Network analysis of FDA approved drugs and their targets

Ma’ayan, Avi; Jenkins, Sherry L.; Goldfarb, Joseph; Iyengar, Ravi

doi:10.1002/msj.20002

Cited by 107 publications

(105 citation statements)

References 11 publications

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“…These graphs have two sets of vertices where edges can connect only nodes in different sets, not nodes within a set. Bipartite networks are used, for example, to represent metabolic networks separating enzymes from their substrates and products, disease gene networks connecting diseases with disease genes (10), and drug networks connecting drugs with their known biomolecular targets (11,12) or to integrate different "omics" data sets (13). Another type of graph, the planar graph, can be drawn on a plane with no edge crossings.…”

Section: Types Of Graphsmentioning

confidence: 99%

Insights into the Organization of Biochemical Regulatory Networks Using Graph Theory Analyses

Ma’ayan

2009

Journal of Biological Chemistry

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Graph theory has been a valuable mathematical modeling tool to gain insights into the topological organization of biochemical networks. There are two types of insights that may be obtained by graph theory analyses. The first provides an overview of the global organization of biochemical networks; the second uses prior knowledge to place results from multivariate experiments, such as microarray data sets, in the context of known pathways and networks to infer regulation. Using graph analyses, biochemical networks are found to be scale-free and small-world, indicating that these networks contain hubs, which are proteins that interact with many other molecules. These hubs may interact with many different types of proteins at the same time and location or at different times and locations, resulting in diverse biological responses. Groups of components in networks are organized in recurring patterns termed network motifs such as feedback and feed-forward loops. Graph analysis revealed that negative feedback loops are less common and are present mostly in proximity to the membrane, whereas positive feedback loops are highly nested in an architecture that promotes dynamical stability. Cell signaling networks have multiple pathways from some input receptors and few from others. Such topology is reminiscent of a classification system. Signaling networks display a bow-tie structure indicative of funneling information from extracellular signals and then dispatching information from a few specific central intracellular signaling nexuses. These insights show that graph theory is a valuable tool for gaining an understanding of global regulatory features of biochemical networks.

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Section: Types Of Graphsmentioning

confidence: 99%

Insights into the Organization of Biochemical Regulatory Networks Using Graph Theory Analyses

Ma’ayan

2009

Journal of Biological Chemistry

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“…Ma'ayan et al introduced graphtheory methods to analyze the FDA-approved drugs and their known molecular targets (17). Zhu et al in (18) explored multiple factors that collectively contribute to druggability of various targets, including its protein sequence, structural, physicochemical, and systems profiles.…”

Section: Introductionmentioning

confidence: 99%

Predicting High-Impact Pharmacological Targets by Integrating Transcriptome and Text-Mining Features

Mayburd

Baranova

2016

J Pharm Pharm Sci

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-Purpose: Novel, "outside of the box" approaches are needed for evaluating candidate molecules, especially in oncology. Throughout the years of 2000-2010, the efficiency of drug development fell to barely acceptable levels, and in the second decade of this century, levels have improved only marginally. This dismal condition continues despite unprecedented progress in the development of a variety of high-throughput tools, computational methods, aggregated databases, drug repurposing programs and innovative chemistries. Here we tested a hypothesis that the economic impact of targeting a particular gene product is predictable a priori by employing a combination of transcriptome profiles and quantitative metrics reflecting existing literature. Methods: To extract classification features, the gene expression patterns of a posteriori high-impact and lowimpact anti-cancer target sets were compared. To minimize the possible bias of text-mining, the number of manuscripts published prior to the first clinical trial or relevant review paper, as well as its first derivative in this interval, were collected and used as quantitative metrics of public interest. Results: By combining the gene expression and literature mining features, a 4-fold enrichment in high-impact targets was produced, resulting in a favourable ROC curve analysis for the top impact targets. The dataset was enriched by the highest impact anticancer targets, while demonstrating drastic differences in economic value between high and low-impact targets.

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“…In this field, the research focus is rapidly shifting from studying an individual entity (e.g., one disease, drug, or gene) to entire networks of many different biological entities. Computational analysis of the knowledge represented in biomedical networks can uncover important new relationships, generate new testable hypotheses and provide new insight into biological systems [5, 6]. Recent investigations use systems biology methods to examine drug responses, by utilizing a network-based view of the genes involved in complex drug responses [7, 8].…”

Section: Introductionmentioning

confidence: 99%

A knowledge-driven conditional approach to extract pharmacogenomics specific drug–gene relationships from free text

Wang²

2012

Journal of Biomedical Informatics

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An important task in pharmacogenomics (PGx) studies is to identify genetic variants that may impact drug response. The success of many systematic and integrative computational approaches for PGx studies depends on the availability of accurate, comprehensive and machine understandable drug-gene relationship knowledge bases. Scientific literature is one of the most comprehensive knowledge sources for PGx-specific drug-gene relationships. However, the major barrier in accessing this information is that the knowledge is buried in a large amount of free text with limited machine understandability. Therefore there is a need to develop automatic approaches to extract structured PGx-specific drug-gene relationships from unstructured free text literature. In this study, we have developed a conditional relationship extraction approach to extract PGx-specific drug-gene pairs from 20 million MEDLINE abstracts using known drug- gene pairs as prior knowledge. We have demonstrated that the conditional drug- gene relationship extraction approach significantly improves the precision and F1 measure compared to the unconditioned approach (precision: 0.345 vs. 0.11; recall: 0.481 vs. 1.00; F1: 0.402 vs. 0.201). In this study, a method based on co-occurrence is used as the underlying relationship extraction method for its simplicity. It can be replaced by or combined with more advanced methods such as machine learning or natural language processing approaches to further improve the performance of the drug- gene relationship extraction from free text. Our method is not limited to extracting a drug-gene relationship; it can be generalized to extract other types of relationships when related background knowledge bases exist.

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Network analysis of FDA approved drugs and their targets

Cited by 107 publications

References 11 publications

Insights into the Organization of Biochemical Regulatory Networks Using Graph Theory Analyses

Insights into the Organization of Biochemical Regulatory Networks Using Graph Theory Analyses

Predicting High-Impact Pharmacological Targets by Integrating Transcriptome and Text-Mining Features

A knowledge-driven conditional approach to extract pharmacogenomics specific drug–gene relationships from free text

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