Integrating Open Data on Cancer in Support to Tumor Growth Analysis

Jeanquartier, Fleur; Jean-Quartier, Claire; Schreck, Tobias; Cemernek, David; Holzinger, Andreas

doi:10.1007/978-3-319-43949-5_4

Cited by 16 publications

(5 citation statements)

References 48 publications

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“…Overall, in silico biological systems, especially integrated mathematical models, provide significant link and enrichment of in vitro and in vivo systems [Figure 1]. In addition, efforts to expand integrative approaches to combine information on multiple levels -molecular, cellular, microenvironmental etc… -will further refine the experimental programs (101)(102)(103). Cancer and biomedical science in general will benefit from the combination of in silico with in vitro and in vivo methods resulting in higher specificity and speed, providing more accurate, detailed and refined models that ultimately support prediction and decision-making.…”

Section: Discussionmentioning

confidence: 99%

Preclinical models in oncological pharmacology: limits and advantages

et al. 2021

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Introduction 2) In vitro models: 2.1 2D culture models 2.1.1 Cell lines 2.1.2 Primary cell culture 2.1.3 Others 2.2 3D cell culture models 3) In vivo models: 3.1 Immunocompetent mouse and rat models: syngeneic, genetically engineered mouse and carcinogen-induced models) 3.1.1 Ectopic and orthotopic syngeneic models 3.1.2 Genetically engineered mouse models 3.1.3 Carcinogen-induced models 3.2 Immunocompromised mouse models: cell-line derived and patient derived xenograft 3.3 zebrafish models 4) Integrated mathematical approches 5) Conclusioni/Opinions.

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Section: Discussionmentioning

confidence: 99%

Preclinical models in oncological pharmacology: limits and advantages

et al. 2021

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“…This project integrates 30 biomedical databases and datasets into RDF extended dataset with 10 billion triples. In order to enrich the cancer research field, the approach by [8] integrates 23 cancer-related datasets from five different categories. The research integrates different representations of datasets (e.g., JavaScript Object Notation (JSON), Tab Separated Value (TSV), and Comma Separated Value (CSV)) with the disease ontology (DO) [9].…”

Section: Related Workmentioning

confidence: 99%

Ontology-based teacher-context data integration

Nashed

Lahoud

Abel

2022

2022 IEEE/SICE International Symposium on System Integration (SII)

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“…Computational biology and bioinformatics are mostly used to store and process large-scale experimental data, extract and provide information as well as develop integrative tools to support analysis tasks and to produce biological insights. Existing well-maintained databases provide, integrate and annotate ”information on various cancers [ 61 ], and are increasingly being used to generate predictive models, which in turn will inform and guide biomedical experiments. Table 2 lists several representative examples of such databases.…”

Section: In Silico Analysismentioning

confidence: 99%

In silico cancer research towards 3R

Jean-Quartier¹,

et al. 2018

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BackgroundImproving our understanding of cancer and other complex diseases requires integrating diverse data sets and algorithms. Intertwining in vivo and in vitro data and in silico models are paramount to overcome intrinsic difficulties given by data complexity. Importantly, this approach also helps to uncover underlying molecular mechanisms. Over the years, research has introduced multiple biochemical and computational methods to study the disease, many of which require animal experiments. However, modeling systems and the comparison of cellular processes in both eukaryotes and prokaryotes help to understand specific aspects of uncontrolled cell growth, eventually leading to improved planning of future experiments. According to the principles for humane techniques milestones in alternative animal testing involve in vitro methods such as cell-based models and microfluidic chips, as well as clinical tests of microdosing and imaging. Up-to-date, the range of alternative methods has expanded towards computational approaches, based on the use of information from past in vitro and in vivo experiments. In fact, in silico techniques are often underrated but can be vital to understanding fundamental processes in cancer. They can rival accuracy of biological assays, and they can provide essential focus and direction to reduce experimental cost.Main bodyWe give an overview on in vivo, in vitro and in silico methods used in cancer research. Common models as cell-lines, xenografts, or genetically modified rodents reflect relevant pathological processes to a different degree, but can not replicate the full spectrum of human disease. There is an increasing importance of computational biology, advancing from the task of assisting biological analysis with network biology approaches as the basis for understanding a cell’s functional organization up to model building for predictive systems.ConclusionUnderlining and extending the in silico approach with respect to the 3Rs for replacement, reduction and refinement will lead cancer research towards efficient and effective precision medicine. Therefore, we suggest refined translational models and testing methods based on integrative analyses and the incorporation of computational biology within cancer research.

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Integrating Open Data on Cancer in Support to Tumor Growth Analysis

Cited by 16 publications

References 48 publications

Preclinical models in oncological pharmacology: limits and advantages

Preclinical models in oncological pharmacology: limits and advantages

Ontology-based teacher-context data integration

In silico cancer research towards 3R

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