Integrating imaging and omics data: A review

Antonelli, Laura; Guarracino, Mario Rosario; Maddalena, Lucia; Sangiovanni, Mara

doi:10.1016/j.bspc.2019.04.032

Cited by 53 publications

(30 citation statements)

References 126 publications

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“…Nonetheless the development of targeted therapies suffer from certain limitations. Finally, it should be noted that the integration of data collected from different omics sources [ 110 ] and medical images may contribute to understanding the evolution of the disease.…”

Section: Sars-cov-2 Omics Data Analysismentioning

confidence: 99%

Data science in unveiling COVID-19 pathogenesis and diagnosis: evolutionary origin to drug repurposing

Das

Tradigo

Veltri

et al. 2021

Briefings in Bioinformatics

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Motivation The outbreak of novel severe acute respiratory syndrome coronavirus (SARS-CoV-2, also known as COVID-19) in Wuhan has attracted worldwide attention. SARS-CoV-2 causes severe inflammation, which can be fatal. Consequently, there has been a massive and rapid growth in research aimed at throwing light on the mechanisms of infection and the progression of the disease. With regard to this data science is playing a pivotal role in in silico analysis to gain insights into SARS-CoV-2 and the outbreak of COVID-19 in order to forecast, diagnose and come up with a drug to tackle the virus. The availability of large multiomics, radiological, bio-molecular and medical datasets requires the development of novel exploratory and predictive models, or the customisation of existing ones in order to fit the current problem. The high number of approaches generates the need for surveys to guide data scientists and medical practitioners in selecting the right tools to manage their clinical data. Results Focusing on data science methodologies, we conduct a detailed study on the state-of-the-art of works tackling the current pandemic scenario. We consider various current COVID-19 data analytic domains such as phylogenetic analysis, SARS-CoV-2 genome identification, protein structure prediction, host–viral protein interactomics, clinical imaging, epidemiological research and drug discovery. We highlight data types and instances, their generation pipelines and the data science models currently in use. The current study should give a detailed sketch of the road map towards handling COVID-19 like situations by leveraging data science experts in choosing the right tools. We also summarise our review focusing on prime challenges and possible future research directions. Contact hguzzi@unicz.it, sroy01@cus.ac.in

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Section: Sars-cov-2 Omics Data Analysismentioning

confidence: 99%

Data science in unveiling COVID-19 pathogenesis and diagnosis: evolutionary origin to drug repurposing

Das

Tradigo

Veltri

et al. 2021

Briefings in Bioinformatics

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“…Through ad-hoc data reduction techniques and highly interactive systems incorporating multidimensional input devices, this can be achieved through standard means such as traditional 2D and 3D displays. The increasing numbers of modalities and complexity of information, as derived from both imaging and nonimaging biomarkers and data [5], to be merged together for a meaningful and actionable representation may still eventually require other approaches, likely derived from other areas, such as games or virtual reality, for more advanced visualization and interaction paradigms also relying on various senses and actuators (e.g., haptics, sound, eye tracking, brain-computer interfaces, etc.). A special case is for planning and guidance during interventional or surgical procedures, where augmented and virtual reality approaches will need to be implemented as un-obstructively as possible to assist with both navigation and delivery.…”

Section: Visualizationmentioning

confidence: 99%

“…At a different scale, biological microscopy images and molecular pathways provide further insight into tissues and living organisms, and into processes and structures of cellular compartments [4]. Given the different types of data generated and scale of information, new ways of integrating and using biomedical information must be found [5]. In this paper, we describe important types of biomedical imaging methods and hypothesize on their short-to mid-term developments with a focus on oncological imaging.…”

