Network Medicine: A Network-based Approach to Human Disease

By Albert-László Barabási, Natali Gulbahce, Joseph Loscalzo. 2011

4,742 citations recorded by the source corpus.

Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

EgoNet: identification of human disease ego-network modules (2014, background): “This observation is consistent with the local property of disease networks – proteins involved in the same disease have an increased tendency to interact with each other [2].”
Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment (2019, neutral): “Their disruption or deregulation often leads to disease.(1,2) Charting these interactions and elucidat-”
A DIseAse MOdule Detection (DIAMOnD) Algorithm Derived from a Systematic Analysis of Connectivity Patterns of Disease Proteins in the Human Interactome (2015, background): “In principle, this discrepancy could be either a mere consequence of incomplete Interactome and gene-disease association data [5,10,26], or reflect an inherent fundamental difference between disease and topological modules.”
Artificial intelligence in cancer target identification and drug discovery (2022, background): “The principles and theory of network-based biology analysis networks are ef ﬁ cient in integrating complicated biological data, because they can capture the property of biological entities and their relationships 92 .”
Human symptoms–disease network (2014, methodology): “A number of resources have been constructed aiming to understand the entangled relationship between diseases, often in the form of networks 6 .”
Integrated interactions database: tissue-specific view of the human and model organism interactomes (2015, background): “Known PPI networks have proven valuable for many applications, including prediction of gene function (1,2), identification of disease genes (3,4) and drug discovery (5,6).”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “…hubs, with the important exception of somatic mutations, such as those occurring in cancer, where diseaseassociated multi-interface hubs form an inter-connected rich club (Jonsson & Bates, 2006; Goh et al., 2007; Feldman et al., 2008; Kar et al., 2009; Barabási et al., 2011; Zhang et al., 2011a).”
Inductive matrix completion for predicting gene–disease associations (2014, methodology): “These methods work by determining similarity between candidate gene and disease nodes in heterogeneous networks composed of different biological networks [see Barab asi et al. (2011) for a detailed review of network-based approaches].”
HINT: High-quality protein interactomes and their applications in understanding human disease (2012, background): “People have realized in the last decade that a human disease is rarely the consequence of an isolated abnormality in a particular gene but is generally the outcome of complex perturbations of the underlying cellular network [39].”
Evolutionary velocity with protein language models (2021, methodology): “We quantify 53 sequence similarity as the Euclidean distance in language model embedding space, which can 54 encode complex functional relationships [13]–[16], and we construct the network by connecting 55 a sequence to its k-nearest neighbors (KNN), which has been useful in modeling biological 56 l”
The challenge of modeling protein assemblies: the CASP12‐CAPRI experiment (2018, background): “Their dysregulation or disruption often leads to disease.(2,3) Characterizing these interactions and understanding the principles that governs them is therefore more than ever at the center”
Robustness and resilience of complex networks (2024, methodology): “cascading failure and robustness of a cell metabolic network 9,42 to activation or inhibition of specific enzymatic reactions can be used for developing specific therapies, drug repurposing and, more broadly, network and system’s medicine 43 .”
BioTriangle: a web-accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions (2016, background): “…approaches in advancing our knowledge and understanding of individual molecules and their functions, it has been increasingly recognized that a single biological process often involves complex interactions among a variety of molecules, especially DNA, RNA, proteins and small molecules [1, 2].”