Insights from Ion Binding Site Network Analysis into Evolution and Functions of Proteins

Škrlj, Blaž; Kunej, Tanja; Konc, Janez

doi:10.1002/minf.201700144

Cited by 7 publications

(7 citation statements)

References 71 publications

(87 reference statements)

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“…The ions data set is based on the recently introduced protein-ion binding site similarity network. 47 The network was constructed by structural alignment using the ProBiS family of algorithms [48][49][50] where all known protein-ion binding sites were considered. The obtained network was pruned for structural redundancy as described in Škrlj et al 47 Each node corresponds to one of 12 possible ions, and each weighted connection corresponds to the ion-binding site similarity between the two considered binding sites.…”

Section: Data Setsmentioning

confidence: 99%

“…47 The network was constructed by structural alignment using the ProBiS family of algorithms [48][49][50] where all known protein-ion binding sites were considered. The obtained network was pruned for structural redundancy as described in Škrlj et al 47 Each node corresponds to one of 12 possible ions, and each weighted connection corresponds to the ion-binding site similarity between the two considered binding sites. Overall, this is to date one of the largest collections of structure-only node classification benchmark data sets.…”

Section: Data Setsmentioning

confidence: 99%

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Deep node ranking for neuro‐symbolic structural node embedding and classification

et al. 2021

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Network node embedding is an active research subfield of complex network analysis. This paper contributes a novel approach to learning network node embeddings and direct node classification using a node ranking scheme, coupled with an autoencoder-based neural network architecture. The main advantages of the proposed Deep Node Ranking (DNR) algorithm are competitive or better classification performance, significantly higher learning speed and lower space requirements when compared to state-of-the-art approaches on 15 real-life structural node classification benchmarks. It also enables exploration of the relationship between symbolic and the derived sub-symbolic node representations, offering insights into the learned node space structure. To avoid the space complexity bottleneck in a direct node classification setting, DNR, if needed, computes stationary distributions of personalized random walks from given nodes in mini-batches, scaling seamlessly to larger networks. The scaling laws associated with DNR were also investigated by considering 1,488 synthetic Erdős-Rényi networks, demonstrating its scalability to tens of millions of links.

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Section: Data Setsmentioning

confidence: 99%

Section: Data Setsmentioning

confidence: 99%

Deep node ranking for neuro‐symbolic structural node embedding and classification

et al. 2021

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“…• Ions [47], [48] is a network of ion binding sites, linked by their structural similarity. The target class is the type of ion that binds to a given protein substructure (node).…”

Section: A Datasetsmentioning

confidence: 99%

SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations

Mežnar¹,

Lavrač²,

Škrlj³

2020

IEEE Access

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Learning from complex real-life networks is a lively research area, with recent advances in learning information-rich, low-dimensional network node representations. However, state-of-the-art methods are not necessarily interpretable and are therefore not fully applicable to sensitive settings in biomedical or user profiling tasks, where explicit bias detection is highly relevant. The proposed SNoRe (Symbolic Node Representations) algorithm is capable of learning symbolic, human-understandable representations of individual network nodes, based on the similarity of neighborhood hashes which serve as features. SNoRe's interpretable features are suitable for direct explanation of individual predictions, which we demonstrate by coupling it with the widely used instance explanation tool SHAP to obtain nomograms representing the relevance of individual features for a given classification. To our knowledge, this is one of the first such attempts in a structural node embedding setting. In the experimental evaluation on eleven real-life datasets, SNoRe proved to be competitive to strong baselines, such as variational graph autoencoders, node2vec and LINE. The vectorized implementation of SNoRe scales to large networks, making it suitable for contemporary network learning and analysis tasks.

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“…Note that more than a single class is possible for an individual node, as we did not attempt to aggregate trust scores for individual nodes. -The Ions data set is based on the recently introduced protein-ion binding site similarity network [65]. The network was constructed by structural alignment using the ProBiS family of algorithms [31,30,32] where all known protein-ion binding sites were considered.…”

Section: Data Setsmentioning

confidence: 99%

Deep Node Ranking for Neuro-symbolic Structural Node Embedding and Classification

Škrlj,

Kralj,

Konc

et al. 2019

Preprint

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Complex networks are used as an abstraction for systems modeling in physics, biology, sociology, and other areas. We propose an algorithm, named Deep Node Ranking (DNR), based on fast personalized node ranking and the approximation power of deep learning for learning supervised and unsupervised network embeddings, as well as for classifying network nodes directly. The experiments demonstrate that the DNR algorithm is competitive with strong baselines on nine node classification benchmarks from the domains of molecular biology, finance, social media and language processing in terms of speed, as well as predictive accuracy. Embeddings, obtained by the proposed algorithm, are also a viable option for network visualization.

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Insights from Ion Binding Site Network Analysis into Evolution and Functions of Proteins

Cited by 7 publications

References 71 publications

Deep node ranking for neuro‐symbolic structural node embedding and classification

Deep node ranking for neuro‐symbolic structural node embedding and classification

SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations

Deep Node Ranking for Neuro-symbolic Structural Node Embedding and Classification

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