ORCID Profile
0000-0002-9632-6878
Current Organisations
The University of Newcastle
,
Memorial University of Newfoundland
,
University of British Columbia
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Publisher: Oxford University Press (OUP)
Date: 24-03-2017
DOI: 10.1093/BIOINFORMATICS/BTX174
Abstract: Cancer is not a single disease and involves different subtypes characterized by different sets of molecules. Patients with different subtypes of cancer often react heterogeneously towards the same treatment. Currently, clinical diagnoses rather than molecular profiles are used to determine the most suitable treatment. A molecular level approach will allow a more precise and informed way for making treatment decisions, leading to a better survival chance and less suffering of patients. Although many computational methods have been proposed to identify cancer subtypes at molecular level, to the best of our knowledge none of them are designed to discover subtypes with heterogeneous treatment responses. In this article we propose the Survival Causal Tree (SCT) method. SCT is designed to discover patient subgroups with heterogeneous treatment effects from censored observational data. Results on TCGA breast invasive carcinoma and glioma datasets have shown that for each subtype identified by SCT, the patients treated with radiotherapy exhibit significantly different relapse free survival pattern when compared to patients without the treatment. With the capability to identify cancer subtypes with heterogeneous treatment responses, SCT is useful in helping to choose the most suitable treatment for in idual patients. Data and code are available at github.com/WeijiaZhang24/SurvivalCausalTree. Supplementary data are available at Bioinformatics online.
Publisher: Elsevier BV
Date: 2018
DOI: 10.2139/SSRN.3188379
Publisher: Informa UK Limited
Date: 17-12-2021
DOI: 10.1080/02770903.2021.2014862
Abstract: This study investigated the association between varying cutoffs for Medication Adherence (MA) among physician-diagnosed asthma patients and subsequent association with asthma exacerbation. We linked four administrative health databases obtained from the Population Data in British Columbia. Index cases were physician-diagnosed asthma patients between January 1, 1998, to December 31, 1999, aged 18 years and older. Patients were prospectively assessed in the follow-up period from January 1, 2000, to December 31, 2018, to identify asthma exacerbation. Two proxy measures were used to assess MA: the proportion of days covered (PDC) and the medication possession ratio (MPR). Using the generalized estimating equation (GEE) logistic regression adjusted for patient covariates, the outcome of "asthma exacerbation" was modeled against varying MA cutoffs The s le included 68,211 physician-diagnosed asthma patients with a mean age of 48.2 years and 59.3% females. The adjusted odds ratios (OR) and 95% confidence interval (CI) at the various cutoff for PDC-levels predicting asthma exacerbation events were: Intervention aimed at improving asthma exacerbation events in adult asthma patients should encourage increased medication adherence threshold level greater than 0.80. Supplemental data for this article is available online at at jas .
Publisher: Cold Spring Harbor Laboratory
Date: 07-05-2019
DOI: 10.1101/611418
Abstract: We define a disease module as a partition of a molecular network whose components are jointly associated with one or several diseases or risk factors thereof. Identification of such modules, across different types of networks, has great potential for elucidating disease mechanisms and establishing new powerful bio-markers. To this end, we launched the “Disease Module Identification (DMI) DREAM Challenge”, a community effort to build and evaluate unsupervised molecular network modularisation algorithms (Choobdar et al ., 2018). Here we present MONET , a toolbox providing easy and unified access to the three top methods from the DMI DREAM Challenge for the bioinformatics community. MONET is a command line tool for Linux, based on Docker and Singularity containers the core algorithms were written in R, Python, Ada and C++. It is freely available for download at github.com/BergmannLab/MONET.git mattia.tomasoni@unil.ch (MT) sven.bergmann@unil.ch (SB)
Publisher: Springer Science and Business Media LLC
Date: 03-2021
Publisher: Oxford University Press (OUP)
Date: 09-04-2020
DOI: 10.1093/BIOINFORMATICS/BTAA236
Abstract: We define a disease module as a partition of a molecular network whose components are jointly associated with one or several diseases or risk factors thereof. Identification of such modules, across different types of networks, has great potential for elucidating disease mechanisms and establishing new powerful biomarkers. To this end, we launched the ‘Disease Module Identification (DMI) DREAM Challenge’, a community effort to build and evaluate unsupervised molecular network modularization algorithms. Here, we present MONET, a toolbox providing easy and unified access to the three top-performing methods from the DMI DREAM Challenge for the bioinformatics community. MONET is a command line tool for Linux, based on Docker and Singularity containers the core algorithms were written in R, Python, Ada and C++. It is freely available for download at github.com/BergmannLab/MONET.git. Supplementary data are available at Bioinformatics online.
