Graphical models for de novo and pathway-based network prediction over multi-modal high-throughput biological data

It is now a standard practice in the study of complex disease to perform many high-throughput -omic experiments (genome wide SNP, copy number, mRNA and miRNA expression) on the same set of patient samples. These multi-modal data should allow researchers to form a more complete, systems-level picture of a sample, but this is only possible if they have a suitable model for integrating the data. Due to the variety of data modalities and possible combinations of data, general, flexible integration methods that will be widely applicable in many settings are desirable. In this dissertation I will present my work using graphical models for de novo structure learning of both undirected and directed sparse graphs over a mixture of Gaussian and categorical variables. Using synthetic and biological data I will show that these models are useful for both variable selection and inference. Selecting the regularization parameters is an important challenge for these models so I will also cover stability based methods for efficiently setting these parameters, and for controlling the false discovery rate of edge predictions. I will also show results from a biological application to data from metastatic melanoma patients where our methods identified a PARP1 slice site variant that is predictive of response to chemotherapy. Finally, I present work incorporating miRNA into a pathway based graphical model called PARADIGM. This extension of the model allows us to study patient-specific changes in miRNA induced silencing in cancer

Editorial: Addressing community priorities in autism research

Autism is a form of neurodiversity, currently characterized by differences compared to the neurotypical population across multiple domains including sensory processing (Proff et al., 2021), social communication style (Crompton et al., 2021), attentional processing (Murray et al., 2005), and movement and motor processing (Miller et al., 2021). Historically, autism (and thus autistic people) has been studied through a medical lens (Chapman and Carel, 2022), owing primarily to the characterization of autism as a disorder of childhood development. These conceptualizations led to dehumanizing narratives about autistic people (Botha) and have impacted on who we consider to be knowledgeable about what it is like to be autistic (Kourti). In recent years, there has been a shift toward recognition of autism as a form of neurodivergence; a naturally occurring variation in the human population that may lead to a differential profile of strengths and challenges in comparison to the non-autistic population (Den Houting, 2019). This shift has been primarily driven by the autistic self-advocacy and neurodiversity movements (Kapp et al., 2013; Walker, 2021), which have campaigned for better understanding of autistic people

Impact and process evaluation of Forward Thinking Birmingham, the 0-25 Mental Health Service : Final Report

This report provides the findings of a year-long evaluation of Forward Thinking Birmingham (FTB) which started just after the service went live in October 2015. Undertaken by a team from the University of Warwick and the GIFT Partnership, the purpose of the evaluation was to understand how the changes to mental health service provision for children and young people aged 0-25 and their parents and carers outlined in the new FTB model impact on key stakeholders across a range of service settings and types. The aim was to generate learning about the new model as to whether it worked/was achieving its specified objectives, what was perhaps less successful and needed amendment or further development. The evaluation would also provide an opportunity to think about the future development of the service in order to ensure a robust and sustainable model of provision