679 research outputs found
Joint Clustering and Registration of Functional Data
Curve registration and clustering are fundamental tools in the analysis of
functional data. While several methods have been developed and explored for
either task individually, limited work has been done to infer functional
clusters and register curves simultaneously. We propose a hierarchical model
for joint curve clustering and registration. Our proposal combines a Dirichlet
process mixture model for clustering of common shapes, with a reproducing
kernel representation of phase variability for registration. We show how
inference can be carried out applying standard posterior simulation algorithms
and compare our method to several alternatives in both engineered data and a
benchmark analysis of the Berkeley growth data. We conclude our investigation
with an application to time course gene expression
Comparison of Clustering Methods for Time Course Genomic Data: Applications to Aging Effects
Time course microarray data provide insight about dynamic biological
processes. While several clustering methods have been proposed for the analysis
of these data structures, comparison and selection of appropriate clustering
methods are seldom discussed. We compared probabilistic based clustering
methods and distance based clustering methods for time course microarray
data. Among probabilistic methods, we considered: smoothing spline clustering
also known as model based functional data analysis (MFDA), functional
clustering models for sparsely sampled data (FCM) and model-based clustering
(MCLUST). Among distance based methods, we considered: weighted gene
co-expression network analysis (WGCNA), clustering with dynamic time warping
distance (DTW) and clustering with autocorrelation based distance (ACF). We
studied these algorithms in both simulated settings and case study data. Our
investigations showed that FCM performed very well when gene curves were short
and sparse. DTW and WGCNA performed well when gene curves were medium or long
( observations). SSC performed very well when there were clusters of gene
curves similar to one another. Overall, ACF performed poorly in these
applications. In terms of computation time, FCM, SSC and DTW were considerably
slower than MCLUST and WGCNA. WGCNA outperformed MCLUST by generating more
accurate and biological meaningful clustering results. WGCNA and MCLUST are the
best methods among the 6 methods compared, when performance and computation
time are both taken into account. WGCNA outperforms MCLUST, but MCLUST provides
model based inference and uncertainty measure of clustering results
Cell-type specific potent Wnt signaling blockade by bispecific antibody.
Cell signaling pathways are often shared between normal and diseased cells. How to achieve cell type-specific, potent inhibition of signaling pathways is a major challenge with implications for therapeutic development. Using the Wnt/β-catenin signaling pathway as a model system, we report here a novel and generally applicable method to achieve cell type-selective signaling blockade. We constructed a bispecific antibody targeting the Wnt co-receptor LRP6 (the effector antigen) and a cell type-associated antigen (the guide antigen) that provides the targeting specificity. We found that the bispecific antibody inhibits Wnt-induced reporter activities with over one hundred-fold enhancement in potency, and in a cell type-selective manner. Potency enhancement is dependent on the expression level of the guide antigen on the target cell surface and the apparent affinity of the anti-guide antibody. Both internalizing and non-internalizing guide antigens can be used, with internalizing bispecific antibody being able to block signaling by all ligands binding to the target receptor due to its removal from the cell surface. It is thus feasible to develop bispecific-based therapeutic strategies that potently and selectively inhibit signaling pathways in a cell type-selective manner, creating opportunity for therapeutic targeting
VT-CLIP: Enhancing Vision-Language Models with Visual-guided Texts
Contrastive Language-Image Pre-training (CLIP) has drawn increasing attention
recently for its transferable visual representation learning. However, due to
the semantic gap within datasets, CLIP's pre-trained image-text alignment
becomes sub-optimal on downstream tasks, which severely harms its transferring
performance. To better adapt the cross-modality embedding space, we propose to
enhance CLIP via Visual-guided Texts, named VT-CLIP. Specifically, we guide
textual features of different categories to adaptively explore informative
regions on the image and aggregate visual features by attention mechanisms. In
this way, the texts become visual-guided, namely, more semantically correlated
with downstream images, which greatly benefits the category-wise matching
process. In few-shot settings, we evaluate our VT-CLIP on 11 well-known
classification datasets to demonstrate its effectiveness
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