Zina M. Ibrahim

Mitochondrial Dysfunction and Immune Activation are Detectable in Early Alzheimer's Disease Blood

Alzheimers disease (AD), like other dementias, is characterized by progressive neuronal loss and neuroinflammation in the brain. The peripheral leukocyte response occurring alongside these brain changes has not been extensively studied, but might inform therapeutic approaches and provide relevant disease biomarkers. Using microarrays, we assessed blood gene expression alterations occurring in people with AD and those with mild cognitive changes at increased risk of developing AD. Of the 2,908 differentially expressed probes identified between the three groups (p < 0.01), a quarter were altered in blood from mild cognitive impairment (MCI) and AD subjects, relative to controls, suggesting a peripheral response to pathology may occur very early. There was strong evidence for mitochondrial dysfunction with decreased expression of many of the respiratory complex I-V genes and subunits of the core mitochondrial ribosome complex. This mirrors changes previously observed in AD brain. A number of genes encoding cell adhesion molecules were increased, along with other immune-related genes. These changes are consistent with leukocyte activation and their increased the transition from circulation into the brain. In addition to expression changes, we also found increased numbers of basophils in people with MCI and AD, and increased monocytes in people with an AD diagnosis. Taken together this study provides both an insight into the functional response of circulating leukocytes during neurodegeneration and also identifies potential targets such as the respiratory chain for designing and monitoring future therapeutic interventions using blood.

Identification of Adverse Drug Events from Free Text Electronic Patient Records and Information in a Large Mental Health Case Register.

Objectives Electronic healthcare records (EHRs) are a rich source of information, with huge potential for secondary research use. The aim of this study was to develop an application to identify instances of Adverse Drug Events (ADEs) from free text psychiatric EHRs. Methods We used the GATE Natural Language Processing (NLP) software to mine instances of ADEs from free text content within the Clinical Record Interactive Search (CRIS) system, a de-identified psychiatric case register developed at the South London and Maudsley NHS Foundation Trust, UK. The tool was built around a set of four movement disorders (extrapyramidal side effects [EPSEs]) related to antipsychotic therapy and rules were then generalised such that the tool could be applied to additional ADEs. We report the frequencies of recorded EPSEs in patients diagnosed with a Severe Mental Illness (SMI) and then report performance in identifying eight other unrelated ADEs. Results The tool identified EPSEs with >0.85 precision and >0.86 recall during testing. Akathisia was found to be the most prevalent EPSE overall and occurred in the Asian ethnic group with a frequency of 8.13%. The tool performed well when applied to most of the non-EPSEs but least well when applied to rare conditions such as myocarditis, a condition that appears frequently in the text as a side effect warning to patients. Conclusions The developed tool allows us to accurately identify instances of a potential ADE from psychiatric EHRs. As such, we were able to study the prevalence of ADEs within subgroups of patients stratified by SMI diagnosis, gender, age and ethnicity. In addition we demonstrated the generalisability of the application to other ADE types by producing a high precision rate on a non-EPSE related set of ADE containing documents. Availability The application can be found at http://git.brc.iop.kcl.ac.uk/rmallah/dystoniaml.

Using Qualitative Probability in Reverse-Engineering Gene Regulatory Networks

This paper demonstrates the use of qualitative probabilistic networks (QPNs) to aid Dynamic Bayesian Networks (DBNs) in the process of learning the structure of gene regulatory networks from microarray gene expression data. We present a study which shows that QPNs define monotonic relations that are capable of identifying regulatory interactions in a manner that is less susceptible to the many sources of uncertainty that surround gene expression data. Moreover, we construct a model that maps the regulatory interactions of genetic networks to QPN constructs and show its capability in providing a set of candidate regulators for target genes, which is subsequently used to establish a prior structure that the DBN learning algorithm can use and which 1) distinguishes spurious correlations from true regulations, 2) enables the discovery of sets of coregulators of target genes, and 3) results in a more efficient construction of gene regulatory networks. The model is compared to the existing literature using the known gene regulatory interactions of Drosophila Melanogaster.

An abstract theory and ontology of motion based on the regions connection calculus

In this paper, we present a framework abstracting motion by creating a qualitative representation of classes describing motion, and use the continuity constraints implicitly embedded in the semantics of these classes to create a framework that enables plausible reasoning about them. In particular, we propose a topology-based calculus of motion whose primitive is a motion class. We subsequently construct a set of primitive motion classes that exhaustively describes the change in topology between two moving objects, and show how compound motion classes are formed from these primitive motion classes using continuity constraints we make explicit. We use composition tables to define queries in the spatio-temporal domain and enable the extension of the classes to reason about the change in topology among three objects as they move.

Spatio-temporal Reasoning for Vague Regions

This paper extends a mereotopological theory of spatiotemporal reasoning to vague ”egg-yolk” regions. In this extension, the egg and its yolk are allowed to move and change over time. We present a classification of motion classes for vague regions as well as composition tables for reasoning about moving vague regions. We also discuss the formation of scrambled eggs when it becomes impossible to distinguish the yolk from the white and examine how to incorporate temporally and spatially dispersed observations to recover the yolk and white from a scrambled egg. Egg splitting may occur as a result of the recovery process when available information supports multiple egg recovery alternatives. Egg splitting adds another dimension of uncertainty to reasoning with vague regions.