Publisher: Cold Spring Harbor Laboratory
Date: 04-03-2022
DOI: 10.1101/2022.03.01.482592
Abstract: Emerging evidence indicates that longer SARS-CoV-2 vaccine dosing intervals results in an enhanced immune response. However, the optimal vaccine dosing interval for achieving maximum immunogenicity is unclear. This study included s les from adult paramedics in Canada who received two doses of either BNT162b2 or mRNA-1273 vaccines and provided blood s les 6 months (170 to 190 days) after the first vaccine dose. The main exposure variable was vaccine dosing interval (days), categorized as “ short ” (first quartile), “ moderate ” (second quartile), “ long ” (third quartile), and “ longest” interval (fourth quartile). The primary outcome was total spike antibody concentrations, measured using the Elecsys SARS-CoV-2 total antibody assay. Secondary outcomes included: spike and RBD IgG antibody concentrations, and inhibition of angiotensin-converting enzyme 2 (ACE-2) binding to wild-type spike protein and several different Delta variant spike proteins. We fit a multiple log-linear regression model to investigate the association between vaccine dosing intervals and the antibody concentrations. A total of 564 adult paramedics (mean age 40 years, SD=10) were included. Compared to “short interval” (≤30 days), higher dosing interval quartiles ( moderate : 31-38 days long : 39-73 days and longest : ≥74 days) were all associated with increased Elescys spike total antibody concentration. Compared to the short interval, “ long ” and “ longest ” interval quartiles were associated with higher spike and RBD IgG antibody concentrations. Similarly, increasing dosing intervals increased inhibition of ACE-2 binding to viral spike protein, regardless of the vaccine type. Increased mRNA vaccine dosing intervals longer than 30 days result in higher levels of circulating antibodies and viral neutralization when assessed at 6 months.
Publisher: Springer Science and Business Media LLC
Date: 30-08-2019
Publisher: Public Library of Science (PLoS)
Date: 11-04-2016
Publisher: Informa UK Limited
Date: 10-01-2022
DOI: 10.1080/15412555.2021.2024159
Abstract: Asthma patients may have an increased risk for diagnosis of chronic obstructive pulmonary disease (COPD). However, risk factors accelerating time-to-COPD diagnosis are unclear. This study aims to estimate risk factors associated with the incidence of COPD diagnosis in asthma patients. Canada's Population Data BC (PopData BC) was used to identify asthma patients without prior COPD diagnosis between January 1, 1998, to December 31, 1999. Patients were assessed for time-to-incidence of COPD diagnosis from January 1, 2000, to December 31, 2018. The study estimated the effects of several risk factors in predicting the incidence of COPD in asthma patients during the 18-year follow-up period. Patient factors such as Medication Adherence (MA) were assessed by the proportion of days covered (PDC) and the medication possession ratio (MPR). The log-logistic mixed-effects accelerated failure time model was used to estimate the adjusted
Publisher: American Society for Microbiology
Date: 23-02-2022
DOI: 10.1128/SPECTRUM.01454-21
Abstract: Among a cohort of adult paramedics in Canada, we investigated the performance of nucleocapsid (N) antibody detection (measured with a V-PLEX assay) to identify previous COVID-19 infections and compared differences among vaccinated and unvaccinated. Our data indicate that vaccinated and unvaccinated groups require different thresholds to achieve optimal test performance, especially for detecting COVID-19 within the preceding 9 months.
Publisher: American Society for Microbiology
Date: 27-04-2022
DOI: 10.1128/SPECTRUM.02702-21
Abstract: The BNT162b2 and mRNA-1273 mRNA SARS-CoV-2 vaccines have demonstrated high efficacy for preventing short-term COVID-19. However, comparative long-term effectiveness is unclear, especially pertaining to the Delta variant.
Publisher: Cold Spring Harbor Laboratory
Date: 06-09-2018
DOI: 10.1101/407148
Abstract: miRBase is the primary repository for published miRNA sequence and annotation data, and serves as the “go-to” place for miRNA research. However, the definition and annotation of miRNAs have been changed significantly across different versions of miRBase. The changes cause inconsistency in miRNA related data between different databases and articles published at different times. Several tools have been developed for different purposes of querying and converting the information of miRNAs between different miRBase versions, but none of them in idually can provide the comprehensive information about miRNAs in miRBase and users will need to use a number of different tools in their analyses. We introduce miRBaseConverter, an R package integrating the latest miRBase version 22 available in Bioconductor to provide a suite of functions for converting and retrieving miRNA name (ID), accession, sequence, species, version and family information in different versions of miRBase. The package is implemented in R and available under the GPL-2 license from the Bioconductor website ( ackages/miRBaseConverter/ ). A Shiny-based GUI suitable for non-R users is also available as a standalone application from the package and also as a web application at nugget.unisa.edu.au:3838/miRBaseConverter . miRBaseConverter has a built-in database for querying miRNA information in all species and for both pre-mature and mature miRNAs defined by miRBase. In addition, it is the first tool for batch querying the miRNA family information. The package aims to provide a comprehensive and easy-to-use tool for miRNA research community where researchers often utilize published miRNA data from different sources. The Bioconductor package miRBaseConverter and the Shiny-based web application are presented to provide a suite of functions for converting and retrieving miRNA name, accession, sequence, species, version and family information in different versions of miRBase. The package will serve a wide range of applications in miRNA research and could provide a full view of the miRNAs of interest.
