15 research outputs found
Inferring functional modules of protein families with probabilistic topic models
<p>Abstract</p> <p>Background</p> <p>Genome and metagenome studies have identified thousands of protein families whose functions are poorly understood and for which techniques for functional characterization provide only partial information. For such proteins, the genome context can give further information about their functional context.</p> <p>Results</p> <p>We describe a Bayesian method, based on a probabilistic topic model, which directly identifies functional modules of protein families. The method explores the co-occurrence patterns of protein families across a collection of sequence samples to infer a probabilistic model of arbitrarily-sized functional modules.</p> <p>Conclusions</p> <p>We show that our method identifies protein modules - some of which correspond to well-known biological processes - that are tightly interconnected with known functional interactions and are different from the interactions identified by pairwise co-occurrence. The modules are not specific to any given organism and may combine different realizations of a protein complex or pathway within different taxa.</p
Limited-Angle Tomography Reconstruction via Deep End-To-End Learning on Synthetic Data
Computed tomography (CT) has become an essential part of modern science and
medicine. A CT scanner consists of an X-ray source that is spun around an
object of interest. On the opposite end of the X-ray source, a detector
captures X-rays that are not absorbed by the object. The reconstruction of an
image is a linear inverse problem, which is usually solved by filtered back
projection. However, when the number of measurements is small, the
reconstruction problem is ill-posed. This is for example the case when the
X-ray source is not spun completely around the object, but rather irradiates
the object only from a limited angle. To tackle this problem, we present a deep
neural network that is trained on a large amount of carefully-crafted synthetic
data and can perform limited-angle tomography reconstruction even for only
30{\deg} or 40{\deg} sinograms. With our approach we won the first place in the
Helsinki Tomography Challenge 2022
A Survey on Self-Supervised Representation Learning
Learning meaningful representations is at the heart of many tasks in the
field of modern machine learning. Recently, a lot of methods were introduced
that allow learning of image representations without supervision. These
representations can then be used in downstream tasks like classification or
object detection. The quality of these representations is close to supervised
learning, while no labeled images are needed. This survey paper provides a
comprehensive review of these methods in a unified notation, points out
similarities and differences of these methods, and proposes a taxonomy which
sets these methods in relation to each other. Furthermore, our survey
summarizes the most-recent experimental results reported in the literature in
form of a meta-study. Our survey is intended as a starting point for
researchers and practitioners who want to dive into the field of representation
learning
Optimizing Intermediate Representations of Generative Models for Phase Retrieval
Phase retrieval is the problem of reconstructing images from magnitude-only
measurements. In many real-world applications the problem is underdetermined.
When training data is available, generative models are a new idea to constrain
the solution set. However, not all possible solutions are within the range of
the generator. Instead, they are represented with some error. To reduce this
representation error in the context of phase retrieval, we first leverage a
novel variation of intermediate layer optimization (ILO) to extend the range of
the generator while still producing images consistent with the training data.
Second, we introduce new initialization schemes that further improve the
quality of the reconstruction. With extensive experiments on Fourier and
Gaussian phase retrieval problems and thorough ablation studies, we can show
the benefits of our modified ILO and the new initialization schemes.Comment: First two authors contributed equall