7,296 research outputs found
A Selective Review of Group Selection in High-Dimensional Models
Grouping structures arise naturally in many statistical modeling problems.
Several methods have been proposed for variable selection that respect grouping
structure in variables. Examples include the group LASSO and several concave
group selection methods. In this article, we give a selective review of group
selection concerning methodological developments, theoretical properties and
computational algorithms. We pay particular attention to group selection
methods involving concave penalties. We address both group selection and
bi-level selection methods. We describe several applications of these methods
in nonparametric additive models, semiparametric regression, seemingly
unrelated regressions, genomic data analysis and genome wide association
studies. We also highlight some issues that require further study.Comment: Published in at http://dx.doi.org/10.1214/12-STS392 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
TTMFN: Two-stream Transformer-based Multimodal Fusion Network for Survival Prediction
Survival prediction plays a crucial role in assisting clinicians with the
development of cancer treatment protocols. Recent evidence shows that
multimodal data can help in the diagnosis of cancer disease and improve
survival prediction. Currently, deep learning-based approaches have experienced
increasing success in survival prediction by integrating pathological images
and gene expression data. However, most existing approaches overlook the
intra-modality latent information and the complex inter-modality correlations.
Furthermore, existing modalities do not fully exploit the immense
representational capabilities of neural networks for feature aggregation and
disregard the importance of relationships between features. Therefore, it is
highly recommended to address these issues in order to enhance the prediction
performance by proposing a novel deep learning-based method. We propose a novel
framework named Two-stream Transformer-based Multimodal Fusion Network for
survival prediction (TTMFN), which integrates pathological images and gene
expression data. In TTMFN, we present a two-stream multimodal co-attention
transformer module to take full advantage of the complex relationships between
different modalities and the potential connections within the modalities.
Additionally, we develop a multi-head attention pooling approach to effectively
aggregate the feature representations of the two modalities. The experiment
results on four datasets from The Cancer Genome Atlas demonstrate that TTMFN
can achieve the best performance or competitive results compared to the
state-of-the-art methods in predicting the overall survival of patients
Undisclosed, unmet and neglected challenges in multi-omics studies
[EN] Multi-omics approaches have become a reality in both large genomics projects and small laboratories. However, the multi-omics research community still faces a number of issues that have either not been sufficiently discussed or for which current solutions are still limited. In this Perspective, we elaborate on these limitations and suggest points of attention for future research. We finally discuss new opportunities and challenges brought to the field by the rapid development of single-cell high-throughput molecular technologies.This work has been funded by the Spanish Ministry of Science and Innovation with grant
number BES-2016-076994 to A.A.-L.Tarazona, S.; Arzalluz-Luque, Á.; Conesa, A. (2021). Undisclosed, unmet and neglected challenges in multi-omics studies. Nature Computational Science. 1(6):395-402. https://doi.org/10.1038/s43588-021-00086-z3954021
Hypothesis exploration with visualization of variance.
BackgroundThe Consortium for Neuropsychiatric Phenomics (CNP) at UCLA was an investigation into the biological bases of traits such as memory and response inhibition phenotypes-to explore whether they are linked to syndromes including ADHD, Bipolar disorder, and Schizophrenia. An aim of the consortium was in moving from traditional categorical approaches for psychiatric syndromes towards more quantitative approaches based on large-scale analysis of the space of human variation. It represented an application of phenomics-wide-scale, systematic study of phenotypes-to neuropsychiatry research.ResultsThis paper reports on a system for exploration of hypotheses in data obtained from the LA2K, LA3C, and LA5C studies in CNP. ViVA is a system for exploratory data analysis using novel mathematical models and methods for visualization of variance. An example of these methods is called VISOVA, a combination of visualization and analysis of variance, with the flavor of exploration associated with ANOVA in biomedical hypothesis generation. It permits visual identification of phenotype profiles-patterns of values across phenotypes-that characterize groups. Visualization enables screening and refinement of hypotheses about variance structure of sets of phenotypes.ConclusionsThe ViVA system was designed for exploration of neuropsychiatric hypotheses by interdisciplinary teams. Automated visualization in ViVA supports 'natural selection' on a pool of hypotheses, and permits deeper understanding of the statistical architecture of the data. Large-scale perspective of this kind could lead to better neuropsychiatric diagnostics
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