24,239 research outputs found
Prediction of protein-protein interactions using one-class classification methods and integrating diverse data
This research addresses the problem of prediction of protein-protein interactions (PPI)
when integrating diverse kinds of biological information. This task has been commonly
viewed as a binary classification problem (whether any two proteins do or do not interact)
and several different machine learning techniques have been employed to solve this
task. However the nature of the data creates two major problems which can affect results.
These are firstly imbalanced class problems due to the number of positive examples (pairs
of proteins which really interact) being much smaller than the number of negative ones.
Secondly the selection of negative examples can be based on some unreliable assumptions
which could introduce some bias in the classification results.
Here we propose the use of one-class classification (OCC) methods to deal with the task of
prediction of PPI. OCC methods utilise examples of just one class to generate a predictive
model which consequently is independent of the kind of negative examples selected; additionally
these approaches are known to cope with imbalanced class problems. We have
designed and carried out a performance evaluation study of several OCC methods for this
task, and have found that the Parzen density estimation approach outperforms the rest. We
also undertook a comparative performance evaluation between the Parzen OCC method
and several conventional learning techniques, considering different scenarios, for example
varying the number of negative examples used for training purposes. We found that the
Parzen OCC method in general performs competitively with traditional approaches and in
many situations outperforms them. Finally we evaluated the ability of the Parzen OCC
approach to predict new potential PPI targets, and validated these results by searching for
biological evidence in the literature
Introduction to Protein Structure Prediction
This chapter gives a graceful introduction to problem of protein three-
dimensional structure prediction, and focuses on how to make structural sense
out of a single input sequence with unknown structure, the 'query' or 'target'
sequence. We give an overview of the different classes of modelling techniques,
notably template-based and template free. We also discuss the way in which
structural predictions are validated within the global com- munity, and
elaborate on the extent to which predicted structures may be trusted and used
in practice. Finally we discuss whether the concept of a sin- gle fold
pertaining to a protein structure is sustainable given recent insights. In
short, we conclude that the general protein three-dimensional structure
prediction problem remains unsolved, especially if we desire quantitative
predictions. However, if a homologous structural template is available in the
PDB model or reasonable to high accuracy may be generated
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
Herb Target Prediction Based on Representation Learning of Symptom related Heterogeneous Network.
Traditional Chinese Medicine (TCM) has received increasing attention as a complementary approach or alternative to modern medicine. However, experimental methods for identifying novel targets of TCM herbs heavily relied on the current available herb-compound-target relationships. In this work, we present an Herb-Target Interaction Network (HTINet) approach, a novel network integration pipeline for herb-target prediction mainly relying on the symptom related associations. HTINet focuses on capturing the low-dimensional feature vectors for both herbs and proteins by network embedding, which incorporate the topological properties of nodes across multi-layered heterogeneous network, and then performs supervised learning based on these low-dimensional feature representations. HTINet obtains performance improvement over a well-established random walk based herb-target prediction method. Furthermore, we have manually validated several predicted herb-target interactions from independent literatures. These results indicate that HTINet can be used to integrate heterogeneous information to predict novel herb-target interactions
Structure- and context-based analysis of the GxGYxYP family reveals a new putative class of glycoside hydrolase.
BackgroundGut microbiome metagenomics has revealed many protein families and domains found largely or exclusively in that environment. Proteins containing the GxGYxYP domain are over-represented in the gut microbiota, and are found in Polysaccharide Utilization Loci in the gut symbiont Bacteroides thetaiotaomicron, suggesting their involvement in polysaccharide metabolism, but little else is known of the function of this domain.ResultsGenomic context and domain architecture analyses support a role for the GxGYxYP domain in carbohydrate metabolism. Sparse occurrences in eukaryotes are the result of lateral gene transfer. The structure of the GxGYxYP domain-containing protein encoded by the BT2193 locus reveals two structural domains, the first composed of three divergent repeats with no recognisable homology to previously solved structures, the second a more familiar seven-stranded β/α barrel. Structure-based analyses including conservation mapping localise a presumed functional site to a cleft between the two domains of BT2193. Matching to a catalytic site template from a GH9 cellulase and other analyses point to a putative catalytic triad composed of Glu272, Asp331 and Asp333.ConclusionsWe suggest that GxGYxYP-containing proteins constitute a novel glycoside hydrolase family of as yet unknown specificity
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