Applications of Deep Learning in Biomedicine.

By Polina Mamoshina, Armando Vieira, E. Putin, A. Zhavoronkov. 2016

643 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Molecular Biology

Citations in the corpus

Deep learning for computational biology (2016, background): “The applications we cover are deliberately broad to illustrate differences and commonalities between approaches; reviews focusing on specific domains can be found elsewhere (Park & Kellis, 2015; Gawehn et al , 2016; Leung et al , 2016; Mamoshina et al , 2016).”
Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review (2017, background): “Deep learning methods have been extensively reviewed in recent years [36, 37, 38, 39, 40] and discussed for particular applications [41, 42, 43, 44].”
Deep Learning and Medical Diagnosis: A Review of Literature (2018, positive): “This includes but is not limited to health informatics [11], biomedicine [12], and magnetic resonance image MRI analysis [13].”
Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions (2023, methodology): “Automatic voice recognition was tested in 2009 to lower the phone error rate (PER) [64] using two different deep belief network topologies.”
Deep learning in bioinformatics: introduction, application, and perspective in big data era (2019, background): “[98] Biomarker development, Transcriptomics It discussed the key features of deep learning and a number of applications of it in biomedical studies”
Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes (2016, background): “…drug development costs exceed US$2.5 billion per the new molecular entity (NME) and the returns of investment in breakthrough drugs with the exception of cancer immunology, are on the decline (Munos and Chin 2011; Mignani et al. 2016; Rubin and Gilliland 2012; DiMasi, Grabowski, and Hansen 2016).”
Deep Multimodal Learning: A Survey on Recent Advances and Trends (2017, background): “A recent review article by Mamoshina et al. [21] demonstrates the rising popularity of DNNs, including models that implement multimodal fusion in biomedical applications involving genomic, proteomic, and drug data.”
Accelerating Drugs Discovery with Deep Reinforcement Learning: An Early Approach (2018, methodology): “They have been also successfully applied in drug discovery [6, 12, 33], including application such as QSAR [32], genomics [1, 61], and VS [8, 58].”
druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico. (2017, background): “substantial potential in many areas of biomedicine [6] such as biomarker development”
Artificial intelligence and innovation management: A review, framework, and research agenda✰ (2021, background): “There are, of course, interesting AI applications in pharmaceutical research and development as well (e.g., Mamoshina et al., 2016; Schuhmacher et al., Inpress).”