DRIM: A Web-Based System for Investigating Drug Response at the Molecular Level by Condition-Specific Multi-Omics Data Integration

Oh, Minsik; Park, Sungjoon; Lee, Sangseon; Lee, Jun Young; Lim, Sangsoo; Jeong, Dabin; Jo, Kyuri; Jung, Il Bong; Kim, Sun

doi:10.3389/fgene.2020.564792

Cited by 11 publications

(2 citation statements)

References 64 publications

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“…[ 67 ], an ANN-based Web-based decision-support tool “accurately predicted the no-show patients by using the variable set that was commonly selected by a genetic algorithm and simulated annealing (p. 1)”. In a combined multi-omics and time series data analysis scheme which recognized perturbed sub-pathways and regulatory mechanisms in drug response [ 68 ], “multi-omics potential mediator genes were chosen by embedding multi-omics data into gene-centric vector space using either a tensor decomposition or an autoencoder deep learning model (p. 3)”.…”

Section: Resultsmentioning

confidence: 99%

Machine and cognitive intelligence for human health: systematic review

Chen

Cheng

Wang³

et al. 2022

Brain Inf.

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Brain informatics is a novel interdisciplinary area that focuses on scientifically studying the mechanisms of human brain information processing by integrating experimental cognitive neuroscience with advanced Web intelligence-centered information technologies. Web intelligence, which aims to understand the computational, cognitive, physical, and social foundations of the future Web, has attracted increasing attention to facilitate the study of brain informatics to promote human health. A large number of articles created in the recent few years are proof of the investment in Web intelligence-assisted human health. This study systematically reviews academic studies regarding article trends, top journals, subjects, countries/regions, and institutions, study design, artificial intelligence technologies, clinical tasks, and performance evaluation. Results indicate that literature is especially welcomed in subjects such as medical informatics and health care sciences and service. There are several promising topics, for example, random forests, support vector machines, and conventional neural networks for disease detection and diagnosis, semantic Web, ontology mining, and topic modeling for clinical or biomedical text mining, artificial neural networks and logistic regression for prediction, and convolutional neural networks and support vector machines for monitoring and classification. Additionally, future research should focus on algorithm innovations, additional information use, functionality improvement, model and system generalization, scalability, evaluation, and automation, data acquirement and quality improvement, and allowing interaction. The findings of this study help better understand what and how Web intelligence can be applied to promote healthcare procedures and clinical outcomes. This provides important insights into the effective use of Web intelligence to support informatics-enabled brain studies.

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Section: Resultsmentioning

confidence: 99%

Machine and cognitive intelligence for human health: systematic review

Chen

Cheng

Wang³

et al. 2022

Brain Inf.

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“…An interpretable neural network is constructed based on the semantics of the GO terms, and through training of 500,000 cell line-drug pairs, the model predicts drug response and simultaneously visualizes mechanisms related to response. Another pharmacogenomic framework named DRIM [ 122 ] integrates multi-omics data and pathway information for understanding the effect of drug treatment. Based on potential mediator genes selected from multi-omics data through tensor decomposition and autoencoder methods, the model analyzes time-series gene expression data upon drug treatment for identifying perturbed sub-pathways and regulation mechanisms.…”

Section: Discussionmentioning

confidence: 99%

A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective

Bang

Park

et al. 2022

IJMS

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Molecular and sequencing technologies have been successfully used in decoding biological mechanisms of various diseases. As revealed by many novel discoveries, the role of non-coding RNAs (ncRNAs) in understanding disease mechanisms is becoming increasingly important. Since ncRNAs primarily act as regulators of transcription, associating ncRNAs with diseases involves multiple inference steps. Leveraging the fast-accumulating high-throughput screening results, a number of computational models predicting ncRNA-disease associations have been developed. These tools suggest novel disease-related biomarkers or therapeutic targetable ncRNAs, contributing to the realization of precision medicine. In this survey, we first introduce the biological roles of different ncRNAs and summarize the databases containing ncRNA-disease associations. Then, we suggest a new trend in recent computational prediction of ncRNA-disease association, which is the mode of action (MoA) network perspective. This perspective includes integrating ncRNAs with mRNA, pathway and phenotype information. In the next section, we describe computational methodologies widely used in this research domain. Existing computational studies are then summarized in terms of their coverage of the MoA network. Lastly, we discuss the potential applications and future roles of the MoA network in terms of integrating biological mechanisms for ncRNA-disease associations.

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Data integration and mechanistic modelling for breast cancer biology: Current state and future directions

Breitling

Francavilla

et al. 2022

Current Opinion in Endocrine and Metabolic Research

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DRIM: A Web-Based System for Investigating Drug Response at the Molecular Level by Condition-Specific Multi-Omics Data Integration

Cited by 11 publications

References 64 publications

Machine and cognitive intelligence for human health: systematic review

Machine and cognitive intelligence for human health: systematic review

A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective

Data integration and mechanistic modelling for breast cancer biology: Current state and future directions

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