School of Mathematics and Statistics - Research Publications

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    A Bayesian method for comparing and combining binary classifiers in the absence of a gold standard.
    Keith, JM ; Davey, CM ; Boyd, SE (Springer Science and Business Media LLC, 2012-07-27)
    BACKGROUND: Many problems in bioinformatics involve classification based on features such as sequence, structure or morphology. Given multiple classifiers, two crucial questions arise: how does their performance compare, and how can they best be combined to produce a better classifier? A classifier can be evaluated in terms of sensitivity and specificity using benchmark, or gold standard, data, that is, data for which the true classification is known. However, a gold standard is not always available. Here we demonstrate that a Bayesian model for comparing medical diagnostics without a gold standard can be successfully applied in the bioinformatics domain, to genomic scale data sets. We present a new implementation, which unlike previous implementations is applicable to any number of classifiers. We apply this model, for the first time, to the problem of finding the globally optimal logical combination of classifiers. RESULTS: We compared three classifiers of protein subcellular localisation, and evaluated our estimates of sensitivity and specificity against estimates obtained using a gold standard. The method overestimated sensitivity and specificity with only a small discrepancy, and correctly ranked the classifiers. Diagnostic tests for swine flu were then compared on a small data set. Lastly, classifiers for a genome-wide association study of macular degeneration with 541094 SNPs were analysed. In all cases, run times were feasible, and results precise. The optimal logical combination of classifiers was also determined for all three data sets. Code and data are available from http://bioinformatics.monash.edu.au/downloads/. CONCLUSIONS: The examples demonstrate the methods are suitable for both small and large data sets, applicable to the wide range of bioinformatics classification problems, and robust to dependence between classifiers. In all three test cases, the globally optimal logical combination of the classifiers was found to be their union, according to three out of four ranking criteria. We propose as a general rule of thumb that the union of classifiers will be close to optimal.
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    Psychosocial Well-Being and Functional Outcomes in Youth With Type 1 Diabetes 12 years After Disease Onset
    Northam, EA ; Lin, A ; Finch, S ; Weather, GA ; Cameron, FJ (AMER DIABETES ASSOC, 2010-07-01)
    OBJECTIVE: Type 1 diabetes in youth and community controls were compared on functional outcomes. Relationships were examined between psychosocial variables at diagnosis and functional outcome 12 years later. RESEARCH DESIGN AND METHODS: Participants were subjects with type 1 diabetes (n = 110, mean age 20.7 years, SD 4.3) and control subjects (n = 76, mean age 20.8 years, SD 4.0). The measures used included the Youth Self-Report and Young Adult Self-Report and a semi-structured interview of functional outcomes. Type 1 diabetes participants also provided information about current diabetes care and metabolic control from diagnosis. RESULTS: Type 1 diabetes participants and control subjects reported similar levels of current well-being but for the youth with type 1 diabetes, the mental health referral rates over the previous 12 years were higher by 19% and school completion rates were lower by 17%. Over one-third of clinical participants were not currently receiving specialist care and this group had higher mental health service usage in the past (61 vs. 33%) and lower current psychosocial well- being. Within the type 1 diabetes group, behavior problems, high activity, and low family cohesion at diagnosis predicted lower current well-being, but were not associated with metabolic control history. Poorer metabolic control was associated with higher mental health service usage. CONCLUSIONS: Type 1 diabetes participants report similar levels of current psychosocial well-being compared with control subjects, but higher levels of psychiatric morbidity since diagnosis and lower school completion rates. Psychiatric morbidity was associated with poor metabolic control and failure to transition to tertiary adult diabetes care.
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    Experimental and analytical evaluation of Incremental Sheet Hydro-Forming strategies to produce high forming angle sheets.
