7 research outputs found
Multi-target drug repositioning by bipartite block-wise sparse multi-task learning
Abstract Background Finding potential drug targets is a crucial step in drug discovery and development. Recently, resources such as the Library of Integrated Network-Based Cellular Signatures (LINCS) L1000 database provide gene expression profiles induced by various chemical and genetic perturbations and thereby make it possible to analyze the relationship between compounds and gene targets at a genome-wide scale. Current approaches for comparing the expression profiles are based on pairwise connectivity mapping analysis. However, this method makes the simple assumption that the effect of a drug treatment is similar to knocking down its single target gene. Since many compounds can bind multiple targets, the pairwise mapping ignores the combined effects of multiple targets, and therefore fails to detect many potential targets of the compounds. Results We propose an algorithm to find sets of gene knock-downs that induce gene expression changes similar to a drug treatment. Assuming that the effects of gene knock-downs are additive, we propose a novel bipartite block-wise sparse multi-task learning model with super-graph structure (BBSS-MTL) for multi-target drug repositioning that overcomes the restrictive assumptions of connectivity mapping analysis. Conclusions The proposed method BBSS-MTL is more accurate for predicting potential drug targets than the simple pairwise connectivity mapping analysis on five datasets generated from different cancer cell lines. Availability The code can be obtained at http://gr.xjtu.edu.cn/web/liminli/codes
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Volumetric two-photon imaging of neurons using stereoscopy (vTwINS)
Two-photon laser scanning microscopy of calcium dynamics using fluorescent indicators is a
widely used imaging method for large scale recording of neural activity in vivo. Here we introduce
volumetric Two-photon Imaging of Neurons using Stereoscopy (vTwINS), a volumetric calcium
imaging method that employs an elongated, V-shaped point spread function to image a 3D brain
volume. Single neurons project to spatially displaced “image pairs” in the resulting 2D image, and
the separation distance between images is proportional to depth in the volume. To demix the
fluorescence time series of individual neurons, we introduce a novel orthogonal matching pursuit
algorithm that also infers source locations within the 3D volume. We illustrate vTwINS by
imaging neural population activity in mouse primary visual cortex and hippocampus. Our results
demonstrate that vTwINS provides an effective method for volumetric two-photon calcium
imaging that increases the number of neurons recorded while maintaining a high frame-rate