Deep Learning (DL) has recently enabled unprecedented advances in one of the grand challenges in computational biology: the half-century-old problem of protein structure prediction. In this paper we discuss recent advances, limitations, and future perspectives of DL on five broad areas: protein structure prediction, protein function prediction, genome engineering, systems biology and data integration, and phylogenetic inference. We discuss each application area and cover the main bottlenecks of DL approaches, such as training data, problem scope, and the ability to leverage existing DL architectures in new contexts. To conclude, we provide a summary of the subject-specific and general challenges for DL across the biosciences.

As computational biologists continue to be inundated by ever increasing amounts of metagenomic data, the need for data analysis approaches that keep up with the pace of sequence archives has remained a challenge. In recent years, the accelerated pace of genomic data availability has been accompanied by the application of a wide array of highly efficient approaches from other fields to the field of metagenomics. For instance, sketching algorithms such as MinHash have seen a rapid and widespread adoption. These techniques handle increasingly large datasets with minimal sacrifices in quality for tasks such as sequence similarity calculations. Here, we briefly review the fundamentals of the most impactful probabilistic and signal processing algorithms. We also highlight more recent advances to augment previous reviews in these areas that have taken a broader approach. We then explore the application of these techniques to metagenomics, discuss their pros and cons, and speculate on their future directions.

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Heart valves consist of leaflets that can degrade due to a range of disease processes. To better design prostheses, it is critical to study leaflet mechanics. Although mechanical testing of heart valve leaflets (HVLs) is the standard for evaluating mechanical behavior, imaging and deep learning (DL) networks, such as convolutional neural networks (CNNs), are more readily available and cost-effective. In this work, we determined the influence that a dataset that we curated had on the ability of a CNN to predict the stress-strain response of the leaflets. Our findings indicate that CNNs can be used to predict the polynomial coefficients needed for reconstructing the toe and linear regions of typically observed mechanical behavior, which lie near the physiological strain, 10% strain.