Over the last decade, astounding progress in ML (especially deep learning) has started a revolution in bioimaging and post-processing of live cells and biomolecules. These approaches have improved our structural understanding of biomolecules by enhancing the quality of data and the characterization of their nanomechanical properties. Prediction of three-dimensional protein structure using deep learning algorithms (AlphaFold) is the ultimate progress made in that direction. Although optical microscopy, Scanning probe microscopy, Electron microscopy, and spectroscopy techniques like Infrared (IR) Spectroscopy, Ultraviolet-Visible (UV/Vis) Spectroscopy, Nuclear Magnetic Resonance (NMR), Spectroscopy, Raman Spectroscopy, and X-Ray Spectroscopy are extremely useful techniques to study biomolecules, proteins, DNA, RNA, and live cells, these techniques are not always very high-throughput. The data analysis, image quality, and many other aspects of these experimental techniques can be immensely improved by the implementation of machine learning approaches.
The goal is this journal is to showcase research articles demonstrating significant improvement over the multiple basic spectroscopy and microscopy techniques achieved using various machine-learning approaches. Different research communities such as biophysics, bioengineering, bioinformatics, and machine learning can benefit from our platform.
This journal aims to publish high-quality, fundamental and applied research, articles on applications related to a combination of any machine learning approach and any spectroscopy or microscopy technique to improve that technique or make it high-throughput. Papers on multi-disciplinary applications of various such combined technologies are welcome.
Keywords:
Machine learning, spectroscopy, microscopy, automation
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
Over the last decade, astounding progress in ML (especially deep learning) has started a revolution in bioimaging and post-processing of live cells and biomolecules. These approaches have improved our structural understanding of biomolecules by enhancing the quality of data and the characterization of their nanomechanical properties. Prediction of three-dimensional protein structure using deep learning algorithms (AlphaFold) is the ultimate progress made in that direction. Although optical microscopy, Scanning probe microscopy, Electron microscopy, and spectroscopy techniques like Infrared (IR) Spectroscopy, Ultraviolet-Visible (UV/Vis) Spectroscopy, Nuclear Magnetic Resonance (NMR), Spectroscopy, Raman Spectroscopy, and X-Ray Spectroscopy are extremely useful techniques to study biomolecules, proteins, DNA, RNA, and live cells, these techniques are not always very high-throughput. The data analysis, image quality, and many other aspects of these experimental techniques can be immensely improved by the implementation of machine learning approaches.
The goal is this journal is to showcase research articles demonstrating significant improvement over the multiple basic spectroscopy and microscopy techniques achieved using various machine-learning approaches. Different research communities such as biophysics, bioengineering, bioinformatics, and machine learning can benefit from our platform.
This journal aims to publish high-quality, fundamental and applied research, articles on applications related to a combination of any machine learning approach and any spectroscopy or microscopy technique to improve that technique or make it high-throughput. Papers on multi-disciplinary applications of various such combined technologies are welcome.
Keywords:
Machine learning, spectroscopy, microscopy, automation
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.