mentioning

confidence: 99%

What scans we will read: imaging instrumentation trends in clinical oncology

et al. 2020

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Oncological diseases account for a significant portion of the burden on public healthcare systems with associated costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific morphology and functional-molecular pathways, cancerous tissue can be detected and characterized noninvasively, so as to provide referring oncologists with essential information to support therapy management decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards integrating molecular image information through various methods, including anato-metabolic imaging (e.g., PET/ CT), advanced MRI, optical or ultrasound imaging. This perspective paper highlights a number of key technological and methodological advances in imaging instrumentation related to anatomical, functional, molecular medicine and hybrid imaging, that is understood as the hardware-based combination of complementary anatomical and molecular imaging. These include novel detector technologies for ionizing radiation used in CT and nuclear medicine imaging, and novel system developments in MRI and optical as well as opto-acoustic imaging. We will also highlight new data processing methods for improved non-invasive tissue characterization. Following a general introduction to the role of imaging in oncology patient management we introduce imaging methods with well-defined clinical applications and potential for clinical translation. For each modality, we report first on the status quo and, then point to perceived technological and methodological advances in a subsequent status go section. Considering the breadth and dynamics of these developments, this perspective ends with a critical reflection on where the authors, with the majority of them being imaging experts with a background in physics and engineering, believe imaging methods will be in a few years from now. Overall, methodological and technological medical imaging advances are geared towards increased image contrast, the derivation of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall examination time. To ensure full translation to the clinic, this progress in technologies and instrumentation is complemented by advances in relevant acquisition and image-processing protocols and improved data analysis. To this end, we should accept diagnostic images as "data", andthrough the wider adoption of advanced analysis, including machine learning approaches and a "big data" conceptmove to the next stage of non-invasive tumour phenotyping. The scans we will be reading in 10 years from now will likely be composed of highly diverse multidimensional data from multiple sources, which mandate the use of advanced and interactive visualization and

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“…Recent years have produced large advances in the analysis of the general aspects of proteins, including ''omics'' studies for protein abundance (Antonelli et al, 2019;Noor et al, 2019), analysis techniques for the abundance of mRNA (Washburn et al, 2019), analyses of protein translation rates (Riba et al, 2019;Sharma et al, 2019) and much more. Similarly, substantial progress has also been made in microscopy techniques for imaging the location of proteins and the general morphology of cells.…”

Section: Introductionmentioning

confidence: 99%

Novel Secondary Ion Mass Spectrometry Methods for the Examination of Metabolic Effects at the Cellular and Subcellular Levels

Bonnin

Rizzoli

2020

Front. Behav. Neurosci.

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The behavior of an animal has substantial effects on its metabolism. Such effects, including changes in the lipid composition of different organs, or changes in the turnover of the proteins, have typically been observed using liquid mass spectrometry methods, averaging the effect of animal behavior across tissue samples containing multiple cells. These methods have provided the scientific community with valuable information, but have limited resolution, making it difficult if not impossible to examine metabolic effects at the cellular and subcellular levels. Recent advances in the field of secondary ion mass spectrometry (SIMS) have made it possible to examine the metabolic effects of animal behavior with high resolution at the nanoscale, enabling the analysis of the metabolic effects of behavior on individual cells. In this review we summarize and present these emerging methods, beginning with an overview of the SIMS technique. We then discuss the specific application of nanoscale SIMS (NanoSIMS) to examine cell behavior. This often requires the use of isotope labeling to highlight specific sections of the cell for analysis, an approach that is presented at length in this review article. We also present SIMS applications concerning animal and cell behavior, from development and aging to changes in the cellular activity programs. We conclude that the emerging group of SIMS technologies represents an exciting set of tools for the study of animal behavior and of its effects on internal metabolism at the smallest possible scales.

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Integrating imaging and omics data: A review

Cited by 53 publications

References 126 publications

Data science in unveiling COVID-19 pathogenesis and diagnosis: evolutionary origin to drug repurposing

Data science in unveiling COVID-19 pathogenesis and diagnosis: evolutionary origin to drug repurposing

What scans we will read: imaging instrumentation trends in clinical oncology

Novel Secondary Ion Mass Spectrometry Methods for the Examination of Metabolic Effects at the Cellular and Subcellular Levels

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