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1. Drug target ranking for glioblastoma multiforme

   https://doi.org/10.1186/s42490-021-00052-w  

   Saraf, Radhika; Agah, Shaghayegh; Datta, Aniruddha; Jiang, Xiaoqian (April 2021, BMC Biomedical Engineering)

   Abstract BackgroundGlioblastoma Multiforme, an aggressive primary brain tumor, has a poor prognosis and no effective standard of care treatments. Most patients undergoing radiotherapy, along with Temozolomide chemotherapy, develop resistance to the drug, and recurrence of the tumor is a common issue after the treatment. We propose to model the pathways active in Glioblastoma using Boolean network techniques. The network captures the genetic interactions and possible mutations that are involved in the development of the brain tumor. The model is used to predict the theoretical efficacies of drugs for the treatment of cancer. ResultsWe use the Boolean network to rank the critical intervention points in the pathway to predict an effective therapeutic strategy for Glioblastoma. Drug repurposing helps to identify non-cancer drugs that could be effective in cancer treatment. We predict the effectiveness of drug combinations of anti-cancer and non-cancer drugs for Glioblastoma. ConclusionsGiven the genetic profile of a GBM tumor, the Boolean model can predict the most effective targets for treatment. We also identified two-drug combinations that could be more effective in killing GBM cells than conventional chemotherapeutic agents. The non-cancer drug Aspirin could potentially increase the cytotoxicity of TMZ in GBM patients. 

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2. Joint embedding of biological networks for cross-species functional alignment

   https://doi.org/10.1093/bioinformatics/btad529  

   Li, Lechuan; Dannenfelser, Ruth; Zhu, Yu; Hejduk, Nathaniel; Segarra, Santiago; Yao, Vicky; Cowen, ed., Lenore (August 2023, Bioinformatics)

   Abstract MotivationModel organisms are widely used to better understand the molecular causes of human disease. While sequence similarity greatly aids this cross-species transfer, sequence similarity does not imply functional similarity, and thus, several current approaches incorporate protein–protein interactions to help map findings between species. Existing transfer methods either formulate the alignment problem as a matching problem which pits network features against known orthology, or more recently, as a joint embedding problem. ResultsWe propose a novel state-of-the-art joint embedding solution: Embeddings to Network Alignment (ETNA). ETNA generates individual network embeddings based on network topological structure and then uses a Natural Language Processing-inspired cross-training approach to align the two embeddings using sequence-based orthologs. The final embedding preserves both within and between species gene functional relationships, and we demonstrate that it captures both pairwise and group functional relevance. In addition, ETNA’s embeddings can be used to transfer genetic interactions across species and identify phenotypic alignments, laying the groundwork for potential opportunities for drug repurposing and translational studies. Availability and implementationhttps://github.com/ylaboratory/ETNA 

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3. Rapid growth in the COVID-19 era

   https://doi.org/10.1557/s43577-021-00185-2  

   Lee, Yerim; Ng, Michelle; Daniel, Kristin; Wayne, Elizabeth (September 2021, MRS Bulletin)

   AbstractFrom Operation Warp Speed to the lipid mRNA vaccine, the COVID-19 pandemic has been a watershed moment for technological development, production, and implementation. The scale and pace of innovation and global collaboration has likely not been experienced since World War II. This article highlights some of the engineering accomplishments that occurred during the pandemic. We provide a broad overview of the technological achievements in vaccine design, antibody engineering, drug repurposing, and rapid diagnostic testing. We also discuss what the future of these technologies and the future of large-scale collaborations might look like moving forward. Graphic abstract 

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4. NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug–target interactions

   https://doi.org/10.1093/bioinformatics/bty543  

   Wan, Fangping; Hong, Lixiang; Xiao, An; Jiang, Tao; Zeng, Jianyang; Wren, ed., Jonathan (July 2018, Bioinformatics)

   Abstract MotivationAccurately predicting drug–target interactions (DTIs) in silico can guide the drug discovery process and thus facilitate drug development. Computational approaches for DTI prediction that adopt the systems biology perspective generally exploit the rationale that the properties of drugs and targets can be characterized by their functional roles in biological networks. ResultsInspired by recent advance of information passing and aggregation techniques that generalize the convolution neural networks to mine large-scale graph data and greatly improve the performance of many network-related prediction tasks, we develop a new nonlinear end-to-end learning model, called NeoDTI, that integrates diverse information from heterogeneous network data and automatically learns topology-preserving representations of drugs and targets to facilitate DTI prediction. The substantial prediction performance improvement over other state-of-the-art DTI prediction methods as well as several novel predicted DTIs with evidence supports from previous studies have demonstrated the superior predictive power of NeoDTI. In addition, NeoDTI is robust against a wide range of choices of hyperparameters and is ready to integrate more drug and target related information (e.g. compound–protein binding affinity data). All these results suggest that NeoDTI can offer a powerful and robust tool for drug development and drug repositioning. Availability and implementationThe source code and data used in NeoDTI are available at: https://github.com/FangpingWan/NeoDTI. Supplementary informationSupplementary data are available at Bioinformatics online. 

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5. Multiscale topology in interactomic network: from transcriptome to antiaddiction drug repurposing

   https://doi.org/10.1093/bib/bbae054  

   Du, Hongyan; Wei, Guo-Wei; Hou, Tingjun (March 2024, Briefings in Bioinformatics)

   Abstract The escalating drug addiction crisis in the United States underscores the urgent need for innovative therapeutic strategies. This study embarked on an innovative and rigorous strategy to unearth potential drug repurposing candidates for opioid and cocaine addiction treatment, bridging the gap between transcriptomic data analysis and drug discovery. We initiated our approach by conducting differential gene expression analysis on addiction-related transcriptomic data to identify key genes. We propose a novel topological differentiation to identify key genes from a protein–protein interaction network derived from DEGs. This method utilizes persistent Laplacians to accurately single out pivotal nodes within the network, conducting this analysis in a multiscale manner to ensure high reliability. Through rigorous literature validation, pathway analysis and data-availability scrutiny, we identified three pivotal molecular targets, mTOR, mGluR5 and NMDAR, for drug repurposing from DrugBank. We crafted machine learning models employing two natural language processing (NLP)-based embeddings and a traditional 2D fingerprint, which demonstrated robust predictive ability in gauging binding affinities of DrugBank compounds to selected targets. Furthermore, we elucidated the interactions of promising drugs with the targets and evaluated their drug-likeness. This study delineates a multi-faceted and comprehensive analytical framework, amalgamating bioinformatics, topological data analysis and machine learning, for drug repurposing in addiction treatment, setting the stage for subsequent experimental validation. The versatility of the methods we developed allows for applications across a range of diseases and transcriptomic datasets. 

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