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2020 2020 IEEE International Conference on Big Data and Smart Computing (BigComp)  
Drug-Induced Liver Injury Prediction Model using Transcriptional Response Data with Graph Neural Network 323 Doyeong Hwang (Korea University, Republic of Korea), Minji Jeon (Korea University, Republic  ...  on a Big Data Platform: A Multi-criteria Decision Making Model for Data-Intensive Science 229 Gautam Pal (University of Liverpool), Katie Atkinson (University of Liverpool), and Gangmin Li (Xi'an Jiaotong-Liverpool  ... 
doi:10.1109/bigcomp48618.2020.00004 fatcat:h26admm4frb7nfxm3ofn4et4ni

Knowledge-guided deep learning models of drug toxicity improve interpretation [article]

Yun Hao, Joseph D Romano, Jason H Moore
2022 bioRxiv   pre-print
Here we developed DTox (Deep learning for Toxicology), an interpretation framework for knowledge-guided neural networks, which can predict compound response to toxicity assays and infer toxicity pathways  ...  We demonstrate that DTox can achieve the same level of predictive performance as conventional models with a significant improvement in interpretability.  ...  We applied the DTox models of cell viability to perform a virtual screening of ~700,000 compounds and linked the predicted cytotoxicity score with clinical phenotypes of drug-induced liver injury.  ... 
doi:10.1101/2022.02.28.482300 fatcat:5c7ue23465fhjpxzt2komtjq2q

Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis

Yunyi Wu, Guanyu Wang
2018 International Journal of Molecular Sciences  
Among its many applications, toxicity prediction is essential to reduce the cost and labor of a drug's preclinical and clinical trials, because a lot of drug evaluations (cellular, animal, and clinical  ...  In the era of Big Data and artificial intelligence, toxicity prediction can benefit from machine learning, which has been widely used in many fields such as natural language processing, speech recognition  ...  In recent years, two neural network models, recurrent neural networks (RNN) [55, 56] , and convolutional neural networks (CNN) [57, 58] , have been commonly used in deep learning.  ... 
doi:10.3390/ijms19082358 pmid:30103448 fatcat:mjgeejthrzex7kbyxgncnncgla

Deep learning in pharmacogenomics: from gene regulation to patient stratification

Alexandr A Kalinin, Gerald A Higgins, Narathip Reamaroon, Sayedmohammadreza Soroushmehr, Ari Allyn-Feuer, Ivo D Dinov, Kayvan Najarian, Brian D Athey
2018 Pharmacogenomics (London)  
We anticipate that in the future deep learning will be widely used to predict personalized drug response and optimize medication selection and dosing, using knowledge extracted from large and complex molecular  ...  of drugs, targets, and their interactions.  ...  [123] , seizure-inducing side effects of preclinical drugs [124] , patient survival from multi-omic data [38] , drug-induced liver injury prediction [62] , and classifying genomic variants into adverse  ... 
doi:10.2217/pgs-2018-0008 pmid:29697304 fatcat:tkhmrqkevjfqxdty6ttbw33jam

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment

Angela Serra, Michele Fratello, Luca Cattelani, Irene Liampa, Georgia Melagraki, Pekka Kohonen, Penny Nymark, Antonio Federico, Pia Anneli Sofia Kinaret, Karolina Jagiello, My Kieu Ha, Jang-Sik Choi (+8 others)
2020 Nanomaterials  
After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied.  ...  of accurate and stable predictive models.  ...  [159] compared the performances of deep neural networks (DNN) with respect to RF and SVM in the prediction of chemically induced liver injuries.  ... 
doi:10.3390/nano10040708 pmid:32276469 pmcid:PMC7221955 fatcat:7n4mf747jjhpjhkvsxqwbu2kfq

Identification and New Indication of Melanin-Concentrating Hormone Receptor 1 (MCHR1) Antagonist Derived from Machine Learning and Transcriptome-Based Drug Repositioning Approaches

