Deep Learning in Biomedicine

Overview

Journal Nat Biotechnol

Specialty Biotechnology

Date 2018 Sep 7

PMID 30188539

Citations 172

Authors

Michael Wainberg

Daniele Merico

Andrew Delong

Brendan J Frey

Affiliations

Soon will be listed here.

Abstract

Deep learning is beginning to impact biological research and biomedical applications as a result of its ability to integrate vast datasets, learn arbitrarily complex relationships and incorporate existing knowledge. Already, deep learning models can predict, with varying degrees of success, how genetic variation alters cellular processes involved in pathogenesis, which small molecules will modulate the activity of therapeutically relevant proteins, and whether radiographic images are indicative of disease. However, the flexibility of deep learning creates new challenges in guaranteeing the performance of deployed systems and in establishing trust with stakeholders, clinicians and regulators, who require a rationale for decision making. We argue that these challenges will be overcome using the same flexibility that created them; for example, by training deep models so that they can output a rationale for their predictions. Significant research in this direction will be needed to realize the full potential of deep learning in biomedicine.

Citing Articles

Deep learning in nuclear medicine: from imaging to therapy.

Zhang M, Liu P, Zhang M, Su P, Shang H, Zhu J Ann Nucl Med. 2025; .

PMID: 40080372 DOI: 10.1007/s12149-025-02031-w.

Transfer learning radiomic model predicts intratumoral tertiary lymphoid structures in hepatocellular carcinoma: a multicenter study.

Long S, Li M, Chen J, Zhong L, Dai G, Pan D J Immunother Cancer. 2025; 13(3).

PMID: 40037925 PMC: 11881188. DOI: 10.1136/jitc-2024-011126.

Unveiling the new chapter in nanobody engineering: advances in traditional construction and AI-driven optimization.

Liu J, Wu L, Xie A, Liu W, He Z, Wan Y J Nanobiotechnology. 2025; 23(1):87.

PMID: 39915791 PMC: 11800653. DOI: 10.1186/s12951-025-03169-5.

Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning.

Chernigovskaya M, Pavlovic M, Kanduri C, Gielis S, Robert P, Scheffer L Nucleic Acids Res. 2025; 53(3).

PMID: 39873270 PMC: 11773363. DOI: 10.1093/nar/gkaf025.

The clinical application of artificial intelligence in cancer precision treatment.

Wang J, Zeng Z, Li Z, Liu G, Zhang S, Luo C J Transl Med. 2025; 23(1):120.

PMID: 39871340 PMC: 11773911. DOI: 10.1186/s12967-025-06139-5.

References

Jurtz V, Johansen A, Nielsen M, Almagro Armenteros J, Nielsen H, Sonderby C . An introduction to deep learning on biological sequence data: examples and solutions. Bioinformatics. 2017; 33(22):3685-3690. PMC: 5870575. DOI: 10.1093/bioinformatics/btx531. View

Angermueller C, Lee H, Reik W, Stegle O . DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome Biol. 2017; 18(1):67. PMC: 5387360. DOI: 10.1186/s13059-017-1189-z. View

Mamoshina P, Vieira A, Putin E, Zhavoronkov A . Applications of Deep Learning in Biomedicine. Mol Pharm. 2016; 13(5):1445-54. DOI: 10.1021/acs.molpharmaceut.5b00982. View

Carpenter A, Jones T, Lamprecht M, Clarke C, Kang I, Friman O . CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 2006; 7(10):R100. PMC: 1794559. DOI: 10.1186/gb-2006-7-10-r100. View

Zhang S, Hu H, Jiang T, Zhang L, Zeng J . TITER: predicting translation initiation sites by deep learning. Bioinformatics. 2017; 33(14):i234-i242. PMC: 5870772. DOI: 10.1093/bioinformatics/btx247. View

Hinton G, Dayan P, Frey B, Neal R . The "wake-sleep" algorithm for unsupervised neural networks. Science. 1995; 268(5214):1158-61. DOI: 10.1126/science.7761831. View

Hoskins R, Repo S, Barsky D, Andreoletti G, Moult J, Brenner S . Reports from CAGI: The Critical Assessment of Genome Interpretation. Hum Mutat. 2017; 38(9):1039-1041. PMC: 5606199. DOI: 10.1002/humu.23290. View

Min S, Lee B, Yoon S . Deep learning in bioinformatics. Brief Bioinform. 2016; 18(5):851-869. DOI: 10.1093/bib/bbw068. View

Ma J, Yu M, Fong S, Ono K, Sage E, Demchak B . Using deep learning to model the hierarchical structure and function of a cell. Nat Methods. 2018; 15(4):290-298. PMC: 5882547. DOI: 10.1038/nmeth.4627. View

10.

Visscher P, Wray N, Zhang Q, Sklar P, McCarthy M, Brown M . 10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet. 2017; 101(1):5-22. PMC: 5501872. DOI: 10.1016/j.ajhg.2017.06.005. View

11.

Quang D, Xie X . DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Res. 2016; 44(11):e107. PMC: 4914104. DOI: 10.1093/nar/gkw226. View

12.

Kearnes S, McCloskey K, Berndl M, Pande V, Riley P . Molecular graph convolutions: moving beyond fingerprints. J Comput Aided Mol Des. 2016; 30(8):595-608. PMC: 5028207. DOI: 10.1007/s10822-016-9938-8. View

13.

Baldi P, Brunak S, Frasconi P, Soda G, Pollastri G . Exploiting the past and the future in protein secondary structure prediction. Bioinformatics. 2000; 15(11):937-46. DOI: 10.1093/bioinformatics/15.11.937. View

14.

Kelley D, Snoek J, Rinn J . Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res. 2016; 26(7):990-9. PMC: 4937568. DOI: 10.1101/gr.200535.115. View

15.

Kraus O, Grys B, Ba J, Chong Y, Frey B, Boone C . Automated analysis of high-content microscopy data with deep learning. Mol Syst Biol. 2017; 13(4):924. PMC: 5408780. DOI: 10.15252/msb.20177551. View

16.

Tan J, Ung M, Cheng C, Greene C . Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders. Pac Symp Biocomput. 2015; :132-43. PMC: 4299935. View

17.

Leinonen R, Sugawara H, Shumway M . The sequence read archive. Nucleic Acids Res. 2010; 39(Database issue):D19-21. PMC: 3013647. DOI: 10.1093/nar/gkq1019. View

18.

Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J . Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015; 17(5):405-24. PMC: 4544753. DOI: 10.1038/gim.2015.30. View

19.

Xiong H, Alipanahi B, Lee L, Bretschneider H, Merico D, Yuen R . RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease. Science. 2014; 347(6218):1254806. PMC: 4362528. DOI: 10.1126/science.1254806. View

20.

Zhou J, Troyanskaya O . Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods. 2015; 12(10):931-4. PMC: 4768299. DOI: 10.1038/nmeth.3547. View