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 18-05-2021
DOI: 10.1609/AAAI.V35I12.17304
Abstract: Much research has been devoted to the problem of estimating treatment effects from observational data however, most methods assume that the observed variables only contain confounders, i.e., variables that affect both the treatment and the outcome. Unfortunately, this assumption is frequently violated in real-world applications, since some variables only affect the treatment but not the outcome, and vice versa. Moreover, in many cases only the proxy variables of the underlying confounding factors can be observed. In this work, we first show the importance of differentiating confounding factors from instrumental and risk factors for both average and conditional average treatment effect estimation, and then we propose a variational inference approach to simultaneously infer latent factors from the observed variables, disentangle the factors into three disjoint sets corresponding to the instrumental, confounding, and risk factors, and use the disentangled factors for treatment effect estimation. Experimental results demonstrate the effectiveness of the proposed method on a wide range of synthetic, benchmark, and real-world datasets.
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2021
Abstract: Multi-instance learning is a type of weakly supervised learning. It deals with tasks where the data is a set of bags and each bag is a set of instances. Only the bag labels are observed whereas the labels for the instances are unknown. An important advantage of multi-instance learning is that by representing objects as a bag of instances, it is able to preserve the inherent dependencies among parts of the objects. Unfortunately, most existing algorithms assume all instances to be identically and independently distributed, which violates real-world scenarios since the instances within a bag are rarely independent. In this work, we propose the Multi-Instance Variational Autoencoder (MIVAE) algorithm which explicitly models the dependencies among the instances for predicting both bag labels and instance labels. Experimental results on several multi-instance benchmarks and end-to-end medical imaging datasets demonstrate that MIVAE performs better than state-of-the-art algorithms for both instance label and bag label prediction tasks.
Publisher: Cold Spring Harbor Laboratory
Date: 29-05-2018
DOI: 10.1101/333278
Abstract: Estimating heterogeneous treatment effects is an important problem in many medical and biological applications since treatments may have different effects on the prognoses of different patients. Recently, several recursive partitioning methods have been proposed to identify the subgroups that with different responds to a treatment, and they rely on a fitness criterion to minimize the error between the estimated treatment effects and the unobservable true effects. In this paper, we propose that a heterogeneity criterion, which maximizes the differences of treatment effects among the subgroups, also needs to be considered. Moreover, we show that better performances can be achieved when the fitness and the heterogeneous criteria are considered simultaneously. Selecting the optimal splitting points then becomes a multi-objective problem however, a solution that achieves optimal in both aspects are often not available. To solve this problem, we propose a multi-objective splitting procedure to balance both criteria. The proposed procedure is computationally efficient and fits naturally into the existing recursive partitioning framework. Experimental results show that the proposed multi-objective approach performs consistently better than existing ones. The effects of a treatment are often not the same for different in iduals with different gene expressions. Learning to predict the heterogeneous treatment effects from clinical and expression data is an important step towards personalized medical treatment. Existing computational methods are not ideal for the task because they do not address the interpretability of the model and do not consider the limited s le sizes in biological and medical applications. Our method addresses these issues and achieves superior performance in analyzing the treatment effects of radiotherapy on breast cancer patients.
Publisher: Springer Science and Business Media LLC
Date: 12-2018
Publisher: Elsevier BV
Date: 09-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Microbiology Society
Date: 17-10-2023
Publisher: Springer Science and Business Media LLC
Date: 12-2018
Publisher: Oxford University Press (OUP)
Date: 05-10-2022
Abstract: The relationship between antibodies to wild-type severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigens and the risk of breakthrough infections is unclear, especially during circulation of the Omicron strain. We investigated the association of anti-spike and anti-receptor binding domain antibody levels and the risk of subsequent breakthrough coronavirus disease 2019 (COVID-19). We included adult paramedics from an observational cohort study who received ≥ 2 mRNA vaccines but did not have COVID-19 before the blood collection. Higher postvaccination antibody levels to wild-type SARS-CoV-2 antigens were associated with a reduced risk of COVID-19. Further research into clinical utility of antibody levels, to inform a threshold for protection and timing of boosters, should be prioritized.
Publisher: Association for Computing Machinery (ACM)
Date: 04-10-2021
DOI: 10.1145/3466818
Abstract: A central question in many fields of scientific research is to determine how an outcome is affected by an action, i.e., to estimate the causal effect or treatment effect of an action. In recent years, in areas such as personalised healthcare, sociology, and online marketing, a need has emerged to estimate heterogeneous treatment effects with respect to in iduals of different characteristics. To meet this need, two major approaches have been taken: treatment effect heterogeneity modelling and uplifting modelling. Researchers and practitioners in different communities have developed algorithms based on these approaches to estimate the heterogeneous treatment effects. In this article, we present a unified view of these two seemingly disconnected yet closely related approaches under the potential outcome framework. We provide a structured survey of existing methods following either of the two approaches, emphasising their inherent connections and using unified notation to facilitate comparisons. We also review the main applications of the surveyed methods in personalised marketing, personalised medicine, and sociology. Finally, we summarise and discuss the available software packages and source codes in terms of their coverage of different methods and applicability to different datasets, and we provide general guidelines for method selection.
No related grants have been discovered for Michael Asamoah-Boaheng.