Network-Based Methods for Approaching Human Pathologies from a Phenotypic Point of View

Abstract

Network and systemic approaches to studying human pathologies are helping us to gain insight into the molecular mechanisms of and potential therapeutic interventions for human diseases, especially for complex diseases where large numbers of genes are involved. The complex human pathological landscape is traditionally partitioned into discrete “diseases”; however, that partition is sometimes problematic, as diseases are highly heterogeneous and can differ greatly from one patient to another. Moreover, for many pathological states, the set of symptoms (phenotypes) manifested by the patient is not enough to diagnose a particular disease. On the contrary, phenotypes, by definition, are directly observable and can be closer to the molecular basis of the pathology. These clinical phenotypes are also important for personalised medicine, as they can help stratify patients and design personalised interventions. For these reasons, network and systemic approaches to pathologies are gradually incorporating phenotypic information. This review covers the current landscape of phenotype-centred network approaches to study different aspects of human diseases.

A framework for considering prior information in network‐based approaches to omics data analysis

“…Phenotype prediction aims to predict how an organism or system responds to disease states and perturbations. These methods may be applied at a cellular level to project signaling events and transformations as well as broad phenomena like cell proliferation and survival, but they can also be extended to a network medicine approach, where predictions are made at a patient level to inform diagnosis, prognosis, or treatment response [42,43] An excellent example of such an analysis is the use of co-phosphorylation networks to predict breast cancer subtypes with the CoPPNet [44] algorithm.…”

Differential expression and co-expression reveal cell types relevant to rare disease phenotypes

“…Due to the low prevalence of rare diseases, phenotypes play a crucial role in ensuring accurate diagnosis and patient stratification. From a systemic standpoint, leveraging these phenotypes constitutes a strategy for prioritizing patient variants (Kelly et al, 2022) and for the development of other phenotype-aware network-based approaches (Ranea et al, 2022). Indeed phenotypes are reflected at the molecular network level to the same extent as diseases (Chagoyen & Pazos, 2016).…”

Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease

“…Biological systems can be abstracted as a series of networks of interconnected molecular entities (genes, proteins, lncRNAs, miRNAs, etc.) [23], whereas a network is a representation of relationships (edges) between entities (nodes) [24]. In general, network-based approaches obtain disease-related information from complex topological patterns [24].…”

“…[23], whereas a network is a representation of relationships (edges) between entities (nodes) [24]. In general, network-based approaches obtain disease-related information from complex topological patterns [24]. Networks commonly used in systems biology include gene regulatory networks, protein-protein interaction (PPI) networks, literature-curated networks and hybrid networks [25].…”