ORCID Profile
0000-0002-8246-7151
Current Organisation
Queensland University of Technology (QUT)
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Springer Science and Business Media LLC
Date: 2003
Publisher: World Scientific Pub Co Pte Lt
Date: 10-2006
DOI: 10.1142/S0129065706000767
Abstract: Identifying promoters is the key to understanding gene expression in bacteria. Promoters lie in tightly constrained positions relative to the transcription start site (TSS). In this paper, we address the problem of predicting transcription start sites in Escherichia coli. Knowing the TSS position, one can then predict the promoter position to within a few base pairs, and vice versa. The accepted method for promoter prediction is to use a pair of position weight matrices (PWMs), which define conserved motifs at the sigma-factor binding site. However this method is known to result in a large number of false positive predictions, thereby limiting its usefulness to the experimental biologist. We adopt an alternative approach based on the Support Vector Machine (SVM) using a modified mismatch spectrum kernel. Our modifications involve tagging the motifs with their location, and selectively pruning the feature set. We quantify the performance of several SVM models and a PWM model using a performance metric of area under the detection-error tradeoff (DET) curve. SVM models are shown to outperform the PWM on a biologically realistic TSS prediction task. We also describe a more broadly applicable peak scoring technique which reduces the number of false positive predictions, greatly enhancing the utility of our results.
Publisher: Elsevier BV
Date: 10-2008
DOI: 10.1016/J.COMPBIOLCHEM.2008.07.009
Abstract: Due to degeneracy of the observed binding sites, the in silico prediction of bacterial sigma(70)-like promoters remains a challenging problem. A large number of sigma(70)-like promoters has been biologically identified in only two species, Escherichia coli and Bacillus subtilis. In this paper we investigate the issues that arise when searching for promoters in other species using an ensemble of SVM classifiers trained on E. coli promoters. DNA sequences are represented using a tagged mismatch string kernel. The major benefit of our approach is that it does not require a prior definition of the typical -35 and -10 hexamers. This gives the SVM classifiers the freedom to discover other features relevant to the prediction of promoters. We use our approach to predict sigma(A) promoters in B. subtilis and sigma(66) promoters in Chlamydia trachomatis. We extended the analysis to identify specific regulatory features of gene sets in C. trachomatis having different expression profiles. We found a strong -35 hexamer and TGN/-10 associated with a set of early expressed genes. Our analysis highlights the advantage of using TSS-PREDICT as a starting point for predicting promoters in species where few are known.
Publisher: ACM
Date: 23-06-2015
Publisher: Springer Berlin Heidelberg
Date: 1999
Publisher: Wiley
Date: 10-07-2021
Abstract: Fauna surveys are traditionally manual, and hence limited in scale, expensive and labour‐intensive. Low‐cost hardware and storage mean that acoustic recording now has the potential to efficiently build scale in terrestrial fauna surveys, both spatially and temporally. With this aim, we have constructed the Australian Acoustic Observatory. It provides a direct and permanent record of terrestrial soundscapes through continuous recording across Australian ecoregions, including those periodically subject to fire and flood, when manual surveys are dangerous or impossible. The observatory comprises 360 permanent listening stations deployed across Australia. Groups of four sensors are deployed at each of 90 sites, placed strategically across ecoregions, to provide representative datasets of soundscapes. Each station continuously records sound, resulting in year‐round data collection. All data are made freely available under an open access licence. The Australian Acoustic Observatory is the world's first terrestrial acoustic observatory of this size. It provides continental‐scale environmental monitoring of unparalleled spatial extent, temporal resolution and archival stability. It enables new approaches to understanding ecosystems, long‐term environmental change, data visualization and acoustic science that will only increase in scientific value over time, particularly as others replicate the design in other parts of the world.
