An Overview of Data Integration in Neuroscience With Focus on Alzheimer's Disease

Abstract

This work represents the first attempt to provide an overview of how to face data integration as the result of a dialogue between neuroscientists and computer scientists. Indeed, data integration is fundamental for studying complex multifactorial diseases, such as the neurodegenerative diseases. This work aims at warning the readers of common pitfalls and critical issues in both medical and data science fields. In this context, we define a road map for data scientists when they first approach the issue of data integration in the biomedical domain, highlighting the challenges that inevitably emerge when dealing with heterogeneous, large-scale and noisy data and proposing possible solutions. Here, we discuss data collection and statistical analysis usually seen as parallel and independent processes, as cross-disciplinary activities . Finally, we provide an exemplary application of data integration to address Alzheimer's Disease (AD), which is the most common multifactorial form of dementia worldwide. We critically discuss the largest and most widely used datasets in AD, and demonstrate how the emergence of machine learning and deep learning methods has had a significant impact on disease's knowledge particularly in the perspective of an early AD diagnosis.

Machine Learning Methods for Gene Selection in Uveal Melanoma

“…Integration of multiple genomics and phenotype data is gradually unveiling the complex molecular biology behind genetic diseases and cancers [39][40][41]. Data fusion has been previously applied as a tool to cluster patients or how to extract relevant features for disease prognosis by integrating data of several NGS, imaging and other clinically related datasets from the same group of samples [42][43][44]. The main limitations to the application of these approaches are batch effects, the curse of dimensionality that arises with genomic data and missing information or heterogeneity (data incompleteness) [43].…”

“…Data fusion has been previously applied as a tool to cluster patients or how to extract relevant features for disease prognosis by integrating data of several NGS, imaging and other clinically related datasets from the same group of samples [42][43][44]. The main limitations to the application of these approaches are batch effects, the curse of dimensionality that arises with genomic data and missing information or heterogeneity (data incompleteness) [43]. Regarding the first point, in each sequencing experiment, technical differences among replicates could mask or mimic biological variation; for example, different sequencing coverage among two groups of samples sequenced with RNAseq could potentially lead to the discovery of several false positives, as differentially expressed genes, if samples are not properly normalized [45].…”

Predictive models in Alzheimer's disease: an evaluation based on data mining techniques

“…KDD outperformed SEMMA and CRISP-DM in terms of versatility and ability to address the specific challenges of our data mining project in a more efficient and comprehensive manner, as shown in Table 5. [38].…”

Guest Editorial Insights of Machine Learning into Medical Decision Making Systems: From Research to Practice

“…Turrisi et al [3] work is the first effort to give a comprehensive guide on addressing data integration via collaboration between neuroscientists and computer scientists. Data integration is essential for analyzing complex multifactorial disorders like neurodegenerative diseases.…”