COSNet: An R package for label prediction in unbalanced biological networks

Frasca, Marco; Valentini, Giorgio

doi:10.1016/j.neucom.2015.11.096

Cited by 9 publications

(9 citation statements)

References 13 publications

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“…For COSNet and RANKS we used the source code publicly available as R package (Frasca and Valentini, 2017;Valentini et al, 2016), and for the other methods we used the code provided by the authors or our in-house software implementations. The parameters required by our GTG approach and the other considered methods in this work have been learned through internal tuning on a small subset of training data.…”

Section: Ms-knnmentioning

confidence: 99%

Protein function prediction as a graph-transduction game

Vascon

Frasca

Tripodi

et al. 2020

Pattern Recognition Letters

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Motivated by the observation that network-based methods for the automatic prediction of protein functions can greatly benefit from exploiting both the similarity between proteins and the similarity between functional classes (as encoded, e.g., in the Gene Ontology), in this paper we propose a novel approach to the problem which is based on the notion of a "graph transduction game." We envisage a (non-cooperative) game, played over a graph, where the players (graph vertices) represent proteins, the functional classes correspond to the (pure) strategies, and protein-and function-level similarities are combined into a suitable payoff function. Within this formulation, Nash equilibria turn out to provide consistent functional labelings of proteins, and we use classical replicator dynamics from evolutionary game theory to find them. To test the effectiveness of our approach we conducted experiments on five different organisms and three ontologies, and the results obtained show that our approach compares favorably with state-of-the-art algorithms.

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Section: Ms-knnmentioning

confidence: 99%

Protein function prediction as a graph-transduction game

Vascon

Frasca

Tripodi

et al. 2020

Pattern Recognition Letters

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“…The COSNET implementation is publicly available as an R package [29], where the time consuming procedures (e.g. parameter learning and Hopfield dynamics) for efficiency reasons are implemented in C language.…”

Section: Resultsmentioning

confidence: 99%

“…It extends COSNET (Cost-Sensitive Neural Network) [14], a state-of-the-art semi-supervised method for AFP based on HNs. COSNET introduces a parametric HN to effectively handle the label imbalance, but its available implementation [29] still adopts a matrix representation of input data, allowing its application (on ordinary off-the-shelf computer) only to networks with few tens of thousands of nodes.…”

Section: Introductionmentioning

confidence: 99%

A GPU-based algorithm for fast node label learning in large and unbalanced biomolecular networks

et al. 2018

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BackgroundSeveral problems in network biology and medicine can be cast into a framework where entities are represented through partially labeled networks, and the aim is inferring the labels (usually binary) of the unlabeled part. Connections represent functional or genetic similarity between entities, while the labellings often are highly unbalanced, that is one class is largely under-represented: for instance in the automated protein function prediction (AFP) for most Gene Ontology terms only few proteins are annotated, or in the disease-gene prioritization problem only few genes are actually known to be involved in the etiology of a given disease. Imbalance-aware approaches to accurately predict node labels in biological networks are thereby required. Furthermore, such methods must be scalable, since input data can be large-sized as, for instance, in the context of multi-species protein networks.ResultsWe propose a novel semi-supervised parallel enhancement of COSNet, an imbalance-aware algorithm build on Hopfield neural model recently suggested to solve the AFP problem. By adopting an efficient representation of the graph and assuming a sparse network topology, we empirically show that it can be efficiently applied to networks with millions of nodes. The key strategy to speed up the computations is to partition nodes into independent sets so as to process each set in parallel by exploiting the power of GPU accelerators. This parallel technique ensures the convergence to asymptotically stable attractors, while preserving the asynchronous dynamics of the original model. Detailed experiments on real data and artificial big instances of the problem highlight scalability and efficiency of the proposed method.ConclusionsBy parallelizing COSNet we achieved on average a speed-up of 180x in solving the AFP problem in the S. cerevisiae, Mus musculus and Homo sapiens organisms, while lowering memory requirements. In addition, to show the potential applicability of the method to huge biomolecular networks, we predicted node labels in artificially generated sparse networks involving hundreds of thousands to millions of nodes.

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“…Imbalance-aware methods obtained successful result in similar contexts, such as in protein function [23,24] and gene expression [25] prediction, and drug repositioning [26].…”

Section: Firstmentioning

confidence: 99%

Exploiting Negative Sample Selection for Prioritizing Candidate Disease Genes

Frasca

Malchiodi

2017

Genomics Comput Biol

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A major challenge in bio-medicine is finding the genetic causes of human diseases, and researchers are often faced with a large number of candidate genes. Gene prioritization methods provide a valuable support in guiding researchers to detect reliable candidate causative-genes for a disease under study. Indeed, such methods rank genes according to their association with a disease of interest. Actually, the majority of genetic disorders has few or none causative genes associated with them; this induces a high labeling unbalance in the corresponding ranking problems, thus linking the need of achieving reliable solutions to the adoption of imbalance-aware techniques. We propose the use of an expressly designed imbalance-aware methodology for prioritizing genes, which first rebalances the training set entries through a negative selection procedure, then applies a learning algorithm 'sensitive' to the misclassification of positive instances, to provide the gene ranking. The algorithm has a reduced time complexity, which makes feasible its application on large-sized datasets. The validation of this methodology proved its competitiveness with state-of-art techniques on a benchmark composed of 708 selected Medical Subject Headings diseases, and provided some putative novel gene-disease associations.

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COSNet: An R package for label prediction in unbalanced biological networks

Cited by 9 publications

References 13 publications

Protein function prediction as a graph-transduction game

Protein function prediction as a graph-transduction game

A GPU-based algorithm for fast node label learning in large and unbalanced biomolecular networks

Exploiting Negative Sample Selection for Prioritizing Candidate Disease Genes

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