Biological Knowledge Guided Deep Neural Network for Brain Genotype-Phenotype Association Study

AbstractBreast cancer is a heterogeneous disease. To guide proper treatment decisions for each patient, robust prognostic biomarkers, which allow reliable prognosis prediction, are necessary. Gene feature selection based on microarray data is an approach to discover potential biomarkers systematically. However, standard pure-statistical feature selection approaches often fail to incorporate prior biological knowledge and select genes that lack biological insights. Besides, due to the high dimensionality and low sample size properties of microarray data, selecting robust gene features is an intrinsically challenging problem. We hence combined systems biology feature selection with ensemble learning in this study, aiming to select genes with biological insights and robust prognostic predictive power. Moreover, to capture breast cancer's complex molecular processes, we adopted a multi-gene approach to predict the prognosis status using deep learning classifiers. We found that all ensemble approaches could improve feature selection robustness, wherein the hybrid ensemble approach led to the most robust result. Among all prognosis prediction models, the bimodal deep neural network (DNN) achieved the highest test performance, further verified by survival analysis. In summary, this study demonstrated the potential of combining ensemble learning and bimodal DNN in guiding precision medicine.

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Pathway-guided deep neural network toward interpretable and predictive modeling of drug sensitivity

10.1101/2020.02.06.930503 ◽

2020 ◽

Author(s):

Lei Deng ◽

Yideng Cai ◽

Wenhao Zhang ◽

Wenyi Yang ◽

Bo Gao ◽

...

Keyword(s):

Neural Network ◽

Cancer Cells ◽

Drug Targets ◽

Deep Neural Network ◽

Drug Sensitivity ◽

Predictive Ability ◽

Biological Knowledge ◽

Data Sets ◽

Source Codes ◽

Sensitivity Data

AbstractMotivationTo efficiently save cost and reduce risk in drug research and development, there is a pressing demand to develop in-silico methods to predict drug sensitivity to cancer cells. With the exponentially increasing number of multi-omics data derived from high-throughput techniques, machine learning-based methods have been applied to the prediction of drug sensitivities. However, these methods have drawbacks either in the interpretability of mechanism of drug action or limited performance in modeling drug sensitivity.ResultsIn this paper, we presented a pathway-guided deep neural network model, referred to as pathDNN, to predict the drug sensitivity to cancer cells. Biological pathways describe a group of molecules in a cell that collaborates to control various biological functions like cell proliferation and death, thereby abnormal function of pathways can result in disease. To make advantage of both the excellent predictive ability of deep neural network and the biological knowledge of pathways, we reshape the canonical DNN structure by incorporating a layer of pathway nodes and their connections to input gene nodes, which makes the DNN model more interpretable and predictive compared to canonical DNN. We have conducted extensive performance evaluations on multiple independent drug sensitivity data sets, and demonstrate that pathDNN significantly outperformed canonical DNN model and seven other classical regression models. Most importantly, we observed remarkable activity decreases of disease-related pathway nodes during forward propagation upon inputs of drug targets, which implicitly corresponds to the inhibition effect of disease-related pathways induced by drug treatment on cancer cells. Our empirical experiments show that pathDNN achieves pharmacological interpretability and predictive ability in modeling drug sensitivity to cancer cells.AvailabilityThe web server, as well as the processed data sets and source codes for reproducing our work, is available at http://pathdnn.denglab.org

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Modeling transcriptional profiles of gene perturbation with deep neural network

10.1101/2021.07.15.452534 ◽

2021 ◽

Author(s):

Wenke Liu ◽

Xuya Wang ◽

D R Mani ◽

David Fenyo

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Expression Profiles ◽

Single Gene ◽

Gene Expression Profiles ◽

Network Models ◽

Biological Knowledge ◽

Neural Network Models ◽

Perturbation Data ◽

Gene Perturbation

Cell line perturbation data could be utilized as a reference for inferring underlying molecular processes in new gene expression profiles. It is important to develop accurate and computationally efficient algorithms to exploit biological knowledge in the growing compendium of existing perturbation data and harness these for new predictions. We reframed the problem of inferring possible gene perturbation based on a reference perturbation database into a classification task and evaluated the application of deep neural network models to address this problem. Our results showed that a fully-connected multi-layer neural network was able to achieve up to 74.9% accuracy in a holdout test set, but the model generalizability was limited by consistency between training and testing data. Capacity and flexibility enables neural network models to efficiently represent transcriptomic features associated with single gene knockdown perturbations. With consistent signals between training and testing sets, neural networks may be trained to classify new samples to experimentally confirmed molecular phenotypes.

