Cytoscape is one of the most successful network biology analysis and visualization tools, but because of its interactive nature, its role in creating reproducible, scalable, and novel workflows has been limited. We describe Cytoscape Automation (CA), which marries Cytoscape to highly productive workflow systems, for example, Python/R in Jupyter/RStudio. We expose over 270 Cytoscape core functions and 34 Cytoscape apps as REST-callable functions with standardized JSON interfaces backed by Swagger documentation. Independent projects to create and publish Python/R native CA interface libraries have reached an advanced stage, and a number of automation workflows are already published. Electronic supplementary material The online version of this article (10.1186/s13059-019-1758-4) contains supplementary material, which is available to authorized users.

Cytoscape is one of the most popular open‐source software tools for the visual exploration of biomedical networks composed of protein, gene, and other types of interactions. It offers researchers a versatile and interactive visualization interface for exploring complex biological interconnections supported by diverse annotation and experimental data, thereby facilitating research tasks such as predicting gene function and constructing pathways. Cytoscape provides core functionality to load, visualize, search, filter, and save networks, and hundreds of Apps extend this functionality to address specific research needs. The latest generation of Cytoscape (version 3.0 and later) has substantial improvements in function, user interface, and performance relative to previous versions. This protocol aims to jump‐start new users with specific protocols for basic Cytoscape functions, such as installing Cytoscape and Cytoscape Apps, loading data, visualizing and navigating the networks, visualizing network associated data (attributes), and identifying clusters. It also highlights new features that benefit experienced users. Curr. Protoc. Bioinform. 47:8.13.1‐8.13.24. © 2014 by John Wiley & Sons, Inc.

Although artificial neural networks simulate a variety of human functions, their internal structures are hard to interpret. In the life sciences, extensive knowledge of cell biology provides an opportunity to design visible neural networks (VNNs) which couple the model’s inner workings to those of real systems. Here we develop DCell, a VNN embedded in the hierarchical structure of 2526 subsystems comprising a eukaryotic cell (http://d-cell.ucsd.edu/). Trained on several million genotypes, DCell simulates cellular growth nearly as accurately as laboratory observations. During simulation, genotypes induce patterns of subsystem activities, enabling in-silico investigations of the molecular mechanisms underlying genotype-phenotype associations. These mechanisms can be validated and many are unexpected; some are governed by Boolean logic. Cumulatively, 80% of the importance for growth prediction is captured by 484 subsystems (21%), reflecting the emergence of a complex phenotype. DCell provides a foundation for decoding the genetics of disease, drug resistance, and synthetic life.

Summary Networks are a powerful and flexible methodology for expressing biological knowledge for computation and communication. Network-encoded information can include systematic screens for molecular interactions, biological relationships curated from literature, and outputs from analysis of Big Data. NDEx, the Network Data Exchange (www.ndexbio.org), is an online commons where scientists can upload, share, and publicly distribute networks. Networks in NDEx receive globally unique accession IDs and can be stored for private use, shared in pre-publication collaboration, or released for public access. Standard and novel data formats are accommodated in a flexible storage model. Organizations can use NDEx as a distribution channel for networks they generate or curate. Developers of bioinformatic applications can store and query NDEx networks via a common programmatic interface. NDEx helps expand the role of networks in scientific discourse and facilitates the integration of networks as data in publications. It is a step towards an ecosystem in which networks bearing data, hypotheses, and findings flow easily between scientists.

Purpose To assess validity of the Personal Activity Location Measurement System (PALMS) for deriving time spent walking/running, bicycling, and in vehicle, using SenseCam as the comparison. Methods 40 adult cyclists wore a Qstarz BT-Q1000XT GPS data logger and SenseCam (camera worn around neck capturing multiple images every minute) for a mean of 4 days. PALMS used distance and speed between GPS points to classify whether each minute was part of a trip (yes/no), and if so, the trip mode (walking/running, bicycling, in vehicle). SenseCam images were annotated to create the same classifications (i.e., trip yes/no and mode). 2×2 contingency tables and confusion matrices were calculated at the minute-level for PALMS vs. SenseCam classifications. Mixed-effects linear regression models estimated agreement (mean differences and intraclass correlations [ICCs]) between PALMS and SenseCam with regards to minutes/day in each mode. Results Minute-level sensitivity, specificity, and negative predictive value were ≥88%, and positive predictive value was ≥75% for non mode-specific trip detection. 72–80% of outdoor walking/running minutes, 73% of bicycling minutes, and 74–76% of in-vehicle minutes were correctly classified by PALMS. For minutes/day, PALMS had a mean bias (i.e., amount of over or under estimation) of 2.4–3.1 minutes (11–15%) for walking/running, 2.3–2.9 minutes (7–9%) for bicycling, and 4.3–5 minutes (15–17%) for vehicle time. ICCs were ≥.80 for all modes. Conclusions PALMS has validity for processing GPS data to objectively measure time walking/running, bicycling, and in vehicle in population studies. Assessing travel patterns is one of many valuable applications of GPS in physical activity research that can improve our understanding of the determinants and health outcomes of active transportation as well as its impact on physical activity.