Network approaches for modeling the effect of drugs and diseases

Rintala, Teemu; Ghosh, Arindam; Fortino, Vittorio

doi:10.1093/bib/bbac229

Cited by 16 publications

(10 citation statements)

References 136 publications

(115 reference statements)

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“…Among them, the network-based approach accounts for a large proportion. Rintala et al (2022) reviewed some representative drug repurposing results for COVID-19, which applied network proximity algorithms. Most of the existing computational methods have two main shortcomings.…”

Section: Open Access Edited Bymentioning

confidence: 99%

Adverse effect signature extraction and prediction for drugs treating COVID-19

Wang

et al. 2022

Front. Genet.

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Given the considerable cost of drug discovery, drug repurposing is becoming attractive as it can effectively shorten the development timeline and reduce the development cost. However, most existing drug-repurposing methods omitted the heterogeneous health conditions of different COVID-19 patients. In this study, we evaluated the adverse effect (AE) profiles of 106 COVID-19 drugs. We extracted four AE signatures to characterize the AE distribution of 106 COVID-19 drugs by non-negative matrix factorization (NMF). By integrating the information from four distinct databases (AE, bioassay, chemical structure, and gene expression information), we predicted the AE profiles of 91 drugs with inadequate AE feedback. For each of the drug clusters, discriminant genes accounting for mechanisms of different AE signatures were identified by sparse linear discriminant analysis. Our findings can be divided into three parts. First, drugs abundant with AE-signature 1 (for example, remdesivir) should be taken with caution for patients with poor liver, renal, or cardiac functions, where the functional genes accumulate in the RHO GTPases Activate NADPH Oxidases pathway. Second, drugs featuring AE-signature 2 (for example, hydroxychloroquine) are unsuitable for patients with vascular disorders, with relevant genes enriched in signal transduction pathways. Third, drugs characterized by AE signatures 3 and 4 have relatively mild AEs. Our study showed that NMF and network-based frameworks contribute to more precise drug recommendations.

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Section: Open Access Edited Bymentioning

confidence: 99%

Adverse effect signature extraction and prediction for drugs treating COVID-19

Wang

et al. 2022

Front. Genet.

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“…Several computational models have been developed to overcome this challenge by improving our understanding of how drugs and genes interact in the human body. For example, the mechanistic models have been developed to investigate the drugs interactions with cells and molecules in the tumors [ 6 , 7 ], and data mining techniques have been used to find relationships between drugs and genes by representing biological networks through graphs and knowledge-based networks [ 8 ]. However, the most common approach is the use of machine learning [ 9–11 ] and deep learning [ 12 ] in a variety of different ways to predict the treatments’ responses.…”

Section: Introductionmentioning

confidence: 99%

Optimal fusion of genotype and drug embeddings in predicting cancer drug response

Nguyen,

Campbell,

Kumar

et al. 2024

Briefings in Bioinformatics

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Predicting cancer drug response using both genomics and drug features has shown some success compared to using genomics features alone. However, there has been limited research done on how best to combine or fuse the two types of features. Using a visible neural network with two deep learning branches for genes and drug features as the base architecture, we experimented with different fusion functions and fusion points. Our experiments show that injecting multiplicative relationships between gene and drug latent features into the original concatenation-based architecture DrugCell significantly improved the overall predictive performance and outperformed other baseline models. We also show that different fusion methods respond differently to different fusion points, indicating that the relationship between drug features and different hierarchical biological level of gene features is optimally captured using different methods. Considering both predictive performance and runtime speed, tensor product partial is the best-performing fusion function to combine late-stage representations of drug and gene features to predict cancer drug response.

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“…Examples of biological networks include protein-protein interaction (PPI) [1] , [2] , co-expression [3] , metabolic [4] , signaling [5] , [6] , and gene regulatory networks (GRNs) [7] . A major challenge in biomedical research is to extract meaningful and interpretable knowledge from molecular profiling (omics) data [8] . Consequently, the field of systems medicine, which deals with network-based modeling of biological systems, has led to deeper and even mechanistic insights from the accumulating wealth of omics data (see Fig.…”

Section: Introductionmentioning

confidence: 99%