    Kumar, Y ; Kumar, S (Elsevier BV, 2019-06)
    Incremental Sheet Hydro-Forming (ISHF) is a hybrid process of Incremental Sheet Forming (ISF) and Sheet Hydro-Forming (SHF). In the ISHF process, a single ball tool moves over one side of the surface of the sheet and hydraulic support is provided in another by using the pressurized hydraulic fluid. In the current research, an attempt has been made to achieve high forming angles using ISHF. The forming strategy, multi-stage & multi-step (MSMS), has been proposed to improve the formability in ISHF. The MSMS has resulted in the improvement in the formability and forming angle achieved is 78.75 o . The primary issue, identified in MSMS forming strategy, is the failure of the product due to thinning of the sheet. To address the failure of the sheet due to thinning, a modified version of MSMS was proposed. This modified version of MSMS has shown tremendous improvement in the formability of the ISHF. The forming angle upto 90 o has been successfully achieved using the modified version of MSMS. Analytical models have been developed for MSMS forming strategy and for the modified version of MSMS forming strategy. The experimental results are closely the same as predicted by analytical models.
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    Evaluating stably expressed genes in single cells
    Lin, Y ; Ghazanfar, S ; Strbenac, D ; Wang, A ; Patrick, E ; Lin, DM ; Speed, T ; Yang, JYH ; Yang, P (OXFORD UNIV PRESS, 2019-09-01)
    BACKGROUND: Single-cell RNA-seq (scRNA-seq) profiling has revealed remarkable variation in transcription, suggesting that expression of many genes at the single-cell level is intrinsically stochastic and noisy. Yet, on the cell population level, a subset of genes traditionally referred to as housekeeping genes (HKGs) are found to be stably expressed in different cell and tissue types. It is therefore critical to question whether stably expressed genes (SEGs) can be identified on the single-cell level, and if so, how can their expression stability be assessed? We have previously proposed a computational framework for ranking expression stability of genes in single cells for scRNA-seq data normalization and integration. In this study, we perform detailed evaluation and characterization of SEGs derived from this framework. RESULTS: Here, we show that gene expression stability indices derived from the early human and mouse development scRNA-seq datasets and the "Mouse Atlas" dataset are reproducible and conserved across species. We demonstrate that SEGs identified from single cells based on their stability indices are considerably more stable than HKGs defined previously from cell populations across diverse biological systems. Our analyses indicate that SEGs are inherently more stable at the single-cell level and their characteristics reminiscent of HKGs, suggesting their potential role in sustaining essential functions in individual cells. CONCLUSIONS: SEGs identified in this study have immediate utility both for understanding variation and stability of single-cell transcriptomes and for practical applications such as scRNA-seq data normalization. Our framework for calculating gene stability index, "scSEGIndex," is incorporated into the scMerge Bioconductor R package (https://sydneybiox.github.io/scMerge/reference/scSEGIndex.html) and can be used for identifying genes with stable expression in scRNA-seq datasets.
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    Accurate RNA Sequencing From Formalin-Fixed Cancer Tissue to Represent High-Quality Transcriptome From Frozen Tissue
    Li, J ; Fu, C ; Speed, TP ; Wang, W ; Symmans, WF (AMER SOC CLINICAL ONCOLOGY, 2018-01-26)
    PURPOSE: Accurate transcriptional sequencing (RNA-seq) from formalin-fixation and paraffin-embedding (FFPE) tumor samples presents an important challenge for translational research and diagnostic development. In addition, there are now several different protocols to prepare a sequencing library from total RNA. We evaluated the accuracy of RNA-seq data generated from FFPE samples in terms of expression profiling. METHODS: We designed a biospecimen study to directly compare gene expression results from different protocols to prepare libraries for RNA-seq from human breast cancer tissues, with randomization to fresh-frozen (FF) or FFPE conditions. The protocols were compared using multiple computational methods to assess alignment of reads to reference genome, and the uniformity and continuity of coverage; as well as the variance and correlation, of overall gene expression and patterns of measuring coding sequence, phenotypic patterns of gene expression, and measurements from representative multigene signatures. RESULTS: The principal determinant of variance in gene expression was use of exon capture probes, followed by the conditions of preservation (FF versus FFPE), and phenotypic differences between breast cancers. One protocol, with RNase H-based rRNA depletion, exhibited least variability of gene expression measurements, strongest correlation between FF and FFPE samples, and was generally representative of the transcriptome from standard FF RNA-seq protocols. CONCLUSION: Method of RNA-seq library preparation from FFPE samples had marked effect on the accuracy of gene expression measurement compared to matched FF samples. Nevertheless, some protocols produced highly concordant expression data from FFPE RNA-seq data, compared to RNA-seq results from matched frozen samples.