Gyutae Lim, Ka Young You, Jeong Hyun Lee, Moon Kook Jeon, Byung Ho Lee, Jae Yong Ryu, Kwang-Seok Oh
2022 International Journal of Molecular Sciences  
In this study, we identified KRX-104130 with potent MCHR1 antagonistic activity and no cardiotoxicity through virtual screening using two MCHR1 binding affinity prediction models and an hERG-induced cardiotoxicity  ...  In a NASH mouse model, the administration of KRX-104130 showed a protective effect by reducing hepatic lipid accumulation, liver injury, and histopathological changes, indicating a promising prospect for  ...  Acknowledgments: The chemical library used in this study was kindly provided by the Korea Chemical Bank (, accessed on 1 February 2021) of Korea Research Institute of Chemical Technology  ... 
doi:10.3390/ijms23073807 pmid:35409167 pmcid:PMC8998904 fatcat:pd3y6y3lf5hb5hvlkxsylynldq

Detection of Target Genes for Drug Repurposing to Treat Skeletal Muscle Atrophy in Mice Flown in Spaceflight

Vidya Manian, Jairo Orozco-Sandoval, Victor Diaz-Martinez, Heeralal Janwa, Carlos Agrinsoni
2022 Genes  
Graph convolutional networks, graph neural networks, random forest, and gradient boosting methods were trained using the embeddings, network features for predicting links and ranking top gene-disease associations  ...  Drugs were selected and a disease drug knowledge graph was constructed.  ...  Data Availability Statement:, accessed on 1 August 2021.  ... 
doi:10.3390/genes13030473 pmid:35328027 pmcid:PMC8953707 fatcat:abyd7zmu6vaxtk5ojk645rbgru

Preclinical models of idiosyncratic drug-induced liver injury (iDILI): Moving towards prediction

Antonio Segovia-Zafra, Daniel E. Di Zeo-Sánchez, Carlos López-Gómez, Zeus Pérez-Valdés, Eduardo García-Fuentes, Raúl J. Andrade, M. Isabel Lucena, Marina Villanueva-Paz
2021 Acta Pharmaceutica Sinica B  
Idiosyncratic drug-induced liver injury (iDILI) encompasses the unexpected harms that prescription and non-prescription drugs, herbal and dietary supplements can cause to the liver. iDILI remains a major  ...  the disease and the most used in vivo animal iDILI models.  ...  One exception is a mouse model of amodiaquine-induced liver injury, in which the treatment with the drug led to a delayedonset liver injury mediated by natural killer (NK) cells 274 .  ... 
doi:10.1016/j.apsb.2021.11.013 pmid:35024301 pmcid:PMC8727925 fatcat:3adqmbk33ra2xcbkb5deo3u3fi

A review on Machine Learning approaches and trends in drug discovery

Paula Carracedo-Reboredo, Jose Linares-Blanco, Nereida Rodriguez-Fernandez, Francisco Cedron, Francisco J. Novoa, Adrian Carballal, Victor Maojo, Alejandro Pazos, Carlos Fernandez-Lozano
2021 Computational and Structural Biotechnology Journal  
This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.  ...  Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies.  ...  For example, The Graph Neural Network Model [51] , published in 2009, opened up a new field of application in drug discovery. Until then, the vast majority of models used data from QSAR models.  ... 
doi:10.1016/j.csbj.2021.08.011 pmid:34471498 pmcid:PMC8387781 fatcat:s5pwhypudfehbotkrofqgbq33m

Graph Neural Networks and Their Current Applications in Bioinformatics

Xiao-Meng Zhang, Li Liang, Lin Liu, Ming-Jing Tang
2021 Frontiers in Genetics  
Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space, perform particularly well in various tasks that process graph structure data.  ...  With the rapid accumulation of biological network data, GNNs have also become an important tool in bioinformatics.  ...  X-MZ contributed to the investigation and data curation. All authors have read and agreed to the published version of the manuscript.  ... 
doi:10.3389/fgene.2021.690049 fatcat:4p55ap6sivcy7h6dpne5fut6lu

Artificial Intelligence in Drug Discovery: Applications and Techniques [article]