Publisher: Wiley
Date: 2001
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 03-1999
DOI: 10.1111/J.1572-0241.1999.00861.X
Abstract: Our aim was to characterize autonomic dysfunction in patients with irritable bowel syndrome (IBS) using heart rate variability (HRV) studies. EKG signals were obtained from 35 patients (mean age, 39.1 +/- 9.5 yr, M:F ratio = 2.9:1) and 18 healthy controls (mean age, 38.2 +/- 6.5 yr, M:F ratio = 2:1) in supine, standing, and deep-breathing modes. Fast Fourier transformation and autoregressive techniques were used to analyze the HRV power spectra in very low (VLF, 0.0078-0.04 Hz), low (LF, 0.04-0.14 Hz), and high (HF, 0.14-0.4 Hz) frequency bands. In the supine position, the VLF power spectral density (PSD) in IBS was significantly higher than normal (3 vs 1.3 beats per minute [bpm]2/Hz, p < 0.01). On changing from the supine to standing position, the normals (NC) had raised median PSDs in the VLF (1.3 vs 12.8 bpm2/Hz, p < 0.01) and LF (1.6 vs 6.1 bpm2/Hz, p < 0.01) bands, as a sign of increased sympathetic tone, whereas the median HF PSDs (parasympathetic tone) remained unchanged (1.8 bpm2/Hz each, p = 0.8). Similarly, the IBS patients had increased VLF (3.04 vs 14.93 bpm2/Hz, p < 0.01) and LF (2.8 vs 8.7 bpm2/Hz, p < 0.01) PSDs on standing up, but the HF PSD was also raised (from 2.4 to 5.7 bpm2/Hz, p = 0.04). On changing from standing to the deep-breathing mode, the normals had a significant increase in the HF (from 1.8 to 10.3 bpm2/Hz, p < 0.001) and a significant reduction of the VLF (from 12.8 to 2.2 bpm2/Hz, p < 0.01) PSDs. The reduction of the LF PSD was not significant (from 6.1 to 5.6 bpm2/Hz, p = 0.6). In IBS, HF PSD remained constant (5.7 bpm2/Hz each, p = 0.6), whereas the LF PSD increased from 8.7 to 24.2 bpm2/Hz (p < 0.0001). The VLF PSD was reduced (from 14.9 to 4.1 bpm2/Hz, p < 0.0001). In IBS, the median sympathovagal outflow ratio was significantly lower in the standing position (1.4 vs 2.8, p < 0.02) and higher in the deep-breathing mode (7.33 vs 0.42, p < 0.0001) than normal. IBS patients have reduced sympathetic influence on the heart period in response to orthostatic stress and diminished parasympathetic modulation during deep breathing.
Publisher: Elsevier BV
Date: 2014
Publisher: Oxford University Press (OUP)
Date: 15-11-2006
DOI: 10.1093/BIOINFORMATICS/BTI771
Abstract: Motivation: Identifying bacterial promoters is an important step towards understanding gene regulation. In this paper, we address the problem of predicting the location of promoters and their transcription start sites (TSSs) in Escherichia coli. The accepted method for this problem is to use position weight matrices (PWMs), which define conserved motifs at the sigma-factor binding site. However this method is known to result in large numbers of false positive predictions. Results: Our approaches to TSS prediction are based upon an ensemble of support vector machines (SVMs) employing a variant of the mismatch string kernel. This classifier is subsequently combined with a PWM and a model based on distribution of distances from TSS to gene start. We investigate the effect of different scoring techniques and quantify performance using area under a detection-error tradeoff curve. When tested on a biologically realistic task, our method provides performance comparable with or superior to the best reported for this task. False positives are significantly reduced, an improvement of great significance to biologists. Availability: The trained ensemble-SVM model with instructions on usage can be downloaded from Contact: m.towsey@qut.edu.au
Publisher: IOP Publishing
Date: 18-12-2013
Publisher: Informa UK Limited
Date: 06-2012
Publisher: IEEE
Date: 05-2008
Publisher: Wiley
Date: 27-06-2014
DOI: 10.1111/JAV.00411
Publisher: No publisher found
Date: 2005
DOI: 10.1007/11508069\_58
Publisher: Wiley
Date: 16-05-2013
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Wiley
Date: 09-2013
DOI: 10.1890/12-2088.1
Abstract: Acoustic sensors can be used to estimate species richness for vocal species such as birds. They can continuously and passively record large volumes of data over extended periods. These data must subsequently be analyzed to detect the presence of vocal species. Automated analysis of acoustic data for large numbers of species is complex and can be subject to high levels of false positive and false negative results. Manual analysis by experienced surveyors can produce accurate results however the time and effort required to process even small volumes of data can make manual analysis prohibitive. This study examined the use of s ling methods to reduce the cost of analyzing large volumes of acoustic sensor data, while retaining high levels of species detection accuracy. Utilizing five days of manually analyzed acoustic sensor data from four sites, we examined a range of s ling frequencies and methods including random, stratified, and biologically informed. We found that randomly selecting 120 one-minute s les from the three hours immediately following dawn over five days of recordings, detected the highest number of species. On average, this method detected 62% of total species from 120 one-minute s les, compared to 34% of total species detected from traditional area search methods. Our results demonstrate that targeted s ling methods can provide an effective means for analyzing large volumes of acoustic sensor data efficiently and accurately. Development of automated and semi-automated techniques is required to assist in analyzing large volumes of acoustic sensor data.