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    Anatomy of a seasonal influenza epidemic forecast
    Moss, R ; Zarebski, AE ; Dawson, P ; Franklin, LJ ; Birrell, FA ; McCaw, JM (Department of Health, Australian Government, 2019-03-15)
    Bayesian methods have been used to predict the timing of infectious disease epidemics in various settings and for many infectious diseases, including seasonal influenza. But integrating these techniques into public health practice remains an ongoing challenge, and requires close collaboration between modellers, epidemiologists, and public health staff. During the 2016 and 2017 Australian influenza seasons, weekly seasonal influenza forecasts were produced for cities in the three states with the largest populations: Victoria, New South Wales and Queensland. Forecast results were presented to Health Department disease surveillance units in these jurisdictions, who provided feedback about the plausibility and public health utility of these predictions. In earlier studies we found that delays in reporting and processing of surveillance data substantially limited forecast performance, and that incorporating climatic effects on transmission improved forecast performance. In this study of the 2016 and 2017 seasons, we sought to refine the forecasting method to account for delays in receiving the data, and used meteorological data from past years to modulate the force of infection. We demonstrate how these refinements improved the forecast’s predictive capacity, and use the 2017 influenza season to highlight challenges in accounting for population and clinician behaviour changes in response to a severe season.
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    Charge Has a Marked Influence on Hyperbranched Polymer Nanoparticle Association in Whole Human Blood
    Glass, JJ ; Chen, L ; Alcantara, S ; Crampin, EJ ; Thurecht, KJ ; De Rose, R ; Kent, SJ (AMER CHEMICAL SOC, 2017-06-01)
    In this study, we synthesize charge-varied hyperbranched polymers (HBPs) and demonstrate surface charge as a key parameter directing their association with specific human blood cell types. Using fresh human blood, we investigate the association of 5 nm HBPs with six white blood cell populations in their natural milieu by flow cytometry. While most cell types associate with cationic HBPs at 4 °C, at 37 °C phagocytic cells display similar (monocyte, dendritic cell) or greater (granulocyte) association with anionic HBPs compared to cationic HBPs. Neutral HBPs display remarkable stealth properties. Notably, these charge-association patterns are not solely defined by the plasma protein corona and are material and/or size dependent. As HBPs progress toward clinical use as imaging and drug delivery agents, the ability to engineer HBPs with defined biological properties is increasingly important. This knowledge can be used in the rational design of HBPs for more effective delivery to desired cell targets.
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    Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.