Jianyuan Deng, Zhibo Yang, Iwao Ojima, Dimitris Samaras, Fusheng Wang
2021 arXiv   pre-print
Various AI techniques have been used in a wide range of applications, such as virtual screening and drug design.  ...  We also provide a GitHub repository ( with the collection of papers and codes, if applicable, as a learning resource, which is regularly updated  ...  Acknowledgement This project is partially funded by a Stony Brook University OVPR Seed Grant. The neural networks templates are from Visuals by (  ... 
arXiv:2106.05386v4 fatcat:w2at5y5jyffrxiejsupmwiimhq

Identification of the Potential Molecular Mechanisms Linking RUNX1 Activity with Nonalcoholic Fatty Liver Disease, by Means of Systems Biology

Laia Bertran, Ailende Eigbefoh-Addeh, Marta Portillo-Carrasquer, Andrea Barrientos-Riosalido, Jessica Binetti, Carmen Aguilar, Javier Ugarte Chicote, Helena Bartra, Laura Artigas, Mireia Coma, Cristóbal Richart, Teresa Auguet
2022 Biomedicines  
Artificial neural networks established NAFLD pathophysiological processes functionally related to RUNX1: hepatic insulin resistance, lipotoxicity, and hepatic injury-liver fibrosis.  ...  Our study indicated that RUNX1 might have a high relationship with hepatic injury-liver fibrosis, and a medium relationship with lipotoxicity and insulin resistance motives.  ...  Then, we used an artificial neural networks (ANNs) strategy [30, 31] to analyze these models in order to establish the functional relationships between RUNX1 and NAFLD, considering the motives both together  ... 
doi:10.3390/biomedicines10061315 pmid:35740337 pmcid:PMC9219880 fatcat:mp2qsixwkbctbgthai7nlwjhka

GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data

Guannan Liu, Manali Singha, Limeng Pu, Prasanga Neupane, Joseph Feinstein, Hsiao-Chun Wu, J. Ramanujam, Michal Brylinski
2021 Journal of Cheminformatics  
Although using protein-protein interaction networks and drug-perturbed gene expression profiles can facilitate system-level investigations of drug-target interactions, utilizing such large and heterogeneous  ...  utilize solely the information on a query drug and a putative target.  ...  Acknowledgements Portions of this research were conducted with high-performance computational resources provided by Louisiana State University.  ... 
doi:10.1186/s13321-021-00540-0 fatcat:bogrqny6pjd5lhdowfxziflvv4

Functional Interaction among lncRNA HOTAIR and MicroRNAs in Cancer and Other Human Diseases

Monica Cantile, Maurizio Di Bonito, Maura Tracey De Bellis, Gerardo Botti
2021 Cancers  
The lncRNA HOX transcript antisense RNA (HOTAIR) represents a diagnostic, prognostic, and predictive biomarker in many human cancers, and its functional interaction with miRNAs has been described as crucial  ...  LncRNAs are a class of non-coding RNAs mostly involved in regulation of cancer initiation, metastatic progression, and drug resistance, through participation in post-transcription regulatory processes  ...  type of neural network designed to work directly on graphs and leverage their structural information.  ... 
doi:10.3390/cancers13030570 pmid:33540611 pmcid:PMC7867281 fatcat:lbjnv5rvxfcapijgvd2pgic5vi

Integrated Analysis of Competitive Endogenous RNA Networks in Acute Ischemic Stroke

Zongkai Wu, Wanyi Wei, Hongzhen Fan, Yongsheng Gu, Litao Li, Hebo Wang
2022 Frontiers in Genetics  
Finally, we used the Genomics of Drug Sensitivity in Cancer (GDSC) database to predict the effect of the identified targets on drug sensitivity.Result: We identified 293 DEGs and 26 DEMirs associated with  ...  , protein–protein interaction (PPI) network, and gene transcription factors (TFs) network analyses were performed to identify hub genes and associated pathways.  ...  Oxygen glucose deprivation/re-oxygenation (OGD/R) was used to mimic neural injury.  ... 
doi:10.3389/fgene.2022.833545 pmid:35401659 pmcid:PMC8990852 fatcat:32tpc3urdnf23bdwz2wklvaqtu
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