Collecting Food and Drink Intake Data With Voice Input: Development, Usability, and Acceptability Study.

BACKGROUND: Voice-based systems such as Amazon Alexa may be useful for collecting self-reported information in real time from participants of epidemiology studies using verbal input. In epidemiological research studies, self-reported data tend to be collected using short, infrequent questionnaires, in which the items require participants to select from predefined options, which may lead to errors in the information collected and lack of coverage. Voice-based systems give the potential to collect self-reported information "continuously" over several days or weeks. At present, to the best of our knowledge, voice-based systems have not been used or evaluated for collecting epidemiological data. OBJECTIVE: We aimed to demonstrate the technical feasibility of using Alexa to collect information from participants, investigate participant acceptability, and provide an initial evaluation of the validity of the collected data. We used food and drink information as an exemplar. METHODS: We recruited 45 staff members and students at the University of Bristol (United Kingdom). Participants were asked to tell Alexa what they ate or drank for 7 days and to also submit this information using a web-based form. Questionnaires asked for basic demographic information, about their experience during the study, and the acceptability of using Alexa. RESULTS: Of the 37 participants with valid data, most (n=30, 81%) were aged 20 to 39 years and 23 (62%) were female. Across 29 participants with Alexa and web entries corresponding to the same intake event, 60.1% (357/588) of Alexa entries contained the same food and drink information as the corresponding web entry. Most participants reported that Alexa interjected, and this was worse when entering the food and drink information (17/35, 49% of participants said this happened often; 1/35, 3% said this happened always) than when entering the event date and time (6/35, 17% of participants said this happened often; 1/35, 3% said this happened always). Most (28/35, 80%) said they would be happy to use a voice-controlled system for future research. CONCLUSIONS: Although there were some issues interacting with the Alexa skill, largely because of its conversational nature and because Alexa interjected if there was a pause in speech, participants were mostly willing to participate in future research studies using Alexa. More studies are needed, especially to trial less conversational interfaces.

Reactome Pengine: a web-logic API to the Homo sapiens reactome.

Summary: Existing ways of accessing data from the Reactome database are limited. Either a researcher is restricted to particular queries defined by a web application programming interface (API) or they have to download the whole database. Reactome Pengine is a web service providing a logic programming-based API to the human reactome. This gives researchers greater flexibility in data access than existing APIs, as users can send their own small programs (alongside queries) to Reactome Pengine. Availability and implementation: The server and an example notebook can be found at https://apps.nms.kcl.ac.uk/reactome-pengine. Source code is available at https://github.com/samwalrus/reactome-pengine and a Docker image is available at https://hub.docker.com/r/samneaves/rp4/. Supplementary information: Supplementary data are available at Bioinformatics online.

Using ILP to Identify Pathway Activation Patterns in Systems Biology.

We show a logical aggregation method that, combined with propositionalization methods, can construct novel structured biological features from gene expression data. We do this to gain understanding of pathway mechanisms, for instance, those associated with a particular disease. We illustrate this method on the task of distinguishing between two types of lung cancer; Squamous Cell Carcinoma (SCC) and Adenocarcinoma (AC). We identify pathway activation patterns in pathways previously implicated in the development of cancers. Our method identified a model with comparable predictive performance to the winning algorithm of a recent challenge, while providing biologically relevant explanations that may be useful to a biologist.