Now showing 1 - 10 of 55
  • Publication
    Using Patient Information for the Prediction of Caregiver Burden in Amyotrophic Lateral Sclerosis
    The aim of this study is to create a Clinical Decision Support System (CDSS) to assist in the early identification and support of caregivers at risk of experiencing burden while caring for a person with Amyotrophic Lateral Sclerosis. We work towards a system that uses a minimum amount of data that could be routinely collected. We investigated if the impairment of patients alone provides sufficient information for the prediction of caregiver burden. Results reveal a better performance of our system in identifying those at risk of high burden, but more information is needed for an accurate CDSS.
  • Publication
    Prediction of caregiver burden in amyotrophic lateral sclerosis: a machine learning approach using random forests applied to a cohort study
    OBJECTIVES:Amyotrophic lateral sclerosis (ALS) is a rare neurodegenerative disease that is characterised by the rapid degeneration of upper and lower motor neurons and has a fatal trajectory 3-4 years from symptom onset. Due to the nature of the condition patients with ALS require the assistance of informal caregivers whose task is demanding and can lead to high feelings of burden. This study aims to predict caregiver burden and identify related features using machine learning techniques. DESIGN:This included demographic and socioeconomic information, quality of life, anxiety and depression questionnaires, for patients and carers, resource use of patients and clinical information. The method used for prediction was the Random forest algorithm. SETTING AND PARTICIPANTS:This study investigates a cohort of 90 patients and their primary caregiver at three different time-points. The patients were attending the National ALS/Motor Neuron Disease Multidisciplinary Clinic at Beaumont Hospital, Dublin. RESULTS:The caregiver's quality of life and psychological distress were the most predictive features of burden (0.92 sensitivity and 0.78 specificity). The most predictive features for Clinical Decision Support model were associated with the weekly caregiving duties of the primary caregiver as well as their age and health and also the patient's physical functioning and age of onset. However, this model had a lower sensitivity and specificity score (0.84 and 0.72, respectively). The ability of patients without gastrostomy to cut food and handle utensils was also highly predictive of burden in this study. Generally, our models are better in predicting the high-risk category, and we suggest that information related to the caregiver's quality of life and psychological distress is required. CONCLUSION:This work demonstrates a proof of concept of an informatics solution to identifying caregivers at risk of burden that could be incorporated into future care pathways.
      98Scopus© Citations 10
  • Publication
    Protein Backbone Angle Prediction in Multidimensional φ-ψ Space
    (University College Dublin. School of Computer Science and Informatics, 2006-01-20) ; ;
    A significant step towards establishing the structure and function of a protein is the prediction of the local conformation of the polypeptide chain. In this article we present systems for the prediction of 3 new alphabets of local structural motifs. The motifs are built by applying multidimensional scaling (MDS) and clustering to pair-wise angular distances for multiple φ-ψ angle values collected from high-resolution protein structures. The predictive systems, based on ensembles of bidirectional recurrent neural network architectures, and trained on a large non-redundant set of protein structures, achieve 72%, 66% and 60% correct structural motif prediction on an independent test set for di-peptides (6 classes), tripeptides (8 classes) and tetra-peptides (14 classes), respectively, 28-30% above base-line statistical predictors. To demonstrate that structural motif predictions contain relevant structural information, we build a further system, based on ensembles of two-layered bidirectional recurrent neural networks, to map structural motif predictions into traditional 3-class (helix, strand, coil) secondary structure. This system achieves 79.5% correct prediction using the “hard” CASP 3-class assignment, and 81.4% with a more lenient assignment, outperforming a sophisticated state-of-the-art predictor (Porter) trained in the same experimental conditions. All the predictive systems will be provided free of charge to academic users and made publicly available at the address
  • Publication
    De-repression of myelin-regulating gene expression after status epilepticus in mice lacking the C/EBP homologous protein CHOP
    The C/EBP homologous protein CHOP is normally present at low levels in cells but increases rapidly after insults such as DNA damage or endoplasmatic reticulum stress where it contributes to cellular homeostasis and apoptosis. By forming heterodimers with other transcription factors, CHOP can either act as a dominant-negative regulator of gene expression or to induce the expression of target genes. Recent work demonstrated that seizure-induced hippocampal damage is significantly worse in mice lacking CHOP and these animals go on to develop an aggravated epileptic phenotype. To identify novel CHOP-controlled target genes which potentially influence the epileptic phenotype, we performed a bioinformatics analysis of tissue microarrays from chop-deficient mice after prolonged seizures. GO analysis revealed genes associated with biological membranes were prominent among those in the chop-deficient array dataset and we identified myelin-associated genes to be particularly de-repressed. These data suggest CHOP might act as an inhibitor of myelin-associated processes in the brain and could be targeted to influence axonal regeneration or reorganisation.
