11,288 research outputs found
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models
The interpretation of complex high-dimensional data typically requires the
use of dimensionality reduction techniques to extract explanatory
low-dimensional representations. However, in many real-world problems these
representations may not be sufficient to aid interpretation on their own, and
it would be desirable to interpret the model in terms of the original features
themselves. Our goal is to characterise how feature-level variation depends on
latent low-dimensional representations, external covariates, and non-linear
interactions between the two. In this paper, we propose to achieve this through
a structured kernel decomposition in a hybrid Gaussian Process model which we
call the Covariate Gaussian Process Latent Variable Model (c-GPLVM). We
demonstrate the utility of our model on simulated examples and applications in
disease progression modelling from high-dimensional gene expression data in the
presence of additional phenotypes. In each setting we show how the c-GPLVM can
extract low-dimensional structures from high-dimensional data sets whilst
allowing a breakdown of feature-level variability that is not present in other
commonly used dimensionality reduction approaches
Joint learning from multiple information sources for biological problems
Thanks to technological advancements, more and more biological data havebeen generated in recent years. Data availability offers unprecedented opportunities to look at the same problem from multiple aspects. It also unveils a more global view of the problem that takes into account the intricated inter-play between the involved molecules/entities. Nevertheless, biological datasets are biased, limited in quantity, and contain many false-positive samples. Such challenges often drastically downgrade the performance of a predictive model on unseen data and, thus, limit its applicability in real biological studies.
Human learning is a multi-stage process in which we usually start with simple things. Through the accumulated knowledge over time, our cognition ability extends to more complex concepts. Children learn to speak simple words before being able to formulate sentences. Similarly, being able to speak correct sentences supports our learning to speak correct and meaningful paragraphs, etc. Generally, knowledge acquired from related learning tasks would help boost our learning capability in the current task. Motivated by such a phenomenon, in this thesis, we study supervised machine learning models for bioinformatics problems that can improve their performance through exploiting multiple related knowledge sources. More specifically, we concern with ways to enrich the supervised models’ knowledge base with publicly available related data to enhance the computational models’ prediction performance.
Our work shares commonality with existing works in multimodal learning, multi-task learning, and transfer learning. Nevertheless, there are certain differences in some cases. Besides the proposed architectures, we present large-scale experiment setups with consensus evaluation metrics along with the creation and release of large datasets to showcase our approaches’ superiority. Moreover, we add case studies with detailed analyses in which we place no simplified assumptions to demonstrate the systems’ utilities in realistic application scenarios. Finally, we develop and make available an easy-to-use website for non-expert users to query the model’s generated prediction results to facilitate field experts’ assessments and adaptation. We believe that our work serves as one of the first steps in bridging the gap between “Computer Science” and “Biology” that will open a new era of fruitful collaboration between computer scientists and biological field experts
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Novel translational approaches to the search for precision therapies for acute respiratory distress syndrome.
In the 50 years since acute respiratory distress syndrome (ARDS) was first described, substantial progress has been made in identifying the risk factors for and the pathogenic contributors to the syndrome and in characterising the protein expression patterns in plasma and bronchoalveolar lavage fluid from patients with ARDS. Despite this effort, however, pharmacological options for ARDS remain scarce. Frequently cited reasons for this absence of specific drug therapies include the heterogeneity of patients with ARDS, the potential for a differential response to drugs, and the possibility that the wrong targets have been studied. Advances in applied biomolecular technology and bioinformatics have enabled breakthroughs for other complex traits, such as cardiovascular disease or asthma, particularly when a precision medicine paradigm, wherein a biomarker or gene expression pattern indicates a patient's likelihood of responding to a treatment, has been pursued. In this Review, we consider the biological and analytical techniques that could facilitate a precision medicine approach for ARDS
Towards a New Science of a Clinical Data Intelligence
In this paper we define Clinical Data Intelligence as the analysis of data
generated in the clinical routine with the goal of improving patient care. We
define a science of a Clinical Data Intelligence as a data analysis that
permits the derivation of scientific, i.e., generalizable and reliable results.
We argue that a science of a Clinical Data Intelligence is sensible in the
context of a Big Data analysis, i.e., with data from many patients and with
complete patient information. We discuss that Clinical Data Intelligence
requires the joint efforts of knowledge engineering, information extraction
(from textual and other unstructured data), and statistics and statistical
machine learning. We describe some of our main results as conjectures and
relate them to a recently funded research project involving two major German
university hospitals.Comment: NIPS 2013 Workshop: Machine Learning for Clinical Data Analysis and
Healthcare, 201
Graph Representation Learning in Biomedicine
Biomedical networks are universal descriptors of systems of interacting
elements, from protein interactions to disease networks, all the way to
healthcare systems and scientific knowledge. With the remarkable success of
representation learning in providing powerful predictions and insights, we have
witnessed a rapid expansion of representation learning techniques into
modeling, analyzing, and learning with such networks. In this review, we put
forward an observation that long-standing principles of networks in biology and
medicine -- while often unspoken in machine learning research -- can provide
the conceptual grounding for representation learning, explain its current
successes and limitations, and inform future advances. We synthesize a spectrum
of algorithmic approaches that, at their core, leverage graph topology to embed
networks into compact vector spaces, and capture the breadth of ways in which
representation learning is proving useful. Areas of profound impact include
identifying variants underlying complex traits, disentangling behaviors of
single cells and their effects on health, assisting in diagnosis and treatment
of patients, and developing safe and effective medicines
Visual Feature Attribution using Wasserstein GANs
Attributing the pixels of an input image to a certain category is an
important and well-studied problem in computer vision, with applications
ranging from weakly supervised localisation to understanding hidden effects in
the data. In recent years, approaches based on interpreting a previously
trained neural network classifier have become the de facto state-of-the-art and
are commonly used on medical as well as natural image datasets. In this paper,
we discuss a limitation of these approaches which may lead to only a subset of
the category specific features being detected. To address this problem we
develop a novel feature attribution technique based on Wasserstein Generative
Adversarial Networks (WGAN), which does not suffer from this limitation. We
show that our proposed method performs substantially better than the
state-of-the-art for visual attribution on a synthetic dataset and on real 3D
neuroimaging data from patients with mild cognitive impairment (MCI) and
Alzheimer's disease (AD). For AD patients the method produces compellingly
realistic disease effect maps which are very close to the observed effects.Comment: Accepted to CVPR 201
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