ORCID Profile
0000-0003-2868-9420
Current Organisations
University of New South Wales - Randwick Campus
,
Macquarie University
,
University of Queensland
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Publisher: Springer Science and Business Media LLC
Date: 23-01-2020
DOI: 10.1038/S41467-020-14286-0
Abstract: Chromosome arm aneuploidies (CAAs) are pervasive in cancers. However, how they affect cancer development, prognosis and treatment remains largely unknown. Here, we analyse CAA profiles of 23,427 tumours, identifying aspects of tumour evolution including probable orders in which CAAs occur and CAAs predicting tissue-specific metastasis. Both haematological and solid cancers initially gain chromosome arms, while only solid cancers subsequently preferentially lose multiple arms. 72 CAAs and 88 synergistically co-occurring CAA pairs multivariately predict good or poor survival for 58% of 6977 patients, with negligible impact of whole-genome doubling. Additionally, machine learning identifies 31 CAAs that robustly alter response to 56 chemotherapeutic drugs across cell lines representing 17 cancer types. We also uncover 1024 potential synthetic lethal pharmacogenomic interactions. Notably, in predicting drug response, CAAs substantially outperform mutations and focal deletions/ lifications combined. Thus, CAAs predict cancer prognosis, shape tumour evolution, metastasis and drug response, and may advance precision oncology.
Publisher: Springer Science and Business Media LLC
Date: 24-11-2022
Publisher: Cambridge University Press (CUP)
Date: 09-2021
DOI: 10.1017/APR.2021.1
Abstract: Coupling-from-the-past (CFTP) methods have been used to generate perfect s les from finite Gibbs hard-sphere models, an important class of spatial point processes consisting of a set of spheres with the centers on a bounded region that are distributed as a homogeneous Poisson point process (PPP) conditioned so that spheres do not overlap with each other. We propose an alternative importance-s ling-based rejection methodology for the perfect s ling of these models. We analyze the asymptotic expected running time complexity of the proposed method when the intensity of the reference PPP increases to infinity while the (expected) sphere radius decreases to zero at varying rates. We further compare the performance of the proposed method analytically and numerically with that of a naive rejection algorithm and of popular dominated CFTP algorithms. Our analysis relies upon identifying large deviations decay rates of the non-overlapping probability of spheres whose centers are distributed as a homogeneous PPP.
Publisher: Association for Computing Machinery (ACM)
Date: 16-09-2015
Abstract: We consider spatial marked Poisson arrivals in a Polish space. These arrivals are accepted or lost in a general state dependent manner. The accepted arrivals remain in the system for a random amount of time, where the in idual sojourn times are i.i.d. For such systems, we develop semi-closed form expressions for the steady state probabilities that can be seen to be insensitive to the sojourn time distribution, and that rely essentially on the static probabilities of marked Poisson objects meeting the state acceptance criteria. The latter observation is then exploited to yield straightforward exact simulation algorithms to s le from the steady state distribution. In addition, for the special case where the arrivals are spheres in a Euclidean space that are lost whenever they overlap with an existing sphere, we develop large deviations asymptotics for the probability of observing a large number of spheres in the system in steady state, under erse asymptotic regimes. Applications include modeling interference in wireless networks and connectivity in ad-hoc networks.
Publisher: Association for Computing Machinery (ACM)
Date: 20-03-2018
Abstract: We consider the problem of generating perfect s les from a Gibbs point process, a spatial process that is absolutely continuous w.r.t. a Poisson point process. Ex les include area-interaction processes, hard-sphere models and Strauss processes. Traditionally, this is addressed using coupling from the past (CFTP) based methods. We consider acceptance-rejection methods that, unlike the common CFTP methods, do not have the impatient-user bias. Our key contribution is a novel importance s ling based acceptance- rejection methodology for generating perfect s les from Gibbs point processes. We focus on a simpler setting of hard-sphere models in a d-dimensional hypercube that we analyze in an asymptotic regime where the number of spheres generated increases to infinity while the sphere radius decreases to zero at varying rates.
Publisher: MDPI AG
Date: 28-09-2022
DOI: 10.3390/A15100354
Abstract: Variance-component models are an indispensable tool for statisticians wanting to capture both random and fixed model effects. They have applications in a wide range of scientific disciplines. While maximum likelihood estimation (MLE) is the most popular method for estimating the variance-component model parameters, it is numerically challenging for large data sets. In this article, we consider the class of coordinate descent (CD) algorithms for computing the MLE. We show that a basic implementation of coordinate descent is numerically costly to implement and does not easily satisfy the standard theoretical conditions for convergence. We instead propose two parameter-expanded versions of CD, called PX-CD and PXI-CD. These novel algorithms not only converge faster than existing competitors (MM and EM algorithms) but are also more amenable to convergence analysis. PX-CD and PXI-CD are particularly well-suited for large data sets—namely, as the scale of the model increases, the performance gap between the parameter-expanded CD algorithms and the current competitor methods increases.
Publisher: IEEE
Date: 12-2019
Publisher: Springer Science and Business Media LLC
Date: 04-05-2021
DOI: 10.1007/S11009-021-09857-7
Abstract: This work introduces and compares approaches for estimating rare-event probabilities related to the number of edges in the random geometric graph on a Poisson point process. In the one-dimensional setting, we derive closed-form expressions for a variety of conditional probabilities related to the number of edges in the random geometric graph and develop conditional Monte Carlo algorithms for estimating rare-event probabilities on this basis. We prove rigorously a reduction in variance when compared to the crude Monte Carlo estimators and illustrate the magnitude of the improvements in a simulation study. In higher dimensions, we use conditional Monte Carlo to remove the fluctuations in the estimator coming from the randomness in the Poisson number of nodes. Finally, building on conceptual insights from large-deviations theory, we illustrate that importance s ling using a Gibbsian point process can further substantially reduce the estimation variance.
Publisher: IEEE
Date: 12-2013
Publisher: Bernoulli Society for Mathematical Statistics and Probability
Date: 08-2020
DOI: 10.3150/19-BEJ1184
Publisher: Association for Computing Machinery (ACM)
Date: 08-05-2015
DOI: 10.1145/2699717
Abstract: Multiclass open queueing networks find wide applications in communication, computer, and fabrication networks. Steady-state performance measures associated with these networks is often a topic of interset. Conceptually, under mild conditions, a sequence of regeneration times exists in multiclass networks, making them amenable to regenerative simulation for estimating steady-state performance measures. However, typically, identification of such a sequence in these networks is difficult. A well-known exception is when all interarrival times are exponentially distributed, where the instants corresponding to customer arrivals to an empty network constitute a sequence of regeneration times. In this article, we consider networks in which the interarrival times are generally distributed but have exponential or heavier tails. We show that these distributions can be decomposed into a mixture of sums of independent random variables such that at least one of the components is exponentially distributed. This allows an easily implementable embedded sequence of regeneration times in the underlying Markov process. We show that among all such interarrival time decompositions, the one with an exponential component that has the largest mean minimizes the asymptotic variance of the standard deviation estimator. We also show that under mild conditions on the network primitives, the regenerative mean and standard deviation estimators are consistent and satisfy a joint central limit theorem useful for constructing asymptotically valid confidence intervals.
Publisher: Elsevier BV
Date: 03-2021
Publisher: IEEE
Date: 11-2019
Publisher: Springer International Publishing
Date: 2021
No related grants have been discovered for Sarat Babu Moka.