic interference and learning
Low grey-level zone emphasis
Long-zone high grey-level emphasis
Long-zone low grey-level emphasis
- mirDIP :
microRNA data integration portal
Magnetic resonance imaging
Metabolic tumor volume
Ordered subset expectation maximisation
Pathway data integration portal
Positron emission tomography
Point spread function
Receiver operating characteristic
Single-cell RNA sequencing
- SNOMED CT:
Standard nomenclature of medicine clinical terms
Standardised uptake value
Short-zone high grey-level emphasis
Short-zone low grey-level emphasis
The cancer genome atlas
Tumor infiltrated lymphocytes
Volume of interest
Zone length non-uniformity
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Jurisica, I. (2022). Integrative Computational Biology, AI, and Radiomics: Building Explainable Models by Integration of Imaging, Omics, and Clinical Data. In: Veit-Haibach, P., Herrmann, K. (eds) Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging. Springer, Cham. https://doi.org/10.1007/978-3-031-00119-2_13
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