Comparison of multiple modalities for drug response prediction with learning curves using neural networks and XGBoost.

Motivation: Anti-cancer drug response prediction is a central problem within stratified medicine. Transcriptomic profiles of cancer cell lines are typically used for drug response prediction, but we hypothesize that proteomics or phosphoproteomics might be more suitable as they give a more direct insight into cellular processes. However, there has not yet been a systematic comparison between all three of these datatypes using consistent evaluation criteria.

Automated and reproducible cell identification in mass cytometry using neural networks.

The principal use of mass cytometry is to identify distinct cell types and changes in their composition, phenotype and function in different samples and conditions. Combining data from different studies has the potential to increase the power of these discoveries in diverse fields such as immunology, oncology and infection. However, current tools are lacking in scalable, reproducible and automated methods to integrate and study data sets from mass cytometry that often use heterogenous approaches to study similar samples.

Community detection in empirical kinase networks identifies new potential members of signalling pathways.

Phosphoproteomics allows one to measure the activity of kinases that drive the fluxes of signal transduction pathways involved in biological processes such as immune function, senescence and cell growth. However, deriving knowledge of signalling network circuitry from these data is challenging due to a scarcity of phosphorylation sites that define kinase-kinase relationships. To address this issue, we previously identified around 6,000 phosphorylation sites as markers of kinase-kinase relationships (that may be conceptualised as network edges), from which empirical cell-model-specific weighted kinase networks may be reconstructed.

Deriving Meaningful Aspects of Health Related to Physical Activity in Chronic Disease: Concept Elicitation Using Machine Learning-Assisted Coding of Online Patient Conversations.

Objectives: Clinical outcome assessment (COA) developers must ensure that measures assess aspects of health that are meaningful to the target patient population. Although the methodology for doing this is well understood for certain COAs, such as patient-reported outcome measures, there are fewer examples of this practice in the development of digital endpoints using mobile sensor technology such as physical activity monitors. This study explored the utility of social media data, specifically, posts on online health boards, in understanding meaningful aspects of health related to physical activity in 3 different chronic diseases: fibromyalgia, chronic obstructive pulmonary disease, and chronic heart failure.

Single-cell phenotypes of peripheral blood immune cells in early and late stages of non-alcoholic fatty liver disease.

Background/aims: Immune and inflammatory cells respond to multiple pathological hits in the development of nonalcoholic steatohepatitis (NASH) and fibrosis. Relatively little is known about how their type and function change through the non-alcoholic fatty liver disease (NAFLD) spectrum. Here we used multi-dimensional mass cytometry and a tailored bioinformatic approach to study circulating immune cells sampled from healthy individuals and people with NAFLD.