Application of deep learning methods in biological networks

Jin, Shilei; Zeng, Xiangxiang; Xia, Feng; Huang, Wei; Liu, Xiangrong

doi:10.1093/bib/bbaa043

Cited by 140 publications

(70 citation statements)

References 49 publications

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“…methods [26,27,33] and instance-based learning methods such as K Nearest neighbor (K-NN) and Weighted KNN (WKNN) [34] have been used for gene essentiality prediction. Deep Learning strategies based on multilayer perceptron networks have also been used for essential gene prediction [24,35]. In these studies, researchers have mostly opted for simpler optimization methods for parameter tuning, such as the grid search technique, where the entire parameter space is explored in all possible combinations.…”

Section: Plos Onementioning

confidence: 99%

Essential gene prediction using limited gene essentiality information–An integrative semi-supervised machine learning strategy

2020

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Essential gene prediction helps to find minimal genes indispensable for the survival of any organism. Machine learning (ML) algorithms have been useful for the prediction of gene essentiality. However, currently available ML pipelines perform poorly for organisms with limited experimental data. The objective is the development of a new ML pipeline to help in the annotation of essential genes of less explored disease-causing organisms for which minimal experimental data is available. The proposed strategy combines unsupervised feature selection technique, dimension reduction using the Kamada-Kawai algorithm, and semi-supervised ML algorithm employing Laplacian Support Vector Machine (LapSVM) for prediction of essential and non-essential genes from genome-scale metabolic networks using very limited labeled dataset. A novel scoring technique, Semi-Supervised Model Selection Score, equivalent to area under the ROC curve (auROC), has been proposed for the selection of the best model when supervised performance metrics calculation is difficult due to lack of data. The unsupervised feature selection followed by dimension reduction helped to observe a distinct circular pattern in the clustering of essential and non-essential genes. LapSVM then created a curve that dissected this circle for the classification and prediction of essential genes with high accuracy (auROC > 0.85) even with 1% labeled data for model training. After successful validation of this ML pipeline on both Eukaryotes and Prokaryotes that show high accuracy even when the labeled dataset is very limited, this strategy is used for the prediction of essential genes of organisms with inadequate experimentally known data, such as Leishmania sp. Using a graph-based semi-supervised machine learning scheme, a novel integrative approach has been proposed for essential gene prediction that shows universality in application to both Prokaryotes and Eukaryotes with limited labeled data. The essential genes predicted using the pipeline provide an important lead for the prediction of gene essentiality and identification of novel therapeutic targets for antibiotic and vaccine development against disease-causing parasites.

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Section: Plos Onementioning

confidence: 99%

Essential gene prediction using limited gene essentiality information–An integrative semi-supervised machine learning strategy

2020

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“…Sort these nodes based on the influence value. (5) Step 5: With the node with the highest combined influence value with the existing seed nodes, add it to the set of seed nodes. (6) Step 6: Repeat steps 4 and 5 until all s seeds are selected.…”

Section: The Influence Maximization Problemmentioning

confidence: 99%

“…The increase in biomedical articles and the formation of various biomolecule interaction databases enable us to obtain diverse biological networks. These biological networks provide a wealth of raw materials for further understanding of biological systems, the discovery of complex diseases, and the search for therapeutic drugs [5]. Both text mining and network analysis have been applied to find the hidden biological knowledge and gene regulation rules behind the huge amount of information [6].…”

Section: Introductionmentioning

confidence: 99%

Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization

Wang

Jin

Chang

et al. 2020

BMC Med Inform Decis Mak

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Background: Gastrointestinal (GI) cancer including colorectal cancer, gastric cancer, pancreatic cancer, etc., are among the most frequent malignancies diagnosed annually and represent a major public health problem worldwide. Methods: This paper reports an aided curation pipeline to identify potential influential genes for gastrointestinal cancer. The curation pipeline integrates biomedical literature to identify named entities by Bi-LSTM-CNN-CRF methods. The entities and their associations can be used to construct a graph, and from which we can compute the sets of cooccurring genes that are the most influential based on an influence maximization algorithm. Results: The sets of co-occurring genes that are the most influential that we discover include RARA-CRBP1, CASP3-BCL2, BCL2-CASP3-CRBP1, RARA-CASP3-CRBP1, FOXJ1-RASSF3-ESR1, FOXJ1-RASSF1A-ESR1, FOXJ1-RASS F1A-TNFAIP8-ESR1. With TCGA and functional and pathway enrichment analysis, we prove the proposed approach works well in the context of gastrointestinal cancer. Conclusions: Our pipeline that uses text mining to identify objects and relationships to construct a graph and uses graph-based influence maximization to discover the most influential co-occurring genes presents a viable direction to assist knowledge discovery for clinical applications.

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“…Recent progress in machine learning has fueled interest in whether such methods could facilitate the discovery and analysis of biological networks [12], [13]. Pioneering applications of deep neural networks (DNNs) in genomics include prediction of TF binding sites [14]), and the effects of non-coding genetic variants [15].…”

Section: Introductionmentioning

confidence: 99%

White-box Deep Neural Network Prediction of Genome-Wide Transcriptome Signatures

Magnusson¹,

Jn²,

Gustafsson³

2021

Preprint

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Prediction algorithms for protein or gene structures, including transcription factor binding from sequence information, have been transformative in understanding gene regulation. Here we ask whether human transcriptomic profiles can be predicted solely from the expression of transcription factors (TFs). To this end, we explore whether a neural network (NN) could predict the transcriptome from TFs. Using at least one hidden layer, we find that the expression of 1,600 TFs can explain >95% of variance in 25,000 genes. Using the light-up technique to inspect the trained NN, we find an overrepresentation of known TF-gene regulations. Furthermore, the learned prediction network has a hierarchical organization. A smaller set of around 125 core TFs could explain close to 80% of the variance. Interestingly, reducing the number of TFs below 500 induces a rapid decline in prediction performance. Next, we evaluated the prediction model using transcriptional data from 22 human diseases. The TFs were sufficient to predict the target genes' dysregulation (rho=0.61, P < 10^-216). By inspecting the model, key causative TFs could be extracted for subsequent validation using disease-associated genetic variants. In conclusion, we demonstrate the construction of an interpretable neural network predictor. Analysis of the predictors revealed key TFs that were inducing transcriptional changes during disease.

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Application of deep learning methods in biological networks

Cited by 140 publications

References 49 publications

Essential gene prediction using limited gene essentiality information–An integrative semi-supervised machine learning strategy

Essential gene prediction using limited gene essentiality information–An integrative semi-supervised machine learning strategy

Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization

White-box Deep Neural Network Prediction of Genome-Wide Transcriptome Signatures

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