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Introduction to Tissue Phenomics
Published in Gerd Binnig, Ralf Huss, Günter Schmidt, Tissue Phenomics, 2018
Ralf Huss, Gerd Binnig, Günter Schmidt, Martin Baatz, Johannes Zimmermann
The concept of the phene, coined a century ago, gained novel significance after being overshadowed by genetic approaches for years. An independent discipline named phenomics was postulated to help elucidate the physiological hierarchies leading from genetic traits to clinical endpoints (Schork, 1997). A decade later, it was understood that the systematic study of phenotypes in relevant biological contexts would be “critically important to provide traction for biomedical advances in the post-genomic era” (Bilder et al., 2009). The toolbox of comprehensive phenotyping is ample and addresses complexity on different scales. From transcriptomics and epigenomics over proteomics to high-throughput assays of cell cultures, biological entities of increasing hierarchical complexity are assessed systematically (Houle et al., 2010). However, these approaches are compromised by methodological challenges. Performing phenomics on a large scale is an intrinsically multidimensional problem, because the phenome is highly dynamic. It is influenced by a multitude of factors from post-translational modifications to high-level environmental stimuli. Approaches such as proteomics, capturing snapshots of multifactorial biological processes, are limited in their transferability and significance. In general, the gigantic molecular network that acts between the genome and the unfolding of tissue structures with all their properties is still not fully understood. Tissue phenomics provides a systematic way of knowledge discovery, thereby contributing to fill this knowledge gap.
Knowledge graphs and their applications in drug discovery
Published in Expert Opinion on Drug Discovery, 2021
In the field of drug discovery, one of the earliest notable attempts to integrate multiple structured biomedical databases was the work of Himmelstein et al., developing Hetionet to prioritize drugs for repurposing [28**] and genes associated with disease [29]. Other KGs include OpenBioLink, principally used to benchmark link prediction models [30], and the work of Womack et al. [31]. Whilst the integration of structured databases has proven its utility, others have derived biological relationships from literature. The Global Network of Biomedical Relationships [32*] screened 24 million research articles to create a disease-gene-chemical KG consisting of 2 million thematically-labeled edges. Biomedical KGs can contain a multitude of multimodal data spanning transcriptomics, proteomics, genomics, phenomics, drug pharmacology, chemistry, and ontological information. The schema of the Drug Repurposing Knowledge Graph [33] exemplifies the heterogeneity of data common in KGs for drug discovery. The majority of large-scale biomedical KGs are based on semantic web technologies, the largest of which is Bio2RDF [34]. One of the defining features of semantic KGs is their extensibility, as demonstrated by projects, such as Chem2Bio2RDF, which combined Bio2RDF with a chemogenomic semantic graph [35].
Modern approaches for the phenotyping of cytochrome P450 enzymes in children
Published in Expert Review of Clinical Pharmacology, 2020
Finally, it is also important to note that drug metabolism is only one element of pharmacokinetic variability and that the measurement of drug metabolism phenotypes is not sufficient on its own to predict individual variations in drug concentrations and exposure. The field of phenomics, that is ‘the acquisition of high-dimensional phenotypic data on an organism-wide scale’ [18], would allow for advanced real-time and real-life phenotyping of drug outcomes [19]. Technologies for high-throughput phenotyping are becoming increasingly available and is a promising area for personalized medicine.