    Flannick, J ; Fuchsberger, C ; Mahajan, A ; Teslovich, TM ; Agarwala, V ; Gaulton, KJ ; Caulkins, L ; Koesterer, R ; Ma, C ; Moutsianas, L ; McCarthy, DJ ; Rivas, MA ; Perry, JRB ; Sim, X ; Blackwell, TW ; Robertson, NR ; Rayner, NW ; Cingolani, P ; Locke, AE ; Tajes, JF ; Highland, HM ; Dupuis, J ; Chines, PS ; Lindgren, CM ; Hartl, C ; Jackson, AU ; Chen, H ; Huyghe, JR ; van de Bunt, M ; Pearson, RD ; Kumar, A ; Mueller-Nurasyid, M ; Grarup, N ; Stringham, HM ; Gamazon, ER ; Lee, J ; Chen, Y ; Scott, RA ; Below, JE ; Chen, P ; Huang, J ; Go, MJ ; Stitzel, ML ; Pasko, D ; Parker, SCJ ; Varga, TV ; Green, T ; Beer, NL ; Day-Williams, AG ; Ferreira, T ; Fingerlin, T ; Horikoshi, M ; Hu, C ; Huh, I ; Ikram, MK ; Kim, B-J ; Kim, Y ; Kim, YJ ; Kwon, M-S ; Lee, J ; Lee, S ; Lin, K-H ; Maxwell, TJ ; Nagai, Y ; Wang, X ; Welch, RP ; Yoon, J ; Zhang, W ; Barzilai, N ; Voight, BF ; Han, B-G ; Jenkinson, CP ; Kuulasmaa, T ; Kuusisto, J ; Manning, A ; Ng, MCY ; Palmer, ND ; Balkau, B ; Stancakova, A ; Abboud, HE ; Boeing, H ; Giedraitis, V ; Prabhakaran, D ; Gottesman, O ; Scott, J ; Carey, J ; Kwan, P ; Grant, G ; Smith, JD ; Neale, BM ; Purcell, S ; Butterworth, AS ; Howson, JMM ; Lee, HM ; Lu, Y ; Kwak, S-H ; Zhao, W ; Danesh, J ; Lam, VKL ; Park, KS ; Saleheen, D ; So, WY ; Tam, CHT ; Afzal, U ; Aguilar, D ; Arya, R ; Aung, T ; Chan, E ; Navarro, C ; Cheng, C-Y ; Palli, D ; Correa, A ; Curran, JE ; Rybin, D ; Farook, VS ; Fowler, SP ; Freedman, BI ; Griswold, M ; Hale, DE ; Hicks, PJ ; Khor, C-C ; Kumar, S ; Lehne, B ; Thuillier, D ; Lim, WY ; Liu, J ; Loh, M ; Musani, SK ; Puppala, S ; Scott, WR ; Yengo, L ; Tan, S-T ; Taylor, HA ; Thameem, F ; Wilson, G ; Wong, TY ; Njolstad, PR ; Levy, JC ; Mangino, M ; Bonnycastle, LL ; Schwarzmayr, T ; Fadista, J ; Surdulescu, GL ; Herder, C ; Groves, CJ ; Wieland, T ; Bork-Jensen, J ; Brandslund, I ; Christensen, C ; Koistinen, HA ; Doney, ASF ; Kinnunen, L ; Esko, T ; Farmer, AJ ; Hakaste, L ; Hodgkiss, D ; Kravic, J ; Lyssenko, V ; Hollensted, M ; Jorgensen, ME ; Jorgensen, T ; Ladenvall, C ; Justesen, JM ; Karajamaki, A ; Kriebel, J ; Rathmann, W ; Lannfelt, L ; Lauritzen, T ; Narisu, N ; Linneberg, A ; Melander, O ; Milani, L ; Neville, M ; Orho-Melander, M ; Qi, L ; Qi, Q ; Roden, M ; Rolandsson, O ; Swift, A ; Rosengren, AH ; Stirrups, K ; Wood, AR ; Mihailov, E ; Blancher, C ; Carneiro, MO ; Maguire, J ; Poplin, R ; Shakir, K ; Fennell, T ; DePristo, M ; de Angelis, MH ; Deloukas, P ; Gjesing, AP ; Jun, G ; Nilsson, PM ; Murphy, J ; Onofrio, R ; Thorand, B ; Hansen, T ; Meisinger, C ; Hu, FB ; Isomaa, B ; Karpe, F ; Liang, L ; Peters, A ; Huth, C ; O'Rahilly, SP ; Palmer, CNA ; Pedersen, O ; Rauramaa, R ; Tuomilehto, J ; Salomaa, V ; Watanabe, RM ; Syvanen, A-C ; Bergman, RN ; Bharadwaj, D ; Bottinger, EP ; Cho, YS ; Chandak, GR ; Chan, JC ; Chia, KS ; Daly, MJ ; Ebrahim, SB ; Langenberg, C ; Elliott, P ; Jablonski, KA ; Lehman, DM ; Jia, W ; Ma, RCW ; Pollin, TI ; Sandhu, M ; Tandon, N ; Froguel, P ; Barroso, I ; Teo, YY ; Zeggini, E ; Loos, RJF ; Small, KS ; Ried, JS ; DeFronzo, RA ; Grallert, H ; Glaser, B ; Metspalu, A ; Wareham, NJ ; Walker, M ; Banks, E ; Gieger, C ; Ingelsson, E ; Im, HK ; Illig, T ; Franks, PW ; Buck, G ; Trakalo, J ; Buck, D ; Prokopenko, I ; Magi, R ; Lind, L ; Farjoun, Y ; Owen, KR ; Gloyn, AL ; Strauch, K ; Tuomi, T ; Kooner, JS ; Lee, J-Y ; Park, T ; Donnelly, P ; Morris, AD ; Hattersley, AT ; Bowden, DW ; Collins, FS ; Atzmon, G ; Chambers, JC ; Spector, TD ; Laakso, M ; Strom, TM ; Bell, GI ; Blangero, J ; Duggirala, R ; Tai, E ; McVean, G ; Hanis, CL ; Wilson, JG ; Seielstad, M ; Frayling, TM ; Meigs, JB ; Cox, NJ ; Sladek, R ; Lander, ES ; Gabriel, S ; Mohlke, KL ; Meitinger, T ; Groop, L ; Abecasis, G ; Scott, LJ ; Morris, AP ; Kang, HM ; Altshuler, D ; Burtt, NP ; Florez, JC ; Boehnke, M ; McCarthy, MI (NATURE PUBLISHING GROUP, 2018-01-23)
    This corrects the article DOI: 10.1038/sdata.2017.179.
