AIMC Topic: Mice

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Phasic dopamine release identification using convolutional neural network.

Computers in biology and medicine
Dopamine has a major behavioral impact related to drug dependence, learning and memory functions, as well as pathologies such as schizophrenia and Parkinson's disease. Phasic release of dopamine can be measured in vivo with fast-scan cyclic voltammet...

Sequential Saliency Guided Deep Neural Network for Joint Mitosis Identification and Localization in Time-Lapse Phase Contrast Microscopy Images.

IEEE journal of biomedical and health informatics
The analysis of cell mitotic behavior plays important role in many biomedical research and medical diagnostic applications. To improve the accuracy of mitosis detection in automated analysis systems, this paper proposes the sequential saliency guided...

Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning.

Nature communications
Highly specific Cas9 nucleases derived from SpCas9 are valuable tools for genome editing, but their wide applications are hampered by a lack of knowledge governing guide RNA (gRNA) activity. Here, we perform a genome-scale screen to measure gRNA acti...

Three-dimensional convolutional neural networks for simultaneous dual-tracer PET imaging.

Physics in medicine and biology
Dual-tracer positron emission tomography (PET) is a promising technique to measure the distribution of two tracers in the body by a single scan, which can improve the clinical accuracy of disease diagnosis and can also serve as a research tool for sc...

Neuromuscular actuation of biohybrid motile bots.

Proceedings of the National Academy of Sciences of the United States of America
The integration of muscle cells with soft robotics in recent years has led to the development of biohybrid machines capable of untethered locomotion. A major frontier that currently remains unexplored is neuronal actuation and control of such muscle-...

A classification model for lncRNA and mRNA based on k-mers and a convolutional neural network.

BMC bioinformatics
BACKGROUND: Long-chain non-coding RNA (lncRNA) is closely related to many biological activities. Since its sequence structure is similar to that of messenger RNA (mRNA), it is difficult to distinguish between the two based only on sequence biometrics...

Machine-Learning Prediction of Tumor Antigen Immunogenicity in the Selection of Therapeutic Epitopes.

Cancer immunology research
Current tumor neoantigen calling algorithms primarily rely on epitope/major histocompatibility complex (MHC) binding affinity predictions to rank and select for potential epitope targets. These algorithms do not predict for epitope immunogenicity usi...

Enhancement of Acoustic Microscopy Lateral Resolution: A Comparison Between Deep Learning and Two Deconvolution Methods.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Scanning acoustic microscopy (SAM) provides high-resolution images of biological tissues. Since higher transducer frequencies limit penetration depth, image resolution enhancement techniques could help in maintaining sufficient lateral resolution wit...

Machine learning predicts putative hematopoietic stem cells within large single-cell transcriptomics data sets.

Experimental hematology
Hematopoietic stem cells (HSCs) are an essential source and reservoir for normal hematopoiesis, and their function is compromised in many blood disorders. HSC research has benefitted from the recent development of single-cell molecular profiling tech...

Utilizing supervised machine learning to identify microglia and astrocytes in situ: implications for large-scale image analysis and quantification.

Journal of neuroscience methods
BACKGROUND: The evaluation of histological tissue samples plays a crucial role in deciphering preclinical disease and injury mechanisms. High-resolution images can be obtained quickly however data acquisition are often bottlenecked by manual analysis...