Network-assisted target identification for haploinsufficiency and homozygous profiling screens

Wang, Sheng; Peng, Jian

doi:10.1371/journal.pcbi.1005553

Cited by 11 publications

(7 citation statements)

References 45 publications

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“…In addition to chemical and genomic data 10 , previous works have incorporated pharmacological or phenotypic information, such as side-effects 11 , 12 , transcriptional response data 13 , drug–disease associations 14 , public gene expression data 15 and functional data 16 for DTI prediction. Heterogeneous data sources provide diverse information and a multi-view perspective for predicting novel DTIs.…”

Section: Introductionmentioning

confidence: 99%

A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information

et al. 2017

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The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. In this work, we develop a computational pipeline, called DTINet, to predict novel drug–target interactions from a constructed heterogeneous network, which integrates diverse drug-related information. DTINet focuses on learning a low-dimensional vector representation of features, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then makes prediction based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for drug–target interaction prediction. Moreover, we experimentally validate the novel interactions between three drugs and the cyclooxygenase proteins predicted by DTINet, and demonstrate the new potential applications of these identified cyclooxygenase inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug–target interactions and repurpose existing drugs.

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Section: Introductionmentioning

confidence: 99%

A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information

et al. 2017

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“…Using this principle, prediction can be formulated as a binary classification task, which aims to predict whether a drug-target interaction is present or not. This straightforward classification approach considers known drug-target interactions as positive labels and uses chemical structure of drugs and DNA sequence of target proteins as input features (or kernels) [257,258,259]. Additionally, many methods integrate side information into the classification model, such as drug side effects [18,260], gene expression profiles [261], drug-disease associations [262], and genes' functional information [263].…”

Section: Drug-target Interaction Predictionmentioning

confidence: 99%

Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities

Žitnik

Nguyen

Wang³

et al. 2019

Information Fusion

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New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include a myriad of properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field.

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“…However, these technologies did not directly connect a drug with disease genes. (3) Constructing proteindisease networks is another approach to identify genedisease associations for selecting therapeutic targets in cancer [18]. Ferrero et al proposed a semi-supervised network approach, which evaluates disease association evidence and makes de novo predictions of potential therapeutic targets based on that [19].…”

Section: Introductionmentioning

confidence: 99%

SCNrank: spectral clustering for network-based ranking to reveal potential drug targets and its application in pancreatic ductal adenocarcinoma

et al. 2020

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Background: Pancreatic ductal adenocarcinoma (PDAC) is the most common pancreatic malignancy. Due to its wide heterogeneity, PDAC acts aggressively and responds poorly to most chemotherapies, causing an urgent need for the development of new therapeutic strategies. Cell lines have been used as the foundation for drug development and disease modeling. CRISPR-Cas9 plays a key role in every step-in drug discovery: from target identification and validation to preclinical cancer cell testing. Using cell-line models and CRISPR-Cas9 technology together make drug target prediction feasible. However, there is still a large gap between predicted results and actionable targets in real tumors. Biological network models provide great modus to mimic genetic interactions in real biological systems, which can benefit gene perturbation studies and potential target identification for treating PDAC. Nevertheless, building a network model that takes cell-line data and CRISPR-Cas9 data as input to accurately predict potential targets that will respond well on real tissue remains unsolved. Methods: We developed a novel algorithm 'Spectral Clustering for Network-based target Ranking' (SCNrank) that systematically integrates three types of data: expression profiles from tumor tissue, normal tissue and cell-line PDAC; protein-protein interaction network (PPI); and CRISPR-Cas9 data to prioritize potential drug targets for PDAC. The whole algorithm can be classified into three steps: 1. using STRING PPI network skeleton, SCNrank constructs tissuespecific networks with PDAC tumor and normal pancreas tissues from expression profiles; 2. With the same network skeleton, SCNrank constructs cell-line-specific networks using the cell-line PDAC expression profiles and CRISPR-Cas 9 data from pancreatic cancer cell-lines; 3. SCNrank applies a novel spectral clustering approach to reduce data dimension and generate gene clusters that carry common features from both networks. Finally, SCNrank applies a scoring scheme called 'Target Influence score' (TI), which estimates a given target's influence towards the cluster it belongs to, for scoring and ranking each drug target.

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Network-assisted target identification for haploinsufficiency and homozygous profiling screens

Cited by 11 publications

References 45 publications

A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information

A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information

Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities

SCNrank: spectral clustering for network-based ranking to reveal potential drug targets and its application in pancreatic ductal adenocarcinoma

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