Publisher: IEEE
Date: 04-2015
Publisher: IEEE
Date: 04-2015
Publisher: IEEE
Date: 12-2010
Publisher: Elsevier BV
Date: 09-2015
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11508069_58
Publisher: Elsevier BV
Date: 05-2014
Publisher: No publisher found
Date: 2008
Publisher: IGI Global
Date: 2010
Publisher: IGI Global
Date: 2010
DOI: 10.4018/978-1-61520-769-5.CH008
Abstract: The impact of urban development and climate change has created the impetus to monitor changes in the environment, particularly, the behaviour, habitat and movement of fauna species. The aim of this chapter is to present the design and development of a sensor network based on Smartphones to automatically collect and analyse acoustic and visual data for environmental monitoring purposes. Due to the communication and sophisticated programming facilities offered by Smartphones, software tools can be developed to allow data to be collected, partially processed and sent to a remote server over the network for storage and further processing. This sensor network which employs a client-server architecture has been deployed in three applications: monitoring a rare bird species near Brisbane Airport, study of koalas behaviour at St Bees Island, and detection of fruit flies. The users of this system include scientists (e.g. ecologists, ornithologists, computer scientists) and community groups participating in data collection or reporting on the environment (e.g. students, bird watchers). The chapter focuses on the following aspects of our research: issues involved in using Smartphones as sensors the overall framework for data acquisition, data quality control, data management and analysis current and future applications of the Smartphone-based sensor network, and our future research directions.
Publisher: IEEE
Date: 08-2015
Publisher: No publisher found
Date: 1999
Publisher: Elsevier BV
Date: 2015
Publisher: Elsevier BV
Date: 1995
Publisher: Georg Thieme Verlag KG
Date: 10-1995
Publisher: World Scientific Pub Co Pte Lt
Date: 06-2012
DOI: 10.1142/S0219519411004794
Abstract: Heart rate is a non-stationary signal and provides a powerful interplay between the sympathetic and parasympathetic nervous systems. The heart rate variation signal can reveal disorders associated with how these nervous systems regulate the heart rate, and hence may contain indicators of this disease state, or warnings about impending or future cardiac diseases. These indicators may be present at all times or may occur at random during certain intervals in the time scale. It is difficult and time consuming to pinpoint these abnormalities in a huge cardiac data set. Heart rate variability (HRV) constitutes a tool for assessing the activities of the autonomic nervous system (ANS). In this work, we have proposed a computer based analytical system to determine the HRV, and analyzed it to obtain HRV Power-spectrum for normal, diabetes and diabetes with neuropathy subjects in deep breathing, standing and supine position. We have then designated indices based on the HRV power-spectra power values and frequency shift of these peaks from their normal frequency values. We have shown the efficacy and sensitivity of these indices, to differentiate between normals, diabetics and diabetics with ischemic heart disease. Thus we have demonstrated how effectively these HRV power-spectral indices can enable diagnosis of diabetic autonomic neuropathy. Finally, we have composed an integrated index made up of these power-spectral indices, to facilitate distinguishing and diagnosing diabetic autonomic neuropathy in terms of just one index or number.
Publisher: Springer Science and Business Media LLC
Date: 05-2001
DOI: 10.1007/PL00011665
Publisher: Elsevier BV
Date: 05-2014
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: IEEE
Date: 12-2013
DOI: 10.1109/CSE.2013.146
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-1994
DOI: 10.1109/51.310986
Publisher: IEEE
Date: 12-2014
Publisher: IEEE
Date: 12-2008
Publisher: Elsevier BV
Date: 02-2013
No related grants have been discovered for Michael Towsey.