SigUNet: signal peptide recognition based on semantic segmentation.

BACKGROUND: Signal peptides play an important role in protein sorting, which is the mechanism whereby proteins are transported to their destination. Recognition of signal peptides is an important first step in determining the active locations and functions of proteins. Many computational methods have been proposed to facilitate signal peptide recognition. In recent years, the development of deep learning methods has seen significant advances in many research fields. However, most existing models for signal peptide recognition use one-hidden-layer neural networks or hidden Markov models, which are relatively simple in comparison with the deep neural networks that are used in other fields. RESULTS: This study proposes a convolutional neural network without fully connected layers, which is an important network improvement in computer vision. The proposed network is more complex in comparison with current signal peptide predictors. The experimental results show that the proposed network outperforms current signal peptide predictors on eukaryotic data. This study also demonstrates how model reduction and data augmentation helps the proposed network to predict bacterial data. CONCLUSIONS: The study makes three contributions to this subject: (a) an accurate signal peptide recognizer is developed, (b) the potential to leverage advanced networks from other fields is demonstrated and (c) important modifications are proposed while adopting complex networks on signal peptide recognition.

An automatic method to calculate heart rate from zebrafish larval cardiac videos.

BACKGROUND: Zebrafish is a widely used model organism for studying heart development and cardiac-related pathogenesis. With the ability of surviving without a functional circulation at larval stages, strong genetic similarity between zebrafish and mammals, prolific reproduction and optically transparent embryos, zebrafish is powerful in modeling mammalian cardiac physiology and pathology as well as in large-scale high throughput screening. However, an economical and convenient tool for rapid evaluation of fish cardiac function is still in need. There have been several image analysis methods to assess cardiac functions in zebrafish embryos/larvae, but they are still improvable to reduce manual intervention in the entire process. This work developed a fully automatic method to calculate heart rate, an important parameter to analyze cardiac function, from videos. It contains several filters to identify the heart region, to reduce video noise and to calculate heart rates. RESULTS: The proposed method was evaluated with 32 zebrafish larval cardiac videos that were recording at three-day post-fertilization. The heart rate measured by the proposed method was comparable to that determined by manual counting. The experimental results show that the proposed method does not lose accuracy while largely reducing the labor cost and uncertainty of manual counting. CONCLUSIONS: With the proposed method, researchers do not have to manually select a region of interest before analyzing videos. Moreover, filters designed to reduce video noise can alleviate background fluctuations during the video recording stage (e.g. shifting), which makes recorders generate usable videos easily and therefore reduce manual efforts while recording.