Methods for the integration of multi-omics data: mathematical aspects

Bersanelli, Matteo; Mosca, Ettore; Remondini, Daniel; Giampieri, Enrico; Sala, Claudia; Castellani, Gastone; Milanesi, Luciano

doi:10.1186/s12859-015-0857-9

Cited by 315 publications

(231 citation statements)

References 52 publications

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“…Despite the efforts [14,15], integrating different sources of genetic data is still challenging. The ultimate goal is to gain novel insights and new knowledge about life and disease from system-level molecular interactions [16].…”

Section: Multi-omicsmentioning

confidence: 99%

Multilayer network modeling of integrated biological systems

Domenico

2018

Physics of Life Reviews

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Biological systems, from a cell to the human brain, are inherently complex. A powerful representation of such systems, described by an intricate web of relationships across multiple scales, is provided by complex networks. Recently, several studies are highlighting how simple networks -obtained by aggregating or neglecting temporal or categorical description of biological data -are not able to account for the richness of information characterizing biological systems. More complex models, namely multilayer networks, are needed to account for interdependencies, often varying across time, of biological interacting units within a cell, a tissue or parts of an organism.Gosak et al [1] review the most recent advances in the application of multilayer networks for modeling complex biological systems, from molecular interactions within a cell to neuronal connectivity of the human brain.

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Section: Multi-omicsmentioning

confidence: 99%

Multilayer network modeling of integrated biological systems

Domenico

2018

Physics of Life Reviews

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“…The common types of omics include genomics, proteomics, metabolomics, epigenomics, phytochemomics, interactomics, and microbiomics [10][11][12]. Integration of multiple omics data in a systematic way enables to study the functional relationship or identify the key problem in an efficient manner.…”

Section: Multi-omics and Big Data Integrationmentioning

confidence: 99%

Prediction of Potential Lead Molecules through Systematic Integration of Multi-omics Datasets - A Mini-Review

2017

IJCRR

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Prediction of a novel or potential lead molecules for a therapeutic drug target without adverse effects is a challenging task in the drug designing, discovery, and development process. The systematic integration of multi-omics data from various data/knowledge bases through computational techniques enables to identify potential lead molecules and study the therapeutic properties. Over the last decades, several drug discoveries using multi-omics and huge dataset integration methods proven with successive results. In this paper, we present different types of computational approaches for prediction of potential lead molecules through the systems-level integration of multi-omics datasets.

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“…Biological systems are complex in that there are many biological variables that encompass biological processes, thus, making integrative analyses through omics approaches a major challenge due to several technological limits associated with analysing biological processes that entail a large number of variables [90]. Thus, there is still a great need for researchers to form consortiums aimed at integrating research eforts that contribute towards integrating the data obtained from the application of omics technologies to have an integrated biological system that will allow easier manipulation of data.…”

Section: The Use Of -Omics Technologies To Improve Nutritional Bioavamentioning

confidence: 99%

Progress and Challenges in Improving Nutritional Quality in Wheat

Lephuthing¹,

Baloyi²,

Sosibo³

et al. 2017

Wheat Improvement, Management and Utilization

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Wheat (Triticum aestivum L.) houses a wide range of nutritional components such as iron (Fe), zinc (Zn), vitamins and phenolic acids, which are important for plant metabolism and human health. The bioavailability of these nutritional components is low due to their interaction with other components and low quantity in the endosperm. Biofortiication is a more sustainable approach that could improve the bioavailability of essential nutritional components. Substantial progress has been made to improve nutritional quality through the application of conventional, technological and transgenic approaches. This has led to the discovery, cloning and introgression of the Gpc-B1 gene; the invention of online databases with minimally characterized biosynthetic, metabolic pathways and biological processes of wheat-related species; the establishment of genetic variation in grain Fe and Zn content and the biofortiication of wheat with Zn by the HarvestPlus organization. Nonetheless, the biofortiication of wheat with micronutrients and phenolic acids is still a challenge due to incomplete understanding of the wheat genome, biosynthesis and translocation of selected nutritional components into diferent wheat grain compartments. There is a need to integrate selected omics technologies to obtain a holistic overview and manipulate key biological processes involved in the remobilization and biosynthesis of nutritional components into desired wheat grain compartments.

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Methods for the integration of multi-omics data: mathematical aspects

Cited by 315 publications

References 52 publications

Multilayer network modeling of integrated biological systems

Multilayer network modeling of integrated biological systems

Prediction of Potential Lead Molecules through Systematic Integration of Multi-omics Datasets - A Mini-Review

Progress and Challenges in Improving Nutritional Quality in Wheat

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