_applied_  
_sciences_  
_Editorial_  
**Special Issue: Deep Learning and Neuro-Evolution Methods in Biomedicine and Bioinformatics**  
**[PERSON]**  
NOVA Information Management School (NOVA INS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal; [EMAIL_ADDRESS]  
Emerging technologies in biomedicine and bioinformatics are generating an increasing amount of complex and heterogeneous data. To tackle the growing complexity associated with emerging and future life science challenges, bioinformatics, and computational biology researchers need to explore, develop, and apply novel computational concepts, methods, tools, and systems. Recent years have seen the rise of deep learning (DL). Thanks to the advances in terms of hardware, algorithms, and availability of data, DL has been used successfully to address complex problems that would have been impossible to address a few decades ago. Nonetheless, the revolution brought by DL techniques is just in its early infancy, with new contributions and new ideas constantly being proposed and published. A related approach comes from the field of neuro-evolution, the use of evolutionary algorithms to optimize DL architectures. Neuro-evolution has the potential to achieve better performance with respect to DL-based models, considering that it can optimize the whole architecture, its hyperparameters, and the learning algorithm. This editorial summarizes the research papers published in the context of the Special Issue (SI) \"Deep Learning and Neuro-Evolution Methods in Biomedicine and Bioinformatics\". This Special Issue was led by [PERSON] from Universidade NOVA de Lisboa (Portugal). This SI aimed at collecting original research articles that focus on the development and application of new DL architectures for addressing complex problems in the fields of biomedicine and bioinformatics. Five contributions have been received and published in the SI. In the first contribution \"Classification of Photoplethysmographic Signal Quality with Deep Convolution Neural Networks for Accurate Measurement of Cardiac Stroke Volume\" [1], the authors propose the use of two-dimensional deep convolution neural networks (DCNN) to classify the quality of photoplethysmographic (PPG) signals when the PPG pulse is used to measure cardiac stroke volume (SV) by impedance cardiography. In particular, an image derived from a PPG pulse and its differential pulse is used as the input to the two DCNN models. The experimental results, obtained by analyzing 3135 PPG pulses, suggest that the proposed DCNN may be applied for the classification of PPG quality and help improve the SV measurement in impedance cardiography. The second paper, \"How Deeply to Fine-Tune a Convolutional Neural Network: A Case Study Using a Histopathology Dataset\" [2], deals with a relevant problem related to the fine-tuning of convolutional neural networks (CNNs) in the medical domain. In more detail, while CNNs can improve the image classification domain by eliminating the need to manually select which features to use to classify images, training CNNs from scratch requires very large annotated datasets that are scarce in the medical field. Transfer learning of CNN weights from another large non-medical dataset can help overcome the problem of medical image scarcity. The main questions when using transfer learning are how deeply to fine-tune the network and what difference in generalization that will make. In this study, the authors considered two histopathology datasets and three state-of-the-art architectures to systematically study the effect of block-wise fine-tuning of CNN. Results show that fine-tuning the entire network is not always the best option; especially for shallow networks,alternatively fine-tuning the top blocks can save both time and computational power and produce more robust classifiers.