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Biological interpretation of deep neural network for phenotype prediction based on gene expression

BMC Bioinformatics ◽

10.1186/s12859-020-03836-4 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Blaise Hanczar ◽

Farida Zehraoui ◽

Tina Issa ◽

Mathieu Arles

Keyword(s):

Neural Network ◽

Gene Expression ◽

Deep Learning ◽

Gene Expression Data ◽

Deep Neural Network ◽

Expression Profiles ◽

Biological Knowledge ◽

Expression Data ◽

Phenotype Prediction ◽

Biological Interpretation

Abstract Background The use of predictive gene signatures to assist clinical decision is becoming more and more important. Deep learning has a huge potential in the prediction of phenotype from gene expression profiles. However, neural networks are viewed as black boxes, where accurate predictions are provided without any explanation. The requirements for these models to become interpretable are increasing, especially in the medical field. Results We focus on explaining the predictions of a deep neural network model built from gene expression data. The most important neurons and genes influencing the predictions are identified and linked to biological knowledge. Our experiments on cancer prediction show that: (1) deep learning approach outperforms classical machine learning methods on large training sets; (2) our approach produces interpretations more coherent with biology than the state-of-the-art based approaches; (3) we can provide a comprehensive explanation of the predictions for biologists and physicians. Conclusion We propose an original approach for biological interpretation of deep learning models for phenotype prediction from gene expression data. Since the model can find relationships between the phenotype and gene expression, we may assume that there is a link between the identified genes and the phenotype. The interpretation can, therefore, lead to new biological hypotheses to be investigated by biologists.

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Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction

10.21203/rs.3.rs-208766/v1 ◽

2021 ◽

Author(s):

Li-Hsin Cheng ◽

Te-Cheng Hsu ◽

Che Lin

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Feature Selection ◽

Systems Biology ◽

Ensemble Learning ◽

Microarray Data ◽

Deep Neural Network ◽

Prediction Models ◽

Biological Knowledge ◽

Prognosis Prediction

Abstract Breast cancer is a heterogeneous disease. To guide proper treatment decisions for each patient, robust prognostic biomarkers, which allow reliable prognosis prediction, are necessary. Gene feature selection based on microarray data is an approach to discover potential biomarkers systematically. However, standard pure-statistical feature selection approaches often fail to incorporate prior biological knowledge and select genes that lack biological insights. Besides, due to the high dimensionality and low sample size properties of microarray data, selecting robust gene features is an intrinsically challenging problem. We hence combined systems biology feature selection with ensemble learning in this study, aiming to select genes with biological insights and robust prognostic, predictive power. Moreover, to capture breast cancer's complex molecular processes, we adopted a multi-gene approach to predict the prognosis status using deep learning classifiers. We found that all ensemble approaches could improve feature selection robustness, wherein the hybrid ensemble approach led to the most robust result. Among all prognosis prediction models, the bimodal deep neural network (DNN) achieved the highest test performance, further verified by survival analysis. In summary, this study demonstrated the potential of combining ensemble learning and bimodal DNN in guiding precision medicine.

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Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction

10.1101/810176 ◽

2019 ◽

Author(s):

Li-Hsin Cheng ◽

Che Lin

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Feature Selection ◽

Systems Biology ◽

Ensemble Learning ◽

Microarray Data ◽

Deep Neural Network ◽

Biological Knowledge ◽

Prognosis Prediction ◽

Gene Feature

AbstractMotivationBreast cancer is a heterogeneous disease. In order to guide proper treatment decisions for each individual patient, there is an urgent need for robust prognostic biomarkers that allow reliable prognosis prediction. Gene feature selection on microarray data is an approach to systematically discover potential biomarkers. However, common pure-statistical feature selection approaches often fail to incorporate prior biological knowledge and thus tend to select genes that lack biological insights. In addition, due to the high dimensionality and low sample size properties of microarray data, selecting robust gene features is an intrinsically challenging problem. We therefore combined systems biology feature selection with ensemble learning in this study, aiming to address the above challenges and select genes with biological insights, as well as robust prognostic predictive power. Moreover, in order to capture the complex molecular processes of breast cancer, where multiple disease-contributing genes may exist and interact, we adopted a multi-gene approach to predict the prognosis status using machine learning classifiers.ResultsWe systematically evaluated three different ensemble approaches that all improved the original systems biology feature selector. We found that compared to the most popular data-perturbation approach, function perturbation can produce significant improvement with just a few ensembles. Among all, the hybrid ensemble approach led to the most robust feature selection result, and the identified genes were shown to be highly involved in pathways, such as ubiquitination and cell cycle. Final prognosis prediction models were constructed using the identified genes and clinical information as input features. Among all models, bimodal deep neural network (DNN) achieved the highest AUC (area under receiver operating characteristic curve) in test performance evaluation, where subsequent survival analysis also verified its ability to differentiate patients with different prognosis statuses. In summary, the study demonstrated the potential of ensemble learning to improve gene feature selection robustness, as well as the potential of bimodal DNN in providing reliable prognosis prediction and guiding precision medicine.

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