Graph neural network: Current state of Art, challenges and applications

Gupta, Atika; Matta, Priya; Pant, Bhasker

doi:10.1016/j.matpr.2021.01.950

Cited by 28 publications

(9 citation statements)

References 8 publications

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“…Second, sequence embeddings and graph neural networks are relatively new tools, and their applicability to biological tasks are still being explored. In this study, we not only demonstrated their usefulness in the prediction of protein-ligand interactions but also developed new solutions such as the EdgeGAT layer (Figure 3), substantially expanding the applicability of GNNs in structural biology (30).…”

Section: Discussionmentioning

confidence: 91%

“…The limitation of the current GNN architectures in the context of processing molecular data is their inability to jointly process information stored in nodes and edges (30). This feature is essential for obtaining a complete graph representation of a protein structure in which residues (nodes) and interactions (edges) aren't artificially separated.…”

Section: Structure-based Approachmentioning

confidence: 99%

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Rossmann-toolbox: a deep learning-based protocol for the prediction and design of cofactor specificity in Rossmann-fold proteins

Kamiński

Ludwiczak

Jasiński

et al. 2021

Preprint

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The Rossmann fold enzymes are involved in essential biochemical pathways such as nucleotide and amino acid metabolism. Their functioning relies on interaction with cofactors, small nucleoside-based compounds specifically recognized by a conserved βαβ motif shared by all Rossmann fold proteins. While Rossmann methyltransferases and enzymes involved in the polyamine synthesis recognize only a single cofactor type, the S-Adenosylmethionine (SAM), the oxidoreductases, depending on the family, bind nicotinamide (NAD, NADP) or flavin-based (FAD) cofactors. In this study, we show that despite its short length, the βαβ motif unambiguously defines the specificity towards the cofactor. Following this observation, we trained two complementary deep learning models for the prediction of the cofactor specificity based on the features of the βαβ motif. The first utilizes contextualized sequence embeddings, whereas the second relies on structures represented as graphs. A benchmark on two test sets, one containing βαβ motifs bearing no resemblance to those of the training set, and the other comprising 38 cases of the experimentally confirmed redesign of the cofactor specificity from NAD to NADP and vice versa, revealed nearly-perfect performance (~95% accuracy) of the two methods. Finally, by combining the two approaches, we built a pipeline for the design of cofactor-switching mutations. Both prediction methods can be accessed via the webserver https://lbs.cent.uw.edu.pl/rossmann-toolbox and are available as a Python package at https://github.com/labstructbioinf/rossmann-toolbox.

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Section: Discussionmentioning

confidence: 91%

Section: Structure-based Approachmentioning

confidence: 99%

Rossmann-toolbox: a deep learning-based protocol for the prediction and design of cofactor specificity in Rossmann-fold proteins

Kamiński

Ludwiczak

Jasiński

et al. 2021

Preprint

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“…Zhang et al [319] conducted a systematic evaluation on the state-of-the-art GNNs to investigate the factors that cause compromised performance in deep GNNs, the application scenarios that suite the best for deep GNNS, and how we build them, with respect to accuracy, flexibility, scalability and efficiency of GNNs. Similar survey works on GNN categories and theories also include [90,287]. For technical issues in designing and implementing specific GNNs, various works surveyed them from the perspectives of explainability [309], dynamics [244], and expressive power [229].…”

Section: Previous Workmentioning

confidence: 99%

Graph Neural Networks in IoT: A Survey

Dong¹,

Tang²,

Wang³

et al. 2022

Preprint

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The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily lives: healthcare, home, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technologies, IoT devices including smart wearables, cameras, smartwatches, and autonomous vehicles can accurately measure and perceive their surrounding environment. Continuous sensing generates massive amounts of data and presents challenges for machine learning. Deep learning models (e.g., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data. Graph Neural Networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art results in numerous IoT learning tasks. In this survey, we present a comprehensive review of recent advances in the application of GNNs to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching list of public data and source code from the collected publications, and future research directions. To keep track of newly published works, we collect representative papers and their open-source implementations and create a Github repository at GNN4IoT. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing systems and tools; • Computing methodologies → Neural networks.

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“…A new DNA pattern-based local feature generator was proposed. There have been several graph-based feature extraction models in the literature [18,47] and molecular structure graphs used in deep learning models and graph networks have attained high classification performance [48,49]. In this study, we used the aromatic heterocyclic chemical structures of nucleotide basic units of the DNA molecule purine with its fused sixand five-membered ring conformation; and pyrimidine, its six-membered ring to generate features from cough sound signal segments.…”

Section: Dna Patternmentioning

confidence: 99%

Automated COVID-19 and Heart Failure Detection Using DNA Pattern Technique with Cough Sounds

et al. 2021

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COVID-19 and heart failure (HF) are common disorders and although they share some similar symptoms, they require different treatments. Accurate diagnosis of these disorders is crucial for disease management, including patient isolation to curb infection spread of COVID-19. In this work, we aim to develop a computer-aided diagnostic system that can accurately differentiate these three classes (normal, COVID-19 and HF) using cough sounds. A novel handcrafted model was used to classify COVID-19 vs. healthy (Case 1), HF vs. healthy (Case 2) and COVID-19 vs. HF vs. healthy (Case 3) automatically using deoxyribonucleic acid (DNA) patterns. The model was developed using the cough sounds collected from 241 COVID-19 patients, 244 HF patients, and 247 healthy subjects using a hand phone. To the best our knowledge, this is the first work to automatically classify healthy subjects, HF and COVID-19 patients using cough sounds signals. Our proposed model comprises a graph-based local feature generator (DNA pattern), an iterative maximum relevance minimum redundancy (ImRMR) iterative feature selector, with classification using the k-nearest neighbor classifier. Our proposed model attained an accuracy of 100.0%, 99.38%, and 99.49% for Case 1, Case 2, and Case 3, respectively. The developed system is completely automated and economical, and can be utilized to accurately detect COVID-19 versus HF using cough sounds.

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Graph neural network: Current state of Art, challenges and applications

Cited by 28 publications

References 8 publications

Rossmann-toolbox: a deep learning-based protocol for the prediction and design of cofactor specificity in Rossmann-fold proteins

Rossmann-toolbox: a deep learning-based protocol for the prediction and design of cofactor specificity in Rossmann-fold proteins

Graph Neural Networks in IoT: A Survey

Automated COVID-19 and Heart Failure Detection Using DNA Pattern Technique with Cough Sounds

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