A scalable random walk with restart on heterogeneous networks with Apache Spark for ranking disease-related genes through type-II fuzzy data fusion

Joodaki, Mehdi; Ghadiri, Nasser; Maleki, Zeinab; Shahreza, Maryam Lotfi

doi:10.1016/j.jbi.2021.103688

Cited by 12 publications

(7 citation statements)

References 38 publications

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“…With the uptake of graph representation learning in biomedicine 27 , novel options exist to process networks alongside the input features in a joint ML model, thus approaching an in silico representation of biological regulation. First implementations based on random-walks [28][29][30][31][32][33][34][35][36][37] or graph neural networks (GNN) [38][39][40][41][42][43] show promise in predicting 'disease genes', but are often disease specific, depend on hard-coded and partially biased input data, and do not further explore the properties of predicted (core) genes (Extended Data Fig. 2).…”

Section: Note 1)mentioning

confidence: 99%

Speos: An ensemble graph representation learning framework to predict core genes for complex diseases

Ratajczak

Joblin

Hildebrandt

et al. 2023

Preprint

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Understanding phenotype-to-genotype relationships is a grand challenge of 21st century biology with translational implications. The recently proposed 'omnigenic' model postulates that effects of genetic variation on traits are mediated by core-genes and -proteins whose activities mechanistically influence the phenotype, whereas peripheral genes encode a regulatory network that indirectly affects phenotypes via core gene products. We have developed a positive-unlabeled graph representation-learning ensemble-approach to predict core genes for diverse diseases using Mendelian disorder genes for training. Employing mouse knockout phenotypes for external validation, we demonstrate that our most confident predictions validate at rates on par with the Mendelian disorder genes, and all candidates exhibit core-gene properties like transcriptional deregulation in diseases and loss-of-function intolerance. Predicted candidates are enriched for drug targets and druggable proteins and, in contrast to Mendelian disorder genes, also for druggable but yet untargeted gene products. Model interpretation suggests key molecular mechanisms and physical interactions for core gene predictions. Our results demonstrate the potential of graph representation learning and pave the way for studying core gene properties and future drug development.

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Section: Note 1)mentioning

confidence: 99%

Speos: An ensemble graph representation learning framework to predict core genes for complex diseases

Ratajczak

Joblin

Hildebrandt

et al. 2023

Preprint

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show abstract

“…Table 1 reports a summary of the main features of the tools proposed in these articles. The table shows that apart from some tools that reports tests only on a multi-core workstation ( [16] , [17] , [18] , [19] ), Spark has been widely used to implement tools aimed at parallelizing the computation on a distributed computing environment. Most of these tools have been specifically devised for, or tested on, a cloud environment ( [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] [29] , [30] , [31] , [32] [33] , [34] , [35] , [36] , [37] ).…”

Section: Apache Spark In Life Sciencesmentioning

confidence: 99%

“…In 2021, Joodaki et al [19] proposed RWRHN-FF a novel algorithm aimed at finding genes involved in relevant phenotypes. Results obtained using gene interaction networks are often not accurate due to the high number of false positives.…”

Section: Apache Spark In Life Sciencesmentioning

confidence: 99%

Framing Apache Spark in life sciences

Manconi¹,

Gnocchi²,

Milanesi³

et al. 2023

Heliyon

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“…Joodaki et al 32 integrated multiple protein/gene networks to overcome the false positive interaction prediction. They built a heterogeneous network based on gene-gene associations, disease–disease associations, and disease–gene associations.…”

Section: Related Workmentioning

confidence: 99%

“…As mentioned above, the current studies have several limitations, summarized in the following points. First, most studies have developed methods to predict the genes related to diseases, but a few of these methods were designed for PD gene prediction 18 – 22 , 32 , 35 . Second, some of these PD methods identified only protein genes related to PD and ignored lncRNA genes, although lncRNAs are critical for improving our understanding and diagnosing different diseases 17 , 23 – 25 .…”

Section: Related Workmentioning

confidence: 99%

Predicting Parkinson disease related genes based on PyFeat and gradient boosted decision tree

Helmy

El-Daydamony

Mekky

et al. 2022

Sci Rep

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Identifying genes related to Parkinson’s disease (PD) is an active research topic in biomedical analysis, which plays a critical role in diagnosis and treatment. Recently, many studies have proposed different techniques for predicting disease-related genes. However, a few of these techniques are designed or developed for PD gene prediction. Most of these PD techniques are developed to identify only protein genes and discard long noncoding (lncRNA) genes, which play an essential role in biological processes and the transformation and development of diseases. This paper proposes a novel prediction system to identify protein and lncRNA genes related to PD that can aid in an early diagnosis. First, we preprocessed the genes into DNA FASTA sequences from the University of California Santa Cruz (UCSC) genome browser and removed the redundancies. Second, we extracted some significant features of DNA FASTA sequences using the PyFeat method with the AdaBoost as feature selection. These selected features achieved promising results compared with extracted features from some state-of-the-art feature extraction techniques. Finally, the features were fed to the gradient-boosted decision tree (GBDT) to diagnose different tested cases. Seven performance metrics were used to evaluate the performance of the proposed system. The proposed system achieved an average accuracy of 78.6%, the area under the curve equals 84.5%, the area under precision-recall (AUPR) equals 85.3%, F1-score equals 78.3%, Matthews correlation coefficient (MCC) equals 0.575, sensitivity (SEN) equals 77.1%, and specificity (SPC) equals 80.2%. The experiments demonstrate promising results compared with other systems. The predicted top-rank protein and lncRNA genes are verified based on a literature review.

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A scalable random walk with restart on heterogeneous networks with Apache Spark for ranking disease-related genes through type-II fuzzy data fusion

Cited by 12 publications

References 38 publications

Speos: An ensemble graph representation learning framework to predict core genes for complex diseases

Speos: An ensemble graph representation learning framework to predict core genes for complex diseases

Framing Apache Spark in life sciences

Predicting Parkinson disease related genes based on PyFeat and gradient boosted decision tree

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