72,047 research outputs found
Fingerprint Policy Optimisation for Robust Reinforcement Learning
Policy gradient methods ignore the potential value of adjusting environment
variables: unobservable state features that are randomly determined by the
environment in a physical setting, but are controllable in a simulator. This
can lead to slow learning, or convergence to suboptimal policies, if the
environment variable has a large impact on the transition dynamics. In this
paper, we present fingerprint policy optimisation (FPO), which finds a policy
that is optimal in expectation across the distribution of environment
variables. The central idea is to use Bayesian optimisation (BO) to actively
select the distribution of the environment variable that maximises the
improvement generated by each iteration of the policy gradient method. To make
this BO practical, we contribute two easy-to-compute low-dimensional
fingerprints of the current policy. Our experiments show that FPO can
efficiently learn policies that are robust to significant rare events, which
are unlikely to be observable under random sampling, but are key to learning
good policies.Comment: ICML 201
K+A Galaxies as the Aftermath of Gas-Rich Mergers: Simulating the Evolution of Galaxies as Seen by Spectroscopic Surveys
Models of poststarburst (or "K+A") galaxies are constructed by combining
fully three-dimensional hydrodynamic simulations of galaxy mergers with
radiative transfer calculations of dust attenuation. Spectral line catalogs are
generated automatically from moderate-resolution optical spectra calculated as
a function of merger progress in each of a large suite of simulations. The
mass, gas fraction, orbital parameters, and mass ratio of the merging galaxies
are varied systematically, showing that the lifetime and properties of the K+A
phase are strong functions of merger scenario. K+A durations are generally less
than ~0.1-0.3 Gyr, significantly shorter than the commonly assumed 1 Gyr, which
is obtained only in rare cases, owing to a wide variation in star formation
histories resulting from different orbital and progenitor configurations.
Combined with empirical merger rates, the model lifetimes predict
rapidly-rising K+A fractions as a function of redshift that are consistent with
results of large spectroscopic surveys, resolving tension between the observed
K+A abundance and that predicted when one assumes the K+A duration is the
lifetime of A stars (~1 Gyr). The effects of dust attenuation, viewing angle,
and aperture bias on our models are analyzed. In some cases, the K+A features
are longer-lived and more pronounced when AGN feedback removes dust from the
center, uncovering the young stars formed during the burst. In this picture,
the K+A phase begins during or shortly after the bright starburst/AGN phase in
violent mergers, and thus offers a unique opportunity to study the effects of
quasar and star formation feedback on the gas reservoir and evolution of the
remnant. Analytic fitting formulae are provided for the estimates of K+A
incidence as a function of merger scenario.Comment: 26 pages, 13 figures; ApJ; minor changes to reflect accepted versio
Gene Expression based Survival Prediction for Cancer Patients: A Topic Modeling Approach
Cancer is one of the leading cause of death, worldwide. Many believe that
genomic data will enable us to better predict the survival time of these
patients, which will lead to better, more personalized treatment options and
patient care. As standard survival prediction models have a hard time coping
with the high-dimensionality of such gene expression (GE) data, many projects
use some dimensionality reduction techniques to overcome this hurdle. We
introduce a novel methodology, inspired by topic modeling from the natural
language domain, to derive expressive features from the high-dimensional GE
data. There, a document is represented as a mixture over a relatively small
number of topics, where each topic corresponds to a distribution over the
words; here, to accommodate the heterogeneity of a patient's cancer, we
represent each patient (~document) as a mixture over cancer-topics, where each
cancer-topic is a mixture over GE values (~words). This required some
extensions to the standard LDA model eg: to accommodate the "real-valued"
expression values - leading to our novel "discretized" Latent Dirichlet
Allocation (dLDA) procedure. We initially focus on the METABRIC dataset, which
describes breast cancer patients using the r=49,576 GE values, from
microarrays. Our results show that our approach provides survival estimates
that are more accurate than standard models, in terms of the standard
Concordance measure. We then validate this approach by running it on the
Pan-kidney (KIPAN) dataset, over r=15,529 GE values - here using the mRNAseq
modality - and find that it again achieves excellent results. In both cases, we
also show that the resulting model is calibrated, using the recent
"D-calibrated" measure. These successes, in two different cancer types and
expression modalities, demonstrates the generality, and the effectiveness, of
this approach
INTEGRATIVE ANALYSIS OF OMICS DATA IN ADULT GLIOMA AND OTHER TCGA CANCERS TO GUIDE PRECISION MEDICINE
Transcriptomic profiling and gene expression signatures have been widely applied as effective approaches for enhancing the molecular classification, diagnosis, prognosis or prediction of therapeutic response towards personalized therapy for cancer patients. Thanks to modern genome-wide profiling technology, scientists are able to build engines leveraging massive genomic variations and integrating with clinical data to identify “at risk” individuals for the sake of prevention, diagnosis and therapeutic interventions. In my graduate work for my Ph.D. thesis, I have investigated genomic sequencing data mining to comprehensively characterise molecular classifications and aberrant genomic events associated with clinical prognosis and treatment response, through applying high-dimensional omics genomic data to promote the understanding of gene signatures and somatic molecular alterations contributing to cancer progression and clinical outcomes. Following this motivation, my dissertation has been focused on the following three topics in translational genomics.