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    An exponential filter model predicts lightness illusions
    Zeman, A ; Brooks, KR ; Ghebreab, S (FRONTIERS MEDIA SA, 2015-06-24)
    Lightness, or perceived reflectance of a surface, is influenced by surrounding context. This is demonstrated by the Simultaneous Contrast Illusion (SCI), where a gray patch is perceived lighter against a black background and vice versa. Conversely, assimilation is where the lightness of the target patch moves toward that of the bounding areas and can be demonstrated in White's effect. Blakeslee and McCourt (1999) introduced an oriented difference-of-Gaussian (ODOG) model that is able to account for both contrast and assimilation in a number of lightness illusions and that has been subsequently improved using localized normalization techniques. We introduce a model inspired by image statistics that is based on a family of exponential filters, with kernels spanning across multiple sizes and shapes. We include an optional second stage of normalization based on contrast gain control. Our model was tested on a well-known set of lightness illusions that have previously been used to evaluate ODOG and its variants, and model lightness values were compared with typical human data. We investigate whether predictive success depends on filters of a particular size or shape and whether pooling information across filters can improve performance. The best single filter correctly predicted the direction of lightness effects for 21 out of 27 illusions. Combining two filters together increased the best performance to 23, with asymptotic performance at 24 for an arbitrarily large combination of filter outputs. While normalization improved prediction magnitudes, it only slightly improved overall scores in direction predictions. The prediction performance of 24 out of 27 illusions equals that of the best performing ODOG variant, with greater parsimony. Our model shows that V1-style orientation-selectivity is not necessary to account for lightness illusions and that a low-level model based on image statistics is able to account for a wide range of both contrast and assimilation effects.
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    Complex cells decrease errors for the Muller-Lyer illusion in a model of the visual ventral stream
    Zeman, A ; Obst, O ; Brooks, KR (FRONTIERS RESEARCH FOUNDATION, 2014-09-24)
    To improve robustness in object recognition, many artificial visual systems imitate the way in which the human visual cortex encodes object information as a hierarchical set of features. These systems are usually evaluated in terms of their ability to accurately categorize well-defined, unambiguous objects and scenes. In the real world, however, not all objects and scenes are presented clearly, with well-defined labels and interpretations. Visual illusions demonstrate a disparity between perception and objective reality, allowing psychophysicists to methodically manipulate stimuli and study our interpretation of the environment. One prominent effect, the Müller-Lyer illusion, is demonstrated when the perceived length of a line is contracted (or expanded) by the addition of arrowheads (or arrow-tails) to its ends. HMAX, a benchmark object recognition system, consistently produces a bias when classifying Müller-Lyer images. HMAX is a hierarchical, artificial neural network that imitates the "simple" and "complex" cell layers found in the visual ventral stream. In this study, we perform two experiments to explore the Müller-Lyer illusion in HMAX, asking: (1) How do simple vs. complex cell operations within HMAX affect illusory bias and precision? (2) How does varying the position of the figures in the input image affect classification using HMAX? In our first experiment, we assessed classification after traversing each layer of HMAX and found that in general, kernel operations performed by simple cells increase bias and uncertainty while max-pooling operations executed by complex cells decrease bias and uncertainty. In our second experiment, we increased variation in the positions of figures in the input images that reduced bias and uncertainty in HMAX. Our findings suggest that the Müller-Lyer illusion is exacerbated by the vulnerability of simple cell operations to positional fluctuations, but ameliorated by the robustness of complex cell responses to such variance.