4,113 research outputs found
FOXM1 coming of age: time for translation into clinical benefits?
A decade since the first evidence implicating the cell cycle transcription factor Forkhead Box M1 (FOXM1) in human tumorigenesis, a slew of subsequent studies revealed an oncogenic role of FOXM1 in the majority of human cancers including oral, nasopharynx, oropharynx, esophagus, breast, ovary, prostate, lung, liver, pancreas, kidney, colon, brain, cervix, thyroid, bladder, uterus, testis, stomach, skin, and blood. Its aberrant upregulation in almost all different cancer types suggests a fundamental role for FOXM1 in tumorigenesis. Its dose-dependent expression pattern correlated well with tumor progression starting from cancer predisposition and initiation, early premalignancy and progression, to metastatic invasion. In addition, emerging studies have demonstrated a causal link between FOXM1 and chemotherapeutic drug resistance. Despite the well-established multifaceted roles for FOXM1 in all stages of oncogenesis, its translation into clinical benefit is yet to materialize. In this contribution, I reviewed and discussed how our current knowledge on the oncogenic mechanisms of FOXM1 could be exploited for clinical use as biomarker for risk prediction, early cancer screening, molecular diagnostics/prognostics, and/or companion diagnostics for personalized cancer therapy
Comments on the Monopole-Antimonopole Pair Solutions
Recently, the monopole-antimonopole pair and monopole-antimonopole chain
solutions are solved with internal space coordinate system of -winding
number greater than one. However, we notice that it is also possible to
solve these solutions numerically in terms of -winding number
instead. When , the exact asymptotic solutions at small and large
distances are parameterized by a single integer parameter . Here we once
again study the monopole-antimonopole pair solution of the SU(2)
Yang-Mills-Higgs theory which belongs to the topological trivial sector
numerically in its new form. This solution with -winding and
-winding number one is parameterized by at small and at
large .Comment: Two figures, 13 pages, to be sent for publicatio
Dyons of One Half Monopole Charge
We would like to present some exact SU(2) Yang-Mills-Higgs dyon solutions of
one half monopole charge. These static dyon solutions satisfy the first order
Bogomol'nyi equations and are characterized by a parameter, . They are
axially symmetric. The gauge potentials and the electromagnetic fields possess
a string singularity along the negative z-axis and hence they possess infinite
energy density along the line singularity. However the net electric charges of
these dyons which varies with the parameter are finite.Comment: 16 pages, 7 figure
Particle Gibbs for Bayesian Additive Regression Trees
Additive regression trees are flexible non-parametric models and popular
off-the-shelf tools for real-world non-linear regression. In application
domains, such as bioinformatics, where there is also demand for probabilistic
predictions with measures of uncertainty, the Bayesian additive regression
trees (BART) model, introduced by Chipman et al. (2010), is increasingly
popular. As data sets have grown in size, however, the standard
Metropolis-Hastings algorithms used to perform inference in BART are proving
inadequate. In particular, these Markov chains make local changes to the trees
and suffer from slow mixing when the data are high-dimensional or the best
fitting trees are more than a few layers deep. We present a novel sampler for
BART based on the Particle Gibbs (PG) algorithm (Andrieu et al., 2010) and a
top-down particle filtering algorithm for Bayesian decision trees
(Lakshminarayanan et al., 2013). Rather than making local changes to individual
trees, the PG sampler proposes a complete tree to fit the residual. Experiments
show that the PG sampler outperforms existing samplers in many settings
Mondrian Forests for Large-Scale Regression when Uncertainty Matters
Many real-world regression problems demand a measure of the uncertainty
associated with each prediction. Standard decision forests deliver efficient
state-of-the-art predictive performance, but high-quality uncertainty estimates
are lacking. Gaussian processes (GPs) deliver uncertainty estimates, but
scaling GPs to large-scale data sets comes at the cost of approximating the
uncertainty estimates. We extend Mondrian forests, first proposed by
Lakshminarayanan et al. (2014) for classification problems, to the large-scale
non-parametric regression setting. Using a novel hierarchical Gaussian prior
that dovetails with the Mondrian forest framework, we obtain principled
uncertainty estimates, while still retaining the computational advantages of
decision forests. Through a combination of illustrative examples, real-world
large-scale datasets, and Bayesian optimization benchmarks, we demonstrate that
Mondrian forests outperform approximate GPs on large-scale regression tasks and
deliver better-calibrated uncertainty assessments than decision-forest-based
methods.Comment: Proceedings of the 19th International Conference on Artificial
Intelligence and Statistics (AISTATS) 2016, Cadiz, Spain. JMLR: W&CP volume
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