Predicting potential cancer genes by integrating network properties, sequence features and functional annotations

Liu, Wei; Xie, Hongwei

doi:10.1007/s11427-013-4500-6

Cited by 7 publications

(5 citation statements)

References 28 publications

(25 reference statements)

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“…Functional annotation (dbNSFP/WGSA framework) and recurrence in public (dbSNP142, 1000 Genomes, UK10K, ExAC) and in‐house databases were used to filter and prioritize the identified variants. Predicted functional impact of variants was assessed by Combined Annotation Dependent Depletion (CADD) (score >15.0) and Database for Nonsynonymous SNPs' Functional Predictions (dbNSFP) (SVM) (radial score >0.0). Only retained changes were prioritized on the basis of the functional relevance of genes using GeneDistiller (http://www.genedistiller.org.)…”

Section: Methodsmentioning

confidence: 99%

Mutations in the IRBIT domain of ITPR1 are a frequent cause of autosomal dominant nonprogressive congenital ataxia

et al. 2016

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Congenital ataxias are nonprogressive neurological disorders characterized by neonatal hypotonia, developmental delay and ataxia, variably associated with intellectual disability and other neurological or extraneurological features. We performed trio-based whole-exome sequencing of 12 families with congenital cerebellar and/or vermis atrophy in parallel with targeted next-generation sequencing of known ataxia genes (CACNA1A, ITPR1, KCNC3, ATP2B3 and GRM1) in 12 additional patients with a similar phenotype. Novel pathological mutations of ITPR1 (inositol 1,4,5-trisphosphate receptor, type 1) were found in seven patients from four families (4/24, ∼16.8%) all localized in the IRBIT (inositol triphosphate receptor binding protein) domain which plays an essential role in the regulation of neuronal plasticity and development. Our study expands the mutational spectrum of ITPR1-related congenital ataxia and indicates that ITPR1 gene screening should be implemented in this subgroup of ataxias.

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Section: Methodsmentioning

confidence: 99%

Mutations in the IRBIT domain of ITPR1 are a frequent cause of autosomal dominant nonprogressive congenital ataxia

et al. 2016

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“…Despite the existence of a large number of infectious diseases with diverse clinical and biochemical features, they have several commonalities, such as acute onset in most cases, transmissibility between the hosts, immune response patterns of the host and the response to antimicrobial agents, which prompted their classification as one broad entity. Similarly, different cancers were considered as a single entity and MLT was applied for the prediction of host genes related to cancer despite considerable variability [17]. Host response due to infection is distinct from non-infectious diseases and initiated by the engagement of microbe- or pathogen-associated molecular patterns (MAMPs or PAMPs) by the innate recognition receptors (for eg, Toll-like or NOD-like receptors).…”

Section: Discussionmentioning

confidence: 99%

“…These methods have utilized various biological information, such as gene co-expression, gene ontology (GO) annotation, protein-protein interaction (PPI) networks, domain, motif and sequence information etc. In addition, machine learning approaches using protein-protein interaction network properties, sequence and functional features were applied to identify cancer and Alzheimer disease-associated genes [17, 18]. However, no methods have been developed so far to predict the host genes associated with infectious diseases.…”

Section: Introductionmentioning

confidence: 99%

Identification of infectious disease-associated host genes using machine learning techniques

