**5. Gene signature-based methods and tools for drug repositioning**

Once the gene signatures of drugs and diseases, as well as other useful information (such as the structure of drugs), are ready, we could make a computational drug repositioning analysis. In the end, it is to find a method to connect the drug and disease. This connecting method could be a similarity metric [44], community discovery, matrix factorization and completion, machine learning-based models and so on. A good method should significantly enrich true positive results and deplete false-positive results.

There are several biologist-friendly web servers, convenient to use without the need for programming. The CMap version 1 website is one of the most popular websites in the field of drug repositioning. The CMap version 2 website supplies a more fruitful website. The enrichr website (https://maayanlab.cloud/Enrichr/) also provides the drug repositioning module with the drug and disease libraries (for example, Drug\_Perturbations\_from\_GEO\_down gene set) [45, 46]. Biologists could easily use these websites for drug repositioning without programming.

The nonparametric Kolmogorov–Smirnov statistic, formalized in Gene Set Enrichment Analysis (GSEA), was used in the original CMap article, indicating its power [8, 47]. It tests whether the empirical distribution of data (a set of genes) is different from a reference distribution (such as a ranked gene list related to a drug). The nonparametric test simplifies the statistical test process, making it feasible to multi situations.

PAGE (parametric analysis of gene set enrichment) was more sensitive and less-computational than GSEA [48], which could be used to evaluate the similarity between two gene expression signatures. Dr. Insight used the concordantly expressed genes in a frame-breaking statistical model to connect the drug and disease [49]. The eXtreme Sum (XSum) was a similarity scoring algorithm, which was developed by Jie et al. It showed a better performance than the KS statistic based on the area under the curve using 890 drug-indication pairs with 496 compounds and 238 disease signatures [50].

Network-based community discovery could exploit the similarity in gene expression signatures of drugs and identify the similar drugs, which should be clustered together [51]. They also implemented a tool, MANTRA (Mode of Action by NeTwoRk Analysis), which was accessible and biologist-friendly at http:// mantra.tigem.it [52]. GPSnet (Genome-wide Positioning Systems network) associated the drug and the gene signature-based disease modules in the protein–protein interactome network [53]. DeMAND (detecting mechanism of action by network dysregulation) developed a regulatory network-based approach to elucidate the MoA using gene expression signatures [54]. Chemical Checker integrated five-level

#### *Gene Signature-Based Drug Repositioning DOI: http://dx.doi.org/10.5772/intechopen.101377*

data of drugs, such as targets, morphology and gene expression signatures, to evaluate the similarity of the drugs via the dimensionality reduction and network embedding algorithm [55].

Cogena, co-expressed gene-set enrichment analysis, focused on the idea of targeting co-expressed genes instead of all the differentially expressed genes for drug repositioning [27]. It empowered simultaneous, gene set knowledgebase-driven drug repositioning analysis and illustrated the mode of action of the predicted drug and disease pairs. Cogena has been widely used in drug repositioning for several diseases, including psoriasis, Coronavirus Disease 2019 (COVID-19) [56, 57], Crohn's disease [58], periodontitis [59].

Machine learning, especially deep learning algorithms, are suitable to the gene expression signatures inherently. The low-rank matrix approximation and randomized algorithms were used in drug repositioning by filling out the unknown connection in the drug-disease pairs [60] The iDrug could reposition drugs via a cross-network embedding and transferring knowledge from the drug target information [61]. DLEPS (deep learning-based efficacy prediction system) used one-dimensional convolutional neural networks to learn the relationship between the structure of drugs and gene expression signatures to predict drug efficacy [62]. Clearly, with the advances of deep learning, especially the graph neural network, lots of innovative algorithms will be continually applied in the drug repositioning field to improve performance.
