Computational Oncology in the Multi-Omics Era: State of the Art

Abstract

Cancer is the quintessential complex disease. As technologies evolve faster each day, we are able to quantify the different layers of biological elements that contribute to the emergence and development of malignancies. In this multi-omics context, the use of integrative approaches is mandatory in order to gain further insights on oncological phenomena, and to move forward toward the precision medicine paradigm. In this review, we will focus on computational oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. We will discuss the current roles of computation in oncology in the context of multi-omic technologies, which include: data acquisition and processing; data management in the clinical and research settings; classification, diagnosis, and prognosis; and the development of models in the research setting, including their use for therapeutic target identification. We will discuss the machine learning and network approaches as two of the most promising emerging paradigms, in computational oncology. These approaches provide a foundation on how to integrate different layers of biological description into coherent frameworks that allow advances both in the basic and clinical settings.

Precision Nutrition in Chronic Inflammation

“…This might be, because many tools still apply a sequential approach by analyzing one data layer after another and integrating the results post-analysis. As de Anda-Jaúregui and Hernańdez-Lemus (71) pointed out, biological processes and phenomena are not comprised of single, independent layers of biological features, and therefore algorithms that can simultaneously analyze multiple data types are preferred.…”

On a Class of Tensor Markov Fields

“…After having considered some of the properties of this class of Tensor Markov Fields, it may become evident that aside from purely theoretical importance, there is a number of important applications that may arise as probabilistic graphical models in tensor valued problems, among the ones that are somewhat evident are the following: The analysis of multidimensional biomolecular networks such as the ones arising from multi-omic experiments (For a real-life example, see Figure 4 ) [ 8 , 9 , 10 ]; Probabilistic graphical models in computer vision (especially 3D reconstructions and 4D [3D+time] rendering) [ 11 ]; The study of fracture mechanics in continuous deformable media [ 12 ]; Probabilistic network models for seismic dynamics [ 13 ]; Boolean networks in control theory [ 14 ]. …”

“…The analysis of multidimensional biomolecular networks such as the ones arising from multi-omic experiments (For a real-life example, see Figure 4 ) [ 8 , 9 , 10 ];…”

STATegra: Multi-omics data integration - A conceptual scheme and a bioinformatics pipeline

“…For instance, while specific DNA variations have been linked with multiple diseases, the associated mechanisms are not fully understood (5,6). As a result, multi-omics data-sets are increasingly applied across biological domains such as cancer biology (7)(8)(9)(10)(11). Furthermore, single-cell multiomics analysis (12)(13)(14)(15) has just become a reality.…”