DRPADC: A novel drug repositioning algorithm predicting adaptive drugs for COVID-19

Xie, Guobo; Xu, Haojie; Li, Jianming; Gu, Guosheng; Sun, Yeping; Lin, Zhiyi; Zhu, Yinting; Wang, Weiming; Wang, Youfu; Shao, Jiang

doi:10.1016/j.compchemeng.2022.107947

Cited by 4 publications

(5 citation statements)

References 71 publications

(30 reference statements)

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“…In practical applications, acquiring stable sample data is often challenging, constraining the effectiveness and ability of these methods. Additionally, traditional machine learning methods are highly dependent on input data and feature extraction, making them less practical for real-world applications [ 14 ]. During the network propagation process in network propagation-based methods, information resources tend to favor edges with higher weights, which deprives nodes lacking associated information of resources for extended periods, resulting in the ‘cold-start problem’ [ 14 ].…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, matrix completion or matrix factorization methods use ‘submatrix simulation’ techniques, which are more flexible in integrating a priori information and do not rely heavily on predefined labels or negative samples. Instead, these methods extract implicit patterns from existing data matrices, capture the original matrix information through submatrices and generate low-rank simulation matrices to fill in the missing portions of the original association matrices [ 14 , 34 ]. This approach does not require prior knowledge of extensive association information for predictions and has the advantages of adapting to sparse data, adapting to heterogeneous data, and scalability.…”

Section: Discussionmentioning

confidence: 99%

“…Current heterogeneous network-based methods can be roughly divided into three categories based on their algorithm: machine learning-based methods, network propagation-based methods and matrix completion or factorization-based methods [ 14 , 15 ]. Furthermore, methods derived from heterogeneous networks can be broadly categorized based on their underlying network structures into bipartite networks-based methods, tripartite networks-based methods and other complex networks-based methods.…”

Section: Introductionmentioning

confidence: 99%

“…For example, while Xie et al. [ 14 ] contend that the BNNR method outperforms HGIMC, other researchers, such as Yang et al. [ 15 ] and Yan et al.…”

Section: Introductionmentioning

confidence: 99%

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A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods

Li,

Yang,

Tong

et al. 2024

Briefings in Bioinformatics

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Computational drug repositioning, which involves identifying new indications for existing drugs, is an increasingly attractive research area due to its advantages in reducing both overall cost and development time. As a result, a growing number of computational drug repositioning methods have emerged. Heterogeneous network-based drug repositioning methods have been shown to outperform other approaches. However, there is a dearth of systematic evaluation studies of these methods, encompassing performance, scalability and usability, as well as a standardized process for evaluating new methods. Additionally, previous studies have only compared several methods, with conflicting results. In this context, we conducted a systematic benchmarking study of 28 heterogeneous network-based drug repositioning methods on 11 existing datasets. We developed a comprehensive framework to evaluate their performance, scalability and usability. Our study revealed that methods such as HGIMC, ITRPCA and BNNR exhibit the best overall performance, as they rely on matrix completion or factorization. HINGRL, MLMC, ITRPCA and HGIMC demonstrate the best performance, while NMFDR, GROBMC and SCPMF display superior scalability. For usability, HGIMC, DRHGCN and BNNR are the top performers. Building on these findings, we developed an online tool called HN-DREP (http://hn-drep.lyhbio.com/) to facilitate researchers in viewing all the detailed evaluation results and selecting the appropriate method. HN-DREP also provides an external drug repositioning prediction service for a specific disease or drug by integrating predictions from all methods. Furthermore, we have released a Snakemake workflow named HN-DRES (https://github.com/lyhbio/HN-DRES) to facilitate benchmarking and support the extension of new methods into the field.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…For example, while Xie et al. [ 14 ] contend that the BNNR method outperforms HGIMC, other researchers, such as Yang et al. [ 15 ] and Yan et al.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods

Li,

Yang,

Tong

et al. 2024

Briefings in Bioinformatics

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“…They employed advanced DL-based models with canonical SMILES representation and more than 800 bioactives and 29 targets against nine coronavirus variants. Xie et al ( 2022 ) proposed a compressed sensing algorithm combined with centered kernel alignment that shortlisted total 15 drug candidates as therapeutics for COVID-19.…”

Section: Facilitating Drug Discovery and Repurposingmentioning

confidence: 99%

Navigating with chemometrics and machine learning in chemistry

Joshi¹

2023

Artif Intell Rev

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Chemometrics and machine learning are artificial intelligence-based methods stirring a transformative change in chemistry. Organic synthesis, drug discovery and analytical techniques are incorporating machine learning techniques at an accelerated pace. However, machine-assisted chemistry faces challenges while solving critical problems in chemistry due to complex relationships in data sets. Even with increasing publishing volumes on machine learning, its application in areas of chemistry is not a straightforward endeavour. A particular concern in applying machine learning in chemistry is data availability and reproducibility. The present review article discusses the various chemometric methods, expert systems, and machine learning techniques developed for solving problems of organic synthesis and drug discovery with selected examples. Further, a concise discussion on chemometrics and ML deployed in analytical techniques such as, spectroscopy, microscopy and chromatography are presented. Finally, the review reflects the challenges, opportunities and future perspectives on machine learning and automation in chemistry. The review concludes by pondering on some tough questions on applying machine learning and their possibility of navigation in the different terrains of chemistry.

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iEssLnc: quantitative estimation of lncRNA gene essentialities with meta-path-guided random walks on the lncRNA-protein interaction network

Zhang

Liang

2023

Briefings in Bioinformatics

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Gene essentiality is defined as the extent to which a gene is required for the survival and reproductive success of a living system. It can vary between genetic backgrounds and environments. Essential protein coding genes have been well studied. However, the essentiality of non-coding regions is rarely reported. Most regions of human genome do not encode proteins. Determining essentialities of non-coding genes is demanded. We developed iEssLnc models, which can assign essentiality scores to lncRNA genes. As far as we know, this is the first direct quantitative estimation to the essentiality of lncRNA genes. By taking the advantage of graph neural network with meta-path-guided random walks on the lncRNA-protein interaction network, iEssLnc models can perform genome-wide screenings for essential lncRNA genes in a quantitative manner. We carried out validations and whole genome screening in the context of human cancer cell-lines and mouse genome. In comparisons to other methods, which are transferred from protein-coding genes, iEssLnc achieved better performances. Enrichment analysis indicated that iEssLnc essentiality scores clustered essential lncRNA genes with high ranks. With the screening results of iEssLnc models, we estimated the number of essential lncRNA genes in human and mouse. We performed functional analysis to find that essential lncRNA genes interact with microRNAs and cytoskeletal proteins significantly, which may be of interest in experimental life sciences. All datasets and codes of iEssLnc models have been deposited in GitHub (https://github.com/yyZhang14/iEssLnc).

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DRPADC: A novel drug repositioning algorithm predicting adaptive drugs for COVID-19

Cited by 4 publications

References 71 publications

A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods

A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods

Navigating with chemometrics and machine learning in chemistry

iEssLnc: quantitative estimation of lncRNA gene essentialities with meta-path-guided random walks on the lncRNA-protein interaction network

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