Qualitative Motif Detection in Gene Regulatory Networks

This paper motivates the use of Qualitative Probabilistic Networks (QPNs) in conjunction with or in lieu of Bayesian Networks (BNs) for reconstructing gene regulatory networks from microarray expression data. QPNs are qualitative abstractions of Bayesian Networks that replace the conditional probability tables associated with BNs by qualitative influences, which use signs to encode how the values of variables change. We demonstrate that the qualitative influences defined by QPNs exhibit a natural mapping to naturally-occurring patterns of connections, termed network motifs, embedded in Gene Regulatory Networks and present a model that maps QPN constructs to such motifs.The contribution of this paper is that of discovering motifs by mapping their time-series experimental data to QPN influences and using the discovered motifs to aid the process of reconstructing the corresponding gene regulatory network via Dynamic Bayesian Networks (DBNs). The general aim is to compile a model that uses qualitative equivalents of Dynamic Bayesian Networks to explore gene expression networks and their regulatory mechanisms. Although this aim remains under development, the results we have obtained shows success for the discovery of regulatory motifs in Saccharomyces Cerevisiae and their effectiveness in improving the results obtained in terms of reconstruction using DBNs.

A Qualitative Hidden Markov Model for Spatio-temporal Reasoning

We present a Hidden Markov Model that uses qualitative order of magnitude probabilities for its states and transitions. We use the resulting model to construct a formalization of qualitative spatiotemporal events as random processes and utilize it to build high-level natural language description of change. We use the resulting model to show an example of foreseen usage of well-known prediction and recognition techniques used in Hidden Markov Models to perform useful queries with the representation.

ADEPt, a semantically-enriched pipeline for extracting adverse drug events from free-text electronic health records

Adverse drug events (ADEs) are unintended responses to medical treatment. They can greatly affect a patient’s quality of life and present a substantial burden on healthcare. Although Electronic health records (EHRs) document a wealth of information relating to ADEs, they are frequently stored in the unstructured or semi-structured free-text narrative requiring Natural Language Processing (NLP) techniques to mine the relevant information. Here we present a rule-based ADE detection and classification pipeline built and tested on a large Psychiatric corpus comprising 264k patients using the de-identified EHRs of four UK-based psychiatric hospitals. The pipeline uses characteristics specific to Psychiatric EHRs to guide the annotation process, and distinguishes: a) the temporal value associated with the ADE mention (whether it is historical or present), b) the categorical value of the ADE (whether it is assertive, hypothetical, retrospective or a general discussion) and c) the implicit contextual value where the status of the ADE is deduced from surrounding indicators, rather than explicitly stated. We manually created the rulebase in collaboration with clinicians and pharmacists by studying ADE mentions in various types of clinical notes. We evaluated the open-source Adverse Drug Event annotation Pipeline (ADEPt) using 19 ADEs specific to antipsychotics and antidepressants medication. The ADEs chosen vary in severity, regularity and persistence. The average F-measure and accuracy achieved by our tool across all tested ADEs were 0.83 and 0.83 respectively. In addition to annotation power, the ADEPT pipeline presents an improvement to the state of the art context-discerning algorithm, ConText.

Surprise-Based Qualitative Probabilistic Networks

This paper discusses a modification of the kappa measure of surprise and uses it to build semi-qualitative probabilistic networks. The new measure is designed to enable the definition of partial-order relations on its conditional values and is hence used to define qualitative influences over the edges of the network, similarly to Qualitative Probabilistic Networks. The resulting networks combine the advantages of kappa calculus of robustness and ease of assessment and the efficiency of Qualitative Probabilistic Networks. The measure also enables a built-in tradeoff resolution mechanism for the proposed network.

The relative vertex clustering value - a new criterion for the fast discovery of functional modules in protein interaction networks

BackgroundCellular processes are known to be modular and are realized by groups of proteins implicated in common biological functions. Such groups of proteins are called functional modules, and many community detection methods have been devised for their discovery from protein interaction networks (PINs) data. In current agglomerative clustering approaches, vertices with just a very few neighbors are often classified as separate clusters, which does not make sense biologically. Also, a major limitation of agglomerative techniques is that their computational efficiency do not scale well to large PINs. Finally, PIN data obtained from large scale experiments generally contain many false positives, and this makes it hard for agglomerative clustering methods to find the correct clusters, since they are known to be sensitive to noisy data.ResultsWe propose a local similarity premetric, the relative vertex clustering value, as a new criterion allowing to decide when a node can be added to a given nodes cluster and which addresses the above three issues. Based on this criterion, we introduce a novel and very fast agglomerative clustering technique, FAC-PIN, for discovering functional modules and protein complexes from a PIN data.ConclusionsOur proposed FAC-PIN algorithm is applied to nine PIN data from eight different species including the yeast PIN, and the identified functional modules are validated using Gene Ontology (GO) annotations from DAVID Bioinformatics Resources. Identified protein complexes are also validated using experimentally verified complexes. Computational results show that FAC-PIN can discover functional modules or protein complexes from PINs more accurately and more efficiently than HC-PIN and CNM, the current state-of-the-art approaches for clustering PINs in an agglomerative manner.