16 papers · ranked by Valyu relevance
Maximilian Joas, Neringa Jurenaite, Dusan Prascevic, Nico Scherf + 1 more
Insights and discoveries in complex biological systems, e.g. for personalized medicine, are gained by the combination of large, feature-rich and high-dimensional data with powerful computational methods uncovering patterns and relationships. In recent years, autoencoders, a family of deep learning-based methods for…
Nan Miles Xi, Jingyi Jessica Li
Autoencoders are the backbones of many imputation methods that aim to relieve the sparsity issue in single-cell RNA sequencing (scRNA-seq) data. The imputation performance of an autoencoder relies on both the neural network architecture and the hyperparameter choice. So far, literature in the single-cell field lacks a…
Aman Gupta, Haohan Wang, Madhavi Ganapathiraju
Genes play a central role in all biological processes. DNA microarray technology has made it possible to study the expression behavior of thousands of genes in one go. Often, gene expression data is used to generate features for supervised and unsupervised learning tasks. At the same time, advances in the field of deep…
Sangyeon Lee, Hanjin Kim, Doheon Lee
Regression analysis is one of the most widely applied methods in many fields including bio-medical study. Dimensionality reduction is also widely used for data preprocessing and feature selection analysis, to extract high-impact features from the predictions. As the complexity of both data and prediction models…
María Peña Fernández, Lara Lloret Iglesias, Jesús Marco de Lucas
One of the most compelling ideas for bridging neuroscience and artificial neural networks is the establishment of a framework based on three main components: network architecture, optimization mechanism, and loss (or objective) function to be minimized. While the first two components have been extensively explored, the…
Guy Aridor, Francesco Grechi, Michael Woodford
We study a model of neural coding with the structure of a variational auto-encoder. The model posits that the encoding of individual stimulus values is optimally adjusted for a finite training sample of stimuli retained in memory. We demonstrate that this model can rationalize existing experimental evidence on both…
Matteo Negri, Davide Bergamini, Carlo Baldassi, Riccardo Zecchina + 1 more
Generative processes in biology and other fields often produce data that can be regarded as resulting from a composition of basic features. Here we present an unsupervised method based on autoencoders for inferring these basic features of data. The main novelty in our approach is that the training is based on the…
Chao Zhang
Variational Autoencoder (VAE) is a generative model from the computer vision community; it learns a latent representation of the images and generates new images in an unsupervised way. Recently, Vanilla VAE has been applied to analyse single-cell datasets, in the hope of harnessing the representation power of latent…
Mostafa Eltager, Tamim Abdelaal, Mohammed Charrout, Ahmed Mahfouz + 2 more
Deep generative models, such as variational autoencoders (VAE), have gained increasing attention in computational biology due to their ability to capture complex data manifolds which subsequently can be used to achieve better performance in downstream tasks, such as cancer type prediction or subtyping of cancer.…
Gian Marco Visani, Michael N. Pun, Armita Nourmohammad
Group-equivariant neural networks have emerged as a data-efficient approach to solve classification and regression tasks, while respecting the relevant symmetries of the data. However, little work has been done to extend this paradigm to the unsupervised and generative domains. Here, we present Holographic-(V)AE…
Axel Truedson, Konrad Gras, Johan Elf
The 3D point spread function of a fluorescence emitter in a living cell is often different from that of the z-stack of bead images typically used as a reference. Here we show that the location in z of a fluorescent emitter can be directly accessed in the latent space of an autoencoder trained on the sample images…
Florian Eichin, Maren Hackenberg, Caroline Broichhagen, Antje Kilias + 3 more
Live imaging techniques, such as two-photon imaging, promise novel insights into cellular activity patterns at a high spatial and temporal resolution. While current deep learning approaches typically focus on specific supervised tasks in the analysis of such data, e.g., learning a segmentation mask as a basis for…
Andrea Tangherloni, Federico Ricciuti, Daniela Besozzi, Pietro Liò + 1 more
Autoencoders (AEs) have been effectively used to capture the non-linearities among gene interactions of single-cell RNA sequencing (scRNA-Seq) data. However, their integration with the common scRNA-Seq bioinformatics pipelines still poses a challenge. Here, we introduce scAEspy, a unifying tool that embodies five of…
Nikhil Parthasarathy, Eleanor Batty, William Falcon, Thomas Rutten + 3 more
Decoding sensory stimuli from neural signals can be used to reveal how we sense our physical environment, and is valuable for the design of brain-machine interfaces. However, existing linear techniques for neural decoding may not fully reveal or exploit the fidelity of the neural signal. Here we develop a new…
Gizem Taş, Eric Postma, Marleen Balvert, Alexander Schönhuth
Exploring the heritability of complex genetic traits requires methods that can handle the genome’s vast scale and the intricate re-lationships among genetic markers. Widely accepted association studies overlook non-linear effects (epistasis), prompting the adoption of deep neural networks (DNNs) for their scalability…
Péter Szabó, Péter Barthó
Recent advancements in multielectrode methods and spike-sorting algorithms enable the in vivo recording of the activities of many neurons at a high temporal resolution. These datasets offer new opportunities in the investigation of the biological neural code, including the direct testing of specific coding hypotheses…