The Partial Order Kernel and its Application to Understanding the Regulatory Grammar of Conserved Non-coding Elements

PhDConserved non-coding elements (CNEs) are regions of non-coding DNA which have remained evolutionarily conserved across various species over millions of years and are found to cluster near genes involved in early embryonic development, suggesting that they play an important role as regulatory elements. Indeed, many CNEs have been shown to act as enhancers; however, not all regulatory elements are conserved and in some cases, deletion of CNEs did not result in any notable phenotypes. These opposing ndings indicate that the functions of CNEs are still poorly understood and further research on these elements is needed to uncover the reasons for their extreme conservation. The aim of this thesis is to investigate the use and development of algorithms for decoding the regulatory grammar of CNEs. Initially, an assessment of several methods for functional classi cation of CNEs is provided. The results obtained using these methods are validated by functional assays and their limitations in capturing the grammar of CNEs are discussed. Motivated by these limitations, a partial order graph representation of the sequence of transcription factor binding sites (TFBSs) in a CNE that allows e cient handling of the overlapping sites is introduced. A dynamic programming-based method for aligning two such graphs and identifying regulatory signatures composed of co-occurring TFBSs is proposed and evaluated. The results demonstrate the predictive ability of this method, which can be used to prioritise regions for experimental validation. Building on this method, the partial order kernel (POKer) for comparison of strings containing alternative substrings and represented by partial order graphs is introduced. The POKer is evaluated in di erent sequence comparison tasks, including visual localisation. An approach using the POKer for functional classi cation of CNEs is introduced and its e ectiveness in capturing the grammar of CNEs is demonstrated. Finally, the implications of the results presented in this work for modelling the evolution of CNEs are discussed

Analysis of mental and physical disorders associated with COVID-19 in online health forums: a natural language processing study

Objectives Online health forums provide rich and untapped real-time data on population health. Through novel data extraction and natural language processing (NLP) techniques, we characterise the evolution of mental and physical health concerns relating to the COVID-19 pandemic among online health forum users. Setting and design We obtained data from three leading online health forums: HealthBoards, Inspire and HealthUnlocked, from the period 1 January 2020 to 31 May 2020. Using NLP, we analysed the content of posts related to COVID-19. Primary outcome measures (1) Proportion of forum posts containing COVID-19 keywords; (2) proportion of forum users making their very first post about COVID-19; (3) proportion of COVID-19-related posts containing content related to physical and mental health comorbidities. Results Data from 739 434 posts created by 53 134 unique users were analysed. A total of 35 581 posts (4.8%) contained a COVID-19 keyword. Posts discussing COVID-19 and related comorbid disorders spiked in early March to mid-March around the time of global implementation of lockdowns prompting a large number of users to post on online health forums for the first time. Over a quarter of COVID-19-related thread titles mentioned a physical or mental health comorbidity. Conclusions We demonstrate that it is feasible to characterise the content of online health forum user posts regarding COVID-19 and measure changes over time. The pandemic and corresponding public response has had a significant impact on posters’ queries regarding mental health. Social media data sources such as online health forums can be harnessed to strengthen population-level mental health surveillance

On the use of skin texture features for gender recognition: An experimental evaluation

© 2016 IEEE.Skin appearance is almost universally the object of gender-related expectations and stereotypes. This not with standing, remarkably little work has been done on establishing quantitatively whether skin texture can be used for gender discrimination. We present a detailed analysis of the skin texture of 43 subjects based on two complementary imaging modalities afforded by a visible-light dermoscope and the recently developed Epsilon sensor for capacitive imaging. We consider an array of established texture features in combination with two supervised classification techniques (1-NN and SVM) and a state-of-the-art unsupervised approach (t-SNE). A statistical analysis of the results suggests that skin microtexture carries very little information on gender

Visual Localization in the Presence of Appearance Changes Using the Partial Order Kernel

Visual localization across seasons and under varying weather and lighting conditions is a challenging task in robotics. In this paper, we present a new sequence-based approach to visual localization using the Partial Order Kernel (POKer), a convolution kernel for string comparison, that is able to handle appearance changes and is robust to speed variations. We use multiple sequence alignment to construct directed acyclic graph representations of the database image sequences, where sequences of images of the same place acquired at different times are represented as alternative paths in a graph. We then use the POKer to compute the pairwise similarities between these graphs and the query image sequences obtained in a subsequent traversal of the environment, and match the corresponding locations. We evaluated our approach on a dataset which features extreme appearance variations due to seasonal changes. The results demonstrate the effectiveness of our approach, where it achieves higher precision and recall than two state-of-the-art baseline method