1) Characterization of transcriptomic plasticity and its association with the tumor microenvironment in glioblastoma (GBM). I have integrated transcriptomic, genomic, protein and clinical data to increase the accuracy of GBM classification, and identify the association between the GBM mesenchymal subtype and reduced tumorpurity, accompanied with increased presence of tumor-associated microglia. Then I have tackled the sole source of microglial as intrinsic tumor bulk but not their corresponding neurosphere cells through both transcriptional and protein level analysis using a panel of sphere-forming glioma cultures and their parent GBM samples.FurthermoreI have demonstrated my hypothesis through longitudinal analysis of paired primary and recurrent GBM samples that the phenotypic alterations of GBM subtypes are not due to intrinsic proneural-to-mesenchymal transition in tumor cells, rather it is intertwined with increased level of microglia upon disease recurrence. Collectively I have elucidated the critical role of tumor microenvironment (Microglia and macrophages from central nervous system) contributing to the intra-tumor heterogeneity and accurate classification of GBM patients based on transcriptomic profiling, which will not only significantly impact on clinical perspective but also pave the way for preclinical cancer research.
2) Identification of prognostic gene signatures that stratify adult diffuse glioma patientsharboring1p/19q co-deletions. I have compared multiple statistical methods and derived a gene signature significantly associated with survival by applying a machine learning algorithm. Then I have identified inflammatory response and acetylation activity that associated with malignant progression of 1p/19q co-deleted glioma. In addition, I showed this signature translates to other types of adult diffuse glioma, suggesting its universality in the pathobiology of other subset gliomas. My efforts on integrative data analysis of this highly curated data set usingoptimizedstatistical models will reflect the pending update to WHO classification system oftumorsin the central nervous system (CNS).
3) Comprehensive characterization of somatic fusion transcripts in Pan-Cancers. I have identified a panel of novel fusion transcripts across all of TCGA cancer types through transcriptomic profiling. Then I have predicted fusion proteins with kinase activity and hub function of pathway network based on the annotation of genetically mobile domains and functional domain architectures. I have evaluated a panel of in -frame gene fusions as potential driver mutations based on network fusion centrality hypothesis. I have also characterised the emerging complexity of genetic architecture in fusion transcripts through integrating genomic structure and somatic variants and delineating the distinct genomic patterns of fusion events across different cancer types. Overall my exploration of the pathogenetic impact and clinical relevance of candidate gene fusions have provided fundamental insights into the management of a subset of cancer patients by predicting the oncogenic signalling and specific drug targets encoded by these fusion genes.
Taken together, the translational genomic research I have conducted during my Ph.D. study will shed new light on precision medicine and contribute to the cancer research community. The novel classification concept, gene signature and fusion transcripts I have identified will address several hotly debated issues in translational genomics, such as complex interactions between tumor bulks and their adjacent microenvironments, prognostic markers for clinical diagnostics and personalized therapy, distinct patterns of genomic structure alterations and oncogenic events in different cancer types, therefore facilitating our understanding of genomic alterations and moving us towards the development of precision medicine
Enhancing Compressed Sensing 4D Photoacoustic Tomography by Simultaneous Motion Estimation
A crucial limitation of current high-resolution 3D photoacoustic tomography
(PAT) devices that employ sequential scanning is their long acquisition time.
In previous work, we demonstrated how to use compressed sensing techniques to
improve upon this: images with good spatial resolution and contrast can be
obtained from suitably sub-sampled PAT data acquired by novel acoustic scanning
systems if sparsity-constrained image reconstruction techniques such as total
variation regularization are used. Now, we show how a further increase of image
quality can be achieved for imaging dynamic processes in living tissue (4D
PAT). The key idea is to exploit the additional temporal redundancy of the data
by coupling the previously used spatial image reconstruction models with
sparsity-constrained motion estimation models. While simulated data from a
two-dimensional numerical phantom will be used to illustrate the main
properties of this recently developed
joint-image-reconstruction-and-motion-estimation framework, measured data from
a dynamic experimental phantom will also be used to demonstrate their potential
for challenging, large-scale, real-world, three-dimensional scenarios. The
latter only becomes feasible if a carefully designed combination of tailored
optimization schemes is employed, which we describe and examine in more detail
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