4,111 research outputs found
Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces
biological space
DHLP 1&2: Giraph based distributed label propagation algorithms on heterogeneous drug-related networks
Background and Objective: Heterogeneous complex networks are large graphs
consisting of different types of nodes and edges. The knowledge extraction from
these networks is complicated. Moreover, the scale of these networks is
steadily increasing. Thus, scalable methods are required. Methods: In this
paper, two distributed label propagation algorithms for heterogeneous networks,
namely DHLP-1 and DHLP-2 have been introduced. Biological networks are one type
of the heterogeneous complex networks. As a case study, we have measured the
efficiency of our proposed DHLP-1 and DHLP-2 algorithms on a biological network
consisting of drugs, diseases, and targets. The subject we have studied in this
network is drug repositioning but our algorithms can be used as general methods
for heterogeneous networks other than the biological network. Results: We
compared the proposed algorithms with similar non-distributed versions of them
namely MINProp and Heter-LP. The experiments revealed the good performance of
the algorithms in terms of running time and accuracy.Comment: Source code available for Apache Giraph on Hadoo
Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs
Background: MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Results: Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations. Conclusions: Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks
A two-step learning approach for solving full and almost full cold start problems in dyadic prediction
Dyadic prediction methods operate on pairs of objects (dyads), aiming to
infer labels for out-of-sample dyads. We consider the full and almost full cold
start problem in dyadic prediction, a setting that occurs when both objects in
an out-of-sample dyad have not been observed during training, or if one of them
has been observed, but very few times. A popular approach for addressing this
problem is to train a model that makes predictions based on a pairwise feature
representation of the dyads, or, in case of kernel methods, based on a tensor
product pairwise kernel. As an alternative to such a kernel approach, we
introduce a novel two-step learning algorithm that borrows ideas from the
fields of pairwise learning and spectral filtering. We show theoretically that
the two-step method is very closely related to the tensor product kernel
approach, and experimentally that it yields a slightly better predictive
performance. Moreover, unlike existing tensor product kernel methods, the
two-step method allows closed-form solutions for training and parameter
selection via cross-validation estimates both in the full and almost full cold
start settings, making the approach much more efficient and straightforward to
implement
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions
Graph representation learning (GRL) has emerged as a pivotal field that has
contributed significantly to breakthroughs in various fields, including
biomedicine. The objective of this survey is to review the latest advancements
in GRL methods and their applications in the biomedical field. We also
highlight key challenges currently faced by GRL and outline potential
directions for future research.Comment: Accepted by 2023 IMIA Yearbook of Medical Informatic
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