Overview
How we perceive intricate biological data, formulate experiments, and create therapeutic strategies is being completely transformed by the convergence of deep learning with biotechnology and biomedicine. By applying big data and revealing hidden patterns, deep learning is opening up new frontiers in such areas as protein annotation, microfluidics, and personalized medicine. (Riordon et al., 2019).
Microfluidics and Deep Learning: The Ideal Pair
As it allows the control of fluid dynamics at a microscale precisely and high-throughput experiments to be conducted while using fewer agents for testing, microfluidics is currently a major biotechnology instrument. However, scientists cannot manage the considerable amounts of information produced by the microfluidic systems every single day. One can solve that by using deep learning, something that can operate with structured information such as pictures and sequences (Riordon et al., 2019).
Biotechnology Applications
Cell Classification: Cells in microfluidic devices have been classified without fluorescent tags through deep learning algorithms such as convolutional neural networks (CNNs). For instance, Chen et al. used deep neural networks and time-stretch quantitative phase microscopy to illustrate label-free cell classification (Riordon et al., 2019).
Flow Sculpting: To address inverse problems in microfluidics, e.g., to design channel geometries to produce desired flow patterns, Stoecklein et al. used deep learning (Riordon et al., 2019).
Prospects for the Future
Deep microfluidics and learning also find promise in organ-on-a-chip (OOC) systems, where artificial intelligence potentially can autonomously monitor and control interactions between more than one organ. Cloud-based deep learning also has the potential to make globally distributed microfluidic diagnostics possible for improving food safety and disease surveillance (Riordon et al., 2019).
The Protein Universe Is Annotated by Deep Learning
One of the hardest problems in biology is the prediction of protein function from amino acid sequences. Homology-less sequences are hard to deal with for traditional methods such as BLAST and hidden Markov models (HMMs). Deep learning is now filling this gap.
Advances in Protein Annotation
ProtCNN and ProtENN are deep learning models that predict protein family annotations from unaligned sequences. They were introduced by Bileschi et al. in 2022. They have enhanced the coverage of the Pfam database by 9.5% and perform better than classical methods, particularly remote homology detection (Bileschi et al., 2022).
Key Breakthroughs
One-Shot Learning: ProtREP, a version of ProtCNN, demonstrates the model's generalization by annotating sequences from new protein families with a single example (Bileschi et al., 2022).
Complementarity with HMMs: Deep learning complementarity with HMMs is demonstrated by the 38.6% improvement in annotation accuracy gained by merging the two approaches (Bileschi et al., 2022).
Biomedical Consequences
These developments allow for the annotation of previously unidentified proteins, like the human protein Q96HJ9, now associated with mitochondrial ATP synthase activity (Bileschi et al., 2022). Such a finding allows for the development of novel therapeutic targets.
Deep Learning in Biomedicine: From Diagnostics to Genomics
With improved diagnostic precision and the capability to replicate sophisticated biological systems, deep learning is revolutionizing biomedicine. It has a role to play in medical imaging, drug discovery, and genomics.
Interpretation of Variants and Genomics
Deep learning models such as DeepBind and DeepSEA make predictions for the impact of genetic variation on molecular phenotypes such as transcription factor binding or splicing, as reported by Wainberg et al. (2018). Such software are vital in the interpretation of variations in cancer and Mendelian disorders.
Drug Discovery
Deep learning speeds up the discovery of potential drugs in quantitative structure-activity relationship (QSAR) modeling. Multi-task learning was shown to increase accuracy, in which models make predictions for a number of targets (Wainberg et al., 2018). Utilizing common patterns among protein targets, for instance, a deep learning model beat conventional approaches at the Merck Molecular Activity Challenge (Wainberg et al., 2018).
Medical Imaging
From the diagnosis of diabetic retinopathy to the diagnosis of tumors, deep learning is excellent at analyzing medical images. By the indication of areas of interest, models can offer diagnoses with explanations (Wainberg et al., 2018). For example, dermatologist-level diagnosis of skin cancer has been attained by AI systems (Wainberg et al., 2018).
Obstacles and Prospects
Deep learning in biomedicine is not exempt from having pitfalls in spite of its promise:
Data Scarcity: There is limited, large, labeled data for the majority of biological problems. The problem is mitigated by semi-supervised approaches and transfer learning (Wainberg et al., 2018).
Interpretability: Stakeholders such as regulators and clinicians require transparent models. Interpretability is being enabled by methods such as attention mechanisms and in silico mutagenesis (Wainberg et al., 2018).
Integration with Other Methods: Usage and reliability are improved when deep learning is coupled with other classic tools like HMMs (Bileschi et al., 2022).
Deep learning in the coming years may allow AI to power personalized medicine, in which treatment is customized according to each patient's unique genetic and molecular signature (Wainberg et al., 2018). AI systems may be able to design experiments independently, identify diseases, and even predict unheard-of patient outcomes in the future.
Conclusion
Through overcoming data analysis bottlenecks, annotation of previously unidentified biological sequences, and improving diagnostic precision, deep learning transforms biotechnology and biomedicine. With these technologies evolving further, their application to clinical practice and research will open new avenues for disease understanding and treatment. The convergence of deep learning and experimental biology promises a future of information from data leading to revolutionary healthcare solutions.
References
Riordon, J., Sovilj, D., Sanner, S., Sinton, D., & Young, E. W. K. (2019). Deep learning with microfluidics for biotechnology. Trends in Biotechnology, 37(3), 310–324. https://doi.org/10.1016/j.tibtech.2018.08.005
Bileschi, M. L., Belanger, D., Bryant, D. H., et al. (2022). Using deep learning to annotate the protein universe. Nature Biotechnology, 40, 932–937. https://doi.org/10.1038/s41587-021-01179-w
Wainberg, M., Merico, D., Delong, A., & Frey, B. J. (2018). Deep learning in biomedicine. Nature Biotechnology, 36(9), 829–838. https://doi.org/10.1038/nbt.4233
