A comprehensive evaluation of connectivity methods for L1000 data

Lin, Kequan; Li, Lü; Dai, Yifei; Wang, Huili; Teng, Shuaishuai; Bao, Xilinqiqige; Lü, Zhi; Wang, Dong

doi:10.1093/bib/bbz129

Cited by 28 publications

(33 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The connectivity approach is essential for drug screen using gene expression data and a previous paper compared several connectivity approaches [ 39 ]. In order to evaluate the power of our method, we compared the performance of our method with all the five connectivity methods.…”

Section: Resultsmentioning

confidence: 99%

MNBDR: A Module Network Based Method for Drug Repositioning

Chen

Zhou

2020

Genes

View full text Add to dashboard Cite

Drug repurposing/repositioning, which aims to find novel indications for existing drugs, contributes to reducing the time and cost for drug development. For the recent decade, gene expression profiles of drug stimulating samples have been successfully used in drug repurposing. However, most of the existing methods neglect the gene modules and the interactions among the modules, although the cross-talks among pathways are common in drug response. It is essential to develop a method that utilizes the cross-talks information to predict the reliable candidate associations. In this study, we developed MNBDR (Module Network Based Drug Repositioning), a novel method that based on module network to screen drugs. It integrated protein–protein interactions and gene expression profile of human, to predict drug candidates for diseases. Specifically, the MNBDR mined dense modules through protein–protein interaction (PPI) network and constructed a module network to reveal cross-talks among modules. Then, together with the module network, based on existing gene expression data set of drug stimulation samples and disease samples, we used random walk algorithms to capture essential modules in disease development and proposed a new indicator to screen potential drugs for a given disease. Results showed MNBDR could provide better performance than popular methods. Moreover, functional analysis of the essential modules in the network indicated our method could reveal biological mechanism in drug response.

show abstract

Section: Resultsmentioning

confidence: 99%

MNBDR: A Module Network Based Method for Drug Repositioning

Chen

Zhou

2020

Genes

View full text Add to dashboard Cite

show abstract

“…top and bottom 100 genes). In any scenario, this choice is likely to significantly influence the performance of each metric in prioritizing real disease-drug associations [ 21 , 27 ].…”

Section: Discussionmentioning

confidence: 99%

“…Lin et al. [ 27 ] further evaluated connectivity approaches that use L1000 data [ 8 ], including six different scores that are used to predict drug–drug relationships.…”

Section: Introductionmentioning

confidence: 99%

“…Another example is the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mathrm{WCS}$\end{document} in CMap 2.0 [ 19 ] that is a bidirectional weighted version of ‘ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mathrm{ES}$\end{document} ’ used in GSEA [ 16 ]; in this case, they are named and notated quite differently though they are essentially direct, simple variants of each other. ‘ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$ZhangScore$\end{document} ’ in [ 27 ] and ‘ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mathrm{WSS}$\end{document} ’ in [ 22 ] refer to the connection strength \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$C$\end{document} in [ 23 ]. In this article, we develop a systematic scheme that defines in the aforementioned methodologies using consistent notations and terms.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reconciling multiple connectivity scores for drug repurposing

Samart

Tuyishime

Krishnan

et al. 2021

Briefings in Bioinformatics

View full text Add to dashboard Cite

The basis of several recent methods for drug repurposing is the key principle that an efficacious drug will reverse the disease molecular ‘signature’ with minimal side effects. This principle was defined and popularized by the influential ‘connectivity map’ study in 2006 regarding reversal relationships between disease- and drug-induced gene expression profiles, quantified by a disease-drug ‘connectivity score.’ Over the past 15 years, several studies have proposed variations in calculating connectivity scores toward improving accuracy and robustness in light of massive growth in reference drug profiles. However, these variations have been formulated inconsistently using various notations and terminologies even though they are based on a common set of conceptual and statistical ideas. Therefore, we present a systematic reconciliation of multiple disease-drug similarity metrics ($ES$, $css$, $Sum$, $Cosine$, $XSum$, $XCor$, $XSpe$, $XCos$, $EWCos$) and connectivity scores ($CS$, $RGES$, $NCS$, $WCS$, $Tau$, $CSS$, $EMUDRA$) by defining them using consistent notation and terminology. In addition to providing clarity and deeper insights, this coherent definition of connectivity scores and their relationships provides a unified scheme that newer methods can adopt, enabling the computational drug-development community to compare and investigate different approaches easily. To facilitate the continuous and transparent integration of newer methods, this article will be available as a live document (https://jravilab.github.io/connectivity_scores) coupled with a GitHub repository (https://github.com/jravilab/connectivity_scores) that any researcher can build on and push changes to.

show abstract

“…Moreover sometimes, due to the different technologies and other experimental settings, even when we restrict the analysis to common drugs, replicability across drug data sets may be low (see e.g. [Lim and Pavlidis, 2019] [Lin et al, 2020] reporting low replicability across CMAP and L1000 data using the CMAP algorithm). Moreover the performance measures we use measure the quality of the whole ranking, and the ranking itself gives a way to compare drugs listed by position.…”

Section: Discussionmentioning

confidence: 99%

Drug Repositioning by Merging Active Subnetworks Validated in Cancer and COVID-19

Lucchetta

Pellegrini

2021

Preprint

View full text Add to dashboard Cite

Computational Drug Repositioning aims at ranking and selecting existing drugs for use in novel diseases or existing diseases for which these drugs were not originally designed. Using vast amounts of available omic data in digital form within an in silico screening has the potential for speeding up considerably the shortlisting of promising candidates in response to outbreaks of diseases such as COVID-19 for which no satisfactory cure has yet been found. We describe DrugMerge as a methodology for preclinical computational drug repositioning based on merging multiple drug rankings obtained with an ensemble of Disease Active Subnetwork construction algorithms. DrugMerge uses differential transcriptomic data from cell lines/tissues of patients affected by the disease and differential transcriptomic data from drug perturbation assays, in the context of a large gene co-expression network. Experiments with four benchmark diseases (Asthma, Rheumatoid Arthritis, Prostate Cancer, and Colorectal Cancer) demonstrate that our method detects in first position drugs in clinical use for the specified disease, in all four cases. Our method is competitive with the state-of-the-art tools such as CMAP (Connectivity Map). Application of DrugMerge to COVID-19 data found rankings with many drugs currently in clinical trials for COVID-19 in top positions, thus showing that DrugMerge is able to mimic human expert judgment

show abstract

A comprehensive evaluation of connectivity methods for L1000 data

Cited by 28 publications

References 28 publications

MNBDR: A Module Network Based Method for Drug Repositioning

MNBDR: A Module Network Based Method for Drug Repositioning

Reconciling multiple connectivity scores for drug repurposing

Drug Repositioning by Merging Active Subnetworks Validated in Cancer and COVID-19

Contact Info

Product

Resources

About