et al. 2019

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BackgroundWith the global spread of multidrug resistance in pathogenic microbes, infectious diseases emerge as a key public health concern of the recent time. Identification of host genes associated with infectious diseases will improve our understanding about the mechanisms behind their development and help to identify novel therapeutic targets.ResultsWe developed a machine learning techniques-based classification approach to identify infectious disease-associated host genes by integrating sequence and protein interaction network features. Among different methods, Deep Neural Networks (DNN) model with 16 selected features for pseudo-amino acid composition (PAAC) and network properties achieved the highest accuracy of 86.33% with sensitivity of 85.61% and specificity of 86.57%. The DNN classifier also attained an accuracy of 83.33% on a blind dataset and a sensitivity of 83.1% on an independent dataset. Furthermore, to predict unknown infectious disease-associated host genes, we applied the proposed DNN model to all reviewed proteins from the database. Seventy-six out of 100 highly-predicted infectious disease-associated genes from our study were also found in experimentally-verified human-pathogen protein-protein interactions (PPIs). Finally, we validated the highly-predicted infectious disease-associated genes by disease and gene ontology enrichment analysis and found that many of them are shared by one or more of the other diseases, such as cancer, metabolic and immune related diseases.ConclusionsTo the best of our knowledge, this is the first computational method to identify infectious disease-associated host genes. The proposed method will help large-scale prediction of host genes associated with infectious-diseases. However, our results indicated that for small datasets, advanced DNN-based method does not offer significant advantage over the simpler supervised machine learning techniques, such as Support Vector Machine (SVM) or Random Forest (RF) for the prediction of infectious disease-associated host genes. Significant overlap of infectious disease with cancer and metabolic disease on disease and gene ontology enrichment analysis suggests that these diseases perturb the functions of the same cellular signaling pathways and may be treated by drugs that tend to reverse these perturbations. Moreover, identification of novel candidate genes associated with infectious diseases would help us to explain disease pathogenesis further and develop novel therapeutics.

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“…A recent study identified the infectious disease-associated host genes from the sequence and PPI-based features and prioritize the important genes [20]. The network-based approaches were also used to prioritize the disease-associated genes for several diseases [21,22].…”

Section: Introductionmentioning

confidence: 99%

AlzGenPred: A CatBoost based method using network features to classify the Alzheimer’s Disease associated genes from the high throughput sequencing data

Shukla,

Singh

2023

Preprint

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Background and ObjectiveAD is a progressive neurodegenerative disorder characterized by memory loss. Due to the advancement in next-generation sequencing technologies, an enormous amount of AD-associated genomics data is available. However, the information about the involvement of these genes in AD association is still a research topic because all these algorithms are based on statistical techniques. Therefore, AlzGenPred is developed to identify the AD-associated genes from a large set of data.MethodsTo develop the AlzGenPred, we have compiled a benchmark dataset consisting of 1086 AD and non-AD genes and used them as positive and negative datasets. We have generated several features including the fused features and evaluated them through machine learning methods. Then hyperparameter tuning approach was also applied and the final model was selected. The proposed method was validated by using the AlzGene and transcriptomics datasets and proposed as a standalone tool.ResultsTotal 13504 features belonging to eight different encoding schemes of these sequences were generated and evaluated by using 16 ML algorithms. It reveals that network-based features can classify AD genes while sequence-based features are not able to classify them. Then we generated 24 different fused features (6020 D) using sequence-based features and fed them into a two-step lightGBM-based recursive feature selection method. It increased up to 5-7% accuracy. After that selected eight fused features with CKSAAP were used for the hyperparameter tuning. They showed <70% accuracy. Therefore, network-based features were used to generate the CatBoost-based ML method called AlzGenPred with 96.55% accuracy and 98.99% AUROC. The developed method is tested on the AlzGene dataset where it showed 96.43% accuracy. Then the model is validated using the transcriptomics dataset also.ConclusionThe validation of AlzGenPred using the AlzGene dataset and transcriptomics dataset obtained from Human, mouse, and ES-derived neural cells revealed that it can classify the omics data and can sort the AD-associated genes. These predicted genes can be directly used in the wet lab for further testing which will reduce labor cost and time expenses. The AlzGenPred is developed as a standalone package and is available for users athttps://www.bioinfoindia.org/alzgenpred/andhttps://github.com/shuklarohit815/AlzGenPred.

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Predicting potential cancer genes by integrating network properties, sequence features and functional annotations

Cited by 7 publications

References 28 publications

Mutations in the IRBIT domain of ITPR1 are a frequent cause of autosomal dominant nonprogressive congenital ataxia

Mutations in the IRBIT domain of ITPR1 are a frequent cause of autosomal dominant nonprogressive congenital ataxia

Identification of infectious disease-associated host genes using machine learning techniques

AlzGenPred: A CatBoost based method using network features to classify the Alzheimer’s Disease associated genes from the high throughput sequencing data

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