  • Publication
    Categorizing Compiler Error Messages with Principal Component Analysis
    Being a competent programmer is critical for students in all computing disciplines and software engineering in particular. Novice programming students face a number of challenges and these have been shown to contribute to worrying dropout rates for students majoring in computing, and the growing number of non-majors who are learning to program. Methods of identifying and helping at-risk programming students have been researched for decades. Much of this research focuses on categorizing the errors that novice programmers make, in order to help understand why these errors are made, with the goal of helping them overcome these errors quickly, or avoid them altogether. This paper presents the first known work on categorizing compiler errors using Principal Component Analysis. In this, we find a new way of discovering categories of related errors from data produced by the students in the course of their programming activity. This method may be used to identify where these students are struggling and provide direction in efforts to help them.
  • Publication
    Multi-level Attention-Based Neural Networks for Distant Supervised Relation Extraction
    We propose a multi-level attention-based neural network forrelation extraction based on the work of Lin et al. to alleviate the problemof wrong labelling in distant supervision. In this paper, we first adoptgated recurrent units to represent the semantic information. Then, weintroduce a customized multi-level attention mechanism, which is expectedto reduce the weights of noisy words and sentences. Experimentalresults on a real-world dataset show that our model achieves significantimprovement on relation extraction tasks compared to both traditionalfeature-based models and existing neural network-based methods
  • Publication
    Potential utility of docking to identify protein-peptide binding regions
    (University College Dublin. School of Computer Science and Informatics, 2013-05) ; ; ; ;
    Disordered regions of proteins often bind to structured domains, mediating interactions within and between proteins. However, it is difficult to identify a priori the short regions involved in binding. We set out to determine if docking peptides to peptide binding domains would assist in these predictions. First, we investigated the docking of known short peptides to their native and non-native peptide binding domains. We then investigated the docking of overlapping peptides adjacent to the native peptide. We found only weak discrimination of docking scores between native peptide and adjacent peptides in this context with similar results for both ordered and disordered regions. Finally, we trained a bidirectional recurrent neural network using as input the peptide sequence, predicted secondary structure, Vina docking score and Pepsite score.We conclude that docking has only modest power to define the location of a peptide within a larger protein region known to contain it. However, this information can be used in training machine learning methods which may allow for the identification of peptide binding regions within a protein sequence.
  • Publication
    Prediction of pathological response to neo‐adjuvant chemoradiotherapy for oesophageal cancer using vibrational spectroscopy
    In oesophageal cancer (OC) neo‐adjuvant chemoradiotherapy (neoCRT) is used to debulk tumour size prior to surgery, with a complete pathological response (pCR) observed in approximately ∼30% of patients. Presently no predictive quantitative methodology exists which can predict response, in particular a pCR or major response (MR), in patients prior to therapy. Raman and Fourier transform infrared imaging were performed on OC tissue specimens acquired from 50 patients prior to therapy, to develop a computational model linking spectral data to treatment outcome. Modelling sensitivities and specificities above 85% were achieved using this approach. Parallel in‐vitro studies using an isogenic model of radioresistant OC supplied further insight into OC cell spectral response to ionising radiation where a potential spectral biomarker of radioresistance was observed at 977 cm−1. This work demonstrates that chemical imaging may provide an option for triage of patients prior to neoCRT treatment allowing more precise prescription of treatment.
  • Publication
    miRNA-Mediated Regulation of Adult Hippocampal Neurogenesis; Implications for Epilepsy
    Hippocampal neural stem/progenitor cells (NSPCs) proliferate and differentiate to generate new neurons across the life span of most mammals, including humans. This process takes place within a characteristic local microenvironment where NSPCs interact with a variety of other cell types and encounter systemic regulatory factors. Within this microenvironment, cell intrinsic gene expression programs are modulated by cell extrinsic signals through complex interactions, in many cases involving short non-coding RNA molecules, such as miRNAs. Here we review the regulation of gene expression in NSPCs by miRNAs and its possible implications for epilepsy, which has been linked to alterations in adult hippocampal neurogenesis.
  • Publication
    Prediction of quality of life in people with ALS: on the road towards explainable clinical decision support
    Amyotrophic Lateral Sclerosis (ALS) is a rare neurodegenerative disease that causes a rapid decline in motor functions and has a fatal trajectory. ALS is currently incurable, so the aim of the treatment is mostly to alleviate symptoms and improve quality of life (QoL) for the patients. The goal of this study is to develop a Clinical Decision Support System (CDSS) to alert clinicians when a patient is at risk of experiencing low QoL. The source of data was the Irish ALS Registry and interviews with the 90 patients and their primary informal caregiver at three time-points. In this dataset, there were two different scores to measure a person's overall QoL, based on the McGill QoL (MQoL) Questionnaire and we worked towards the prediction of both. We used Extreme Gradient Boosting (XGBoost) for the development of the predictive models, which was compared to a logistic regression baseline model. Additionally, we used Synthetic Minority Over-sampling Technique (SMOTE) to examine if that would increase model performance and SHAP (SHapley Additive explanations) as a technique to provide local and global explanations to the outputs as well as to select the most important features. The total calculated MQoL score was predicted accurately using three features - age at disease onset, ALSFRS-R score for orthopnoea and the caregiver's status pre-caregiving - with a F1-score on the test set equal to 0.81, recall of 0.78, and precision of 0.84. The addition of two extra features (caregiver's age and the ALSFRS-R score for speech) produced similar outcomes (F1-score 0.79, recall 0.70 and precision 0.90).