Tag: deeplearning

Weakly-Supervised Prediction of Cell Migration Modes in Confocal Microscopy Images Using Bayesian Deep Learning

Cell migration is pivotal for their development, physiology and disease treatment. A single cell on a 2D surface can utilize continuous or discontinuous migration modes. To comprehend the cell migration, an adequate quantification for single cell-based analysis is crucial. An automatized approach could alleviate tedious manual analysis, facilitating large-scale drug screening. Supervised deep learning has…
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Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

Live-cell imaging experiments have opened an exciting window into the behavior of living systems. While these experiments can produce rich data, the computational analysis of these datasets is challenging. Single-cell analysis requires that cells be accurately identified in each image and subsequently tracked over time. Increasingly, deep learning is being used to interpret microscopy image…
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Gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

Understanding the genetic regulatory code that governs gene expression is a primary, yet challenging aspiration in molecular biology that opens up possibilities to cure human diseases and solve biotechnology problems. However, the fundamental question of how each of the individual coding and non-coding regions of the gene regulatory structure interact and contribute to the mRNA…
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Fully Interpretable Deep Learning Model of Transcriptional Control

The universal expressibility assumption of Deep Neural Networks (DNN) is the key motivation behind recent work in the system biology community to employ DNNs to solve important problems in functional genomics and molecular genetics. Because of the black-box nature of DNN, such assumptions, while useful in practice, are unsatisfactory for scientific analysis. In this paper,…
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