2016
DOI: 10.1021/acs.molpharmaceut.5b00982
|View full text |Cite
|
Sign up to set email alerts
|

Abstract: Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this data may be useful for biomarker identification and drug discovery, the bulk of it remains underutilized. Deep neural networks (DNNs) are efficient algorithms based on the use of compositional layers of neurons, with advantages well matched to the challenges -omics data pres… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

1
372
0
7

Year Published

2016
2016
2022
2022

Publication Types

Select...
5
5

Relationship

1
9

Authors

Journals

citations
Cited by 560 publications
(380 citation statements)
references
References 51 publications
1
372
0
7
Order By: Relevance
“…To date there have been relatively few studies that have made comparisons of deep learning to the wide array of classical machine learning methods or have discussed this methods application in pharmaceutical research 41, 110, 111 or even used the models for actual predictions for ongoing projects. This study therefore fills a void related to drug discovery applications of these methods.…”
Section: Discussionmentioning
confidence: 99%
“…To date there have been relatively few studies that have made comparisons of deep learning to the wide array of classical machine learning methods or have discussed this methods application in pharmaceutical research 41, 110, 111 or even used the models for actual predictions for ongoing projects. This study therefore fills a void related to drug discovery applications of these methods.…”
Section: Discussionmentioning
confidence: 99%
“…The ability learn at the higher levels of abstraction made DL is a promising and effective tool for working with biological and chemical data 7 . Methods using DL architecture capable to deal with sparse and complex information, which is especially demanded in the analysis of high-dimensional gene expression data.…”
Section: Inroductionmentioning
confidence: 99%
“…For example, cancer images with pathologically-proven labels are costly to collect at scale, thus data integration with different type of clinical labels may present an alternative means to overcome such obstacles for deep-learning models, which need large datasets as inputs 74 . Although it remains uncertain how we gain clinical insights from these deep-learning outcomes and how we optimize network architectures for the better use of multi-scale medical data (e.g., serial MRI, genomics, and clinical data) 75 , the extraction of compact patterns via hierarchical networks presents enormous opportunities for large-scale radiomics applications (see Figure 3). …”
Section: Research Opportunities and Challengesmentioning
confidence: 99%