%0 Journal Article %1 Mamoshina2016Applications %A Mamoshina, Polina %A Vieira, Armando %A Putin, Evgeny %A Zhavoronkov, Alex %D 2016 %I American Chemical Society %J Mol. Pharmaceutics %K deep-learning review %N 5 %P 1445--1454 %R 10.1021/acs.molpharmaceut.5b00982 %T Applications of Deep Learning in Biomedicine %U http://dx.doi.org/10.1021/acs.molpharmaceut.5b00982 %V 13 %X 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 presents. While achieving state-of-the-art results and even surpassing human accuracy in many challenging tasks, the adoption of deep learning in biomedicine has been comparatively slow. Here, we discuss key features of deep learning that may give this approach an edge over other machine learning methods. We then consider limitations and review a number of applications of deep learning in biomedical studies demonstrating proof of concept and practical utility.

@article{Mamoshina2016Applications, 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 presents. While achieving state-of-the-art results and even surpassing human accuracy in many challenging tasks, the adoption of deep learning in biomedicine has been comparatively slow. Here, we discuss key features of deep learning that may give this approach an edge over other machine learning methods. We then consider limitations and review a number of applications of deep learning in biomedical studies demonstrating proof of concept and practical utility.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Mamoshina, Polina and Vieira, Armando and Putin, Evgeny and Zhavoronkov, Alex}, biburl = {https://www.bibsonomy.org/bibtex/2cd23d881f426b0c5d466c704592efaa4/karthikraman}, citeulike-article-id = {14007962}, citeulike-linkout-0 = {http://dx.doi.org/10.1021/acs.molpharmaceut.5b00982}, citeulike-linkout-1 = {http://pubs.acs.org/doi/abs/10.1021/acs.molpharmaceut.5b00982}, day = 2, doi = {10.1021/acs.molpharmaceut.5b00982}, interhash = {d3a9bc06556ecd19c0f94c5a5d26e342}, intrahash = {cd23d881f426b0c5d466c704592efaa4}, journal = {Mol. Pharmaceutics}, keywords = {deep-learning review}, month = may, number = 5, pages = {1445--1454}, posted-at = {2016-04-11 13:38:40}, priority = {2}, publisher = {American Chemical Society}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {Applications of Deep Learning in Biomedicine}, url = {http://dx.doi.org/10.1021/acs.molpharmaceut.5b00982}, volume = 13, year = 2016 }