16,048 research outputs found
Structured penalized regression for drug sensitivity prediction
Large-scale {\it in vitro} drug sensitivity screens are an important tool in
personalized oncology to predict the effectiveness of potential cancer drugs.
The prediction of the sensitivity of cancer cell lines to a panel of drugs is a
multivariate regression problem with high-dimensional heterogeneous multi-omics
data as input data and with potentially strong correlations between the outcome
variables which represent the sensitivity to the different drugs. We propose a
joint penalized regression approach with structured penalty terms which allow
us to utilize the correlation structure between drugs with group-lasso-type
penalties and at the same time address the heterogeneity between omics data
sources by introducing data-source-specific penalty factors to penalize
different data sources differently. By combining integrative penalty factors
(IPF) with tree-guided group lasso, we create the IPF-tree-lasso method. We
present a unified framework to transform more general IPF-type methods to the
original penalized method. Because the structured penalty terms have multiple
parameters, we demonstrate how the interval-search Efficient Parameter
Selection via Global Optimization (EPSGO) algorithm can be used to optimize
multiple penalty parameters efficiently. Simulation studies show that
IPF-tree-lasso can improve the prediction performance compared to other
lasso-type methods, in particular for heterogenous data sources. Finally, we
employ the new methods to analyse data from the Genomics of Drug Sensitivity in
Cancer project.Comment: Zhao Z, Zucknick M (2020). Structured penalized regression for drug
sensitivity prediction. Journal of the Royal Statistical Society, Series C.
19 pages, 6 figures and 2 table
On the four-zero texture of quark mass matrices and its stability
We carry out a new study of quark mass matrices (up-type) and
(down-type) which are Hermitian and have four zero entries, and
find a new part of the parameter space which was missed in the previous works.
We identify two more specific four-zero patterns of and
with fewer free parameters, and present two toy flavor-symmetry
models which can help realize such special and interesting quark flavor
structures. We also show that the texture zeros of and
are essentially stable against the evolution of energy scales in
an analytical way by using the one-loop renormalization-group equations.Comment: 33 pages, 4 figures, minor comments added, version to appear in Nucl.
Phys.
Hierarchical RNN with Static Sentence-Level Attention for Text-Based Speaker Change Detection
Speaker change detection (SCD) is an important task in dialog modeling. Our
paper addresses the problem of text-based SCD, which differs from existing
audio-based studies and is useful in various scenarios, for example, processing
dialog transcripts where speaker identities are missing (e.g., OpenSubtitle),
and enhancing audio SCD with textual information. We formulate text-based SCD
as a matching problem of utterances before and after a certain decision point;
we propose a hierarchical recurrent neural network (RNN) with static
sentence-level attention. Experimental results show that neural networks
consistently achieve better performance than feature-based approaches, and that
our attention-based model significantly outperforms non-attention neural
networks.Comment: In Proceedings of the ACM on Conference on Information and Knowledge
Management (CIKM), 201
The effective neutrino mass of neutrinoless double-beta decays: how possible to fall into a well
If massive neutrinos are the Majorana particles and have a normal mass
ordering, the effective mass term of a neutrinoless
double-beta () decay may suffer significant cancellations among
its three components and thus sink into a decline, resulting in a "well" in the
three-dimensional graph of against the smallest
neutrino mass and the relevant Majorana phase . We present a new
and complete analytical understanding of the fine issues inside such a well,
and discover a novel threshold of in terms of the
neutrino masses and flavor mixing angles: in connection with and . This threshold point, which links the
{\it local} minimum and maximum of , can be used to
signify observability or sensitivity of the future -decay
experiments. Given current neutrino oscillation data, the possibility of
is found to be
very small.Comment: 9 pages, 3 figures, version to appear in Eur. Phys. J.
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