RNDEtree: Regulatory Network With Differential Equation Based on Flexible Neural Tree With Novel Criterion Function

Yang, Bin; Bao, Wenzheng

doi:10.1109/access.2019.2913084

Cited by 9 publications

(6 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nowadays, lots of machine-learning methods have been utilized to solve biomedical problems [25][26][27][28][29]. However, it is very difficult to detect somatic mutations accurately from the massive sequencing data.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network for Somatic Mutation Classification

Wang

2021

Scientific Programming

View full text Add to dashboard Cite

The detection and characterization of somatic mutations have become the important means to analyze the occurrence and development of cancer and, ultimately, will help to select effective and precise treatment for specific cancer patients. It is very difficult to detect somatic mutations accurately from the massive sequencing data. In this paper, a forest-graph-embedded deep feed-forward network (forgeNet) is utilized to detect somatic mutations from the sequencing data. In forgeNet, the random forest (RF) or Gradient Boosting Machine (GBM) and graph-embedded deep feed-forward network (GEDFN) are utilized to extract features and implement classification, respectively. Three real somatic mutation datasets collected from 48 triple-negative breast cancers are utilized to test the somatic mutation detection performances of forgeNet. The detection results show that forgeNet could make the 0.05%–0.424% improvements in terms of area under the curve (AUC) compared with support vector machines and random forest.

show abstract

Section: Introductionmentioning

confidence: 99%

Deep Neural Network for Somatic Mutation Classification

Wang

2021

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…To address this issue, it is proposed to use the hybrid-methods that combine the statistical methods to design a static model and the differential equation methods for a dynamic model together [31][32][33]. In recent years, machine learning algorithms have been used to infer regulatory networks using omics datasets and single-cell data [34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Integrated Inference of Asymmetric Protein Interaction Networks Using Dynamic Model and Individual Patient Proteomics Data

et al. 2021

View full text Add to dashboard Cite

Recent advances in experimental biology studies have produced large amount of molecular activity data. In particular, individual patient data provide non-time series information for the molecular activities in disease conditions. The challenge is how to design effective algorithms to infer regulatory networks using the individual patient datasets and consequently address the issue of network symmetry. This work is aimed at developing an efficient pipeline to reverse-engineer regulatory networks based on the individual patient proteomic data. The first step uses the SCOUT algorithm to infer the pseudo-time trajectory of individual patients. Then the path-consistent method with part mutual information is used to construct a static network that contains the potential protein interactions. To address the issue of network symmetry in terms of undirected symmetric network, a dynamic model of ordinary differential equations is used to further remove false interactions to derive asymmetric networks. In this work a dataset from triple-negative breast cancer patients is used to develop a protein-protein interaction network with 15 proteins.

show abstract

“…Multiple computational methods can be used to learn GRNs from observational data, including correlation analysis [11], Boolean networks [12], Bayesian networks [13,14], and differential equation models [15,16]. Bayesian network approaches provide a good trade-off between the scalability and interpretability of discovered networks but do not allow directed cycles, rendering it impossible for them to model feedback loops.…”

Section: Introductionmentioning

confidence: 99%

Discovering gene regulatory networks of multiple phenotypic groups using dynamic Bayesian networks

Suter

Kuipers

Beerenwinkel

2021

Preprint

View full text Add to dashboard Cite

Dynamic Bayesian networks (DBNs) can be used for the discovery of gene regulatory networks from time series gene expression data. Here, we suggest a strategy for learning DBNs from gene expression data by employing a Bayesian approach that is scalable to large networks and is targeted at learning models with high predictive accuracy. Our framework can be used to learn DBNs for multiple groups of samples and highlight differences and similarities in their gene regulatory networks. We learn these DBN models based on different structural and parametric assumptions and select the optimal model based on the cross-validated predictive accuracy. We show in simulation studies that our approach is better equipped to prevent overfitting than techniques used in previous studies. We applied the proposed DBN-based classification approach to two time series transcriptomic datasets from the Gene Expression Omnibus database, each comprising data from distinct phenotypic groups of the same tissue type. In the first case, we used DBNs to characterize responders and non-responders to anti-cancer therapy. In the second case, we compared normal to tumor cells of colorectal tissue. The classification accuracy reached by the DBN-based classifier for both datasets was higher than reported previously. For the colorectal cancer dataset, our analysis suggested that GRNs for cancer and normal tissues have a lot of differences, which are most pronounced in the neighborhoods of oncogenes and known cancer tissue markers. The identified differences in gene networks of cancer and normal cells may be used for the discovery of targeted therapies.

show abstract

RNDEtree: Regulatory Network With Differential Equation Based on Flexible Neural Tree With Novel Criterion Function

Cited by 9 publications

References 45 publications

Deep Neural Network for Somatic Mutation Classification

Deep Neural Network for Somatic Mutation Classification

Integrated Inference of Asymmetric Protein Interaction Networks Using Dynamic Model and Individual Patient Proteomics Data

Discovering gene regulatory networks of multiple phenotypic groups using dynamic Bayesian networks

Contact Info

Product

Resources

About