Mining influential genes based on deep learning

Kong, Lingpeng; Chen, Yuanyuan; Xu, Fengjiao; Xu, Mei‐Ling; Li, Zutan; Fang, Jingya; Zhang, Liangyun; Pian, Cong

doi:10.1186/s12859-021-03972-5

Cited by 9 publications

(7 citation statements)

References 34 publications

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“…Aiming to interpret models with biological meanings, we applied a published package named DeepLIFT which was previously used in biology field to interpret the models [ 6 , 15 , 17 , 18 ]. We selected all true positive sequences (genes were correctly predicted as expressed) in three stages for interpretation.…”

Section: Resultsmentioning

confidence: 99%

Interpretation of convolutional neural networks reveals crucial sequence features involving in transcription during fiber development

et al. 2022

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Background Upland cotton provides the most natural fiber in the world. During fiber development, the quality and yield of fiber were influenced by gene transcription. Revealing sequence features related to transcription has a profound impact on cotton molecular breeding. We applied convolutional neural networks to predict gene expression status based on the sequences of gene transcription start regions. After that, a gradient-based interpretation and an N-adjusted kernel transformation were implemented to extract sequence features contributing to transcription. Results Our models had approximate 80% accuracies, and the area under the receiver operating characteristic curve reached over 0.85. Gradient-based interpretation revealed 5' untranslated region contributed to gene transcription. Furthermore, 6 DOF binding motifs and 4 transcription activator binding motifs were obtained by N-adjusted kernel-motif transformation from models in three developmental stages. Apart from 10 general motifs, 3 DOF5.1 genes were also detected. In silico analysis about these motifs’ binding proteins implied their potential functions in fiber formation. Besides, we also found some novel motifs in plants as important sequence features for transcription. Conclusions In conclusion, the N-adjusted kernel transformation method could interpret convolutional neural networks and reveal important sequence features related to transcription during fiber development. Potential functions of motifs interpreted from convolutional neural networks could be validated by further wet-lab experiments and applied in cotton molecular breeding.

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Section: Resultsmentioning

confidence: 99%

Interpretation of convolutional neural networks reveals crucial sequence features involving in transcription during fiber development

et al. 2022

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“…More recently, non-parametric machine learning models, particularly deep neural networks, have been applied to more complex pan-cancer data analysis, such as multi-omics cancer type classification and survival prediction. For example, Kong et al . (2021) used an autoencoder and a backward propagation-based neuron importance assignment method to capture nonlinear relationships among genes to develop a robust cancer type classification that outperformed L1000 expression profiling, a reduced representation of ∼1000 genes whose expression is highly representative ( Subramanian et al .…”

Section: Related Workmentioning

confidence: 99%

Interpretable meta-learning of multi-omics data for survival analysis and pathway enrichment

et al. 2023

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Motivation Despite the success of recent machine learning algorithms’ applications to survival analysis, their black-box nature hinders interpretability, which is arguably the most important aspect. Similarly, multi-omics data integration for survival analysis is often constrained by the underlying relationships and correlations that are rarely well understood. The goal of this work is to alleviate the interpretability problem in machine learning approaches for survival analysis and also demonstrate how multi-omics data integration improves survival analysis and pathway enrichment. We use meta-learning, a machine learning algorithm that is trained on a variety of related datasets and allows quick adaptations to new tasks, to perform survival analysis and pathway enrichment on pan-cancer datasets. In recent machine learning research, meta-learning has been effectively used for knowledge transfer among multiple related datasets. Results We use meta-learning with cox hazard loss to show that the integration of TCGA pan-cancer data increases the performance of survival analysis. We also apply advanced model interpretability method called DeepLIFT (Deep Learning Important FeaTures) to show different sets of enriched pathways for multi-omics and transcriptomics data. Our results show that multi-omics cancer survival analysis enhances performance compared with using transcriptomics or clinical data alone. Additionally, we show a correlation between variable importance assignment from DeepLIFT and gene co-enrichment, suggesting that genes with higher and similar contribution scores are more likely to be enriched together in the same enrichment sets. Availability https://github.com/berkuva/TCGA-omics-integration Supplementary information Supplementary data are available at Bioinformatics online.

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“…Another popular interpretation method is DeepLIFT [ 197 ], which calculates the contribution of neurons in a trained neural network by evaluating the difference in activation from a chosen representative reference. DeepLIFT has also been useful for interpreting model prediction in genomic datasets [ 198 , 199 , 200 , 201 , 202 , 203 ]. Another interpretive model is SHapley Additive exPlanations (SHAP) [ 204 ], which is based on the Shapley value from game theory.…”

Section: Major Challenges For Clinical Utility Of Complex and Data-dr...mentioning

confidence: 99%

Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications

Patterson

Elbasir

Tian

et al. 2023

Cancers

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Since the rise of next-generation sequencing technologies, the catalogue of mutations in cancer has been continuously expanding. To address the complexity of the cancer-genomic landscape and extract meaningful insights, numerous computational approaches have been developed over the last two decades. In this review, we survey the current leading computational methods to derive intricate mutational patterns in the context of clinical relevance. We begin with mutation signatures, explaining first how mutation signatures were developed and then examining the utility of studies using mutation signatures to correlate environmental effects on the cancer genome. Next, we examine current clinical research that employs mutation signatures and discuss the potential use cases and challenges of mutation signatures in clinical decision-making. We then examine computational studies developing tools to investigate complex patterns of mutations beyond the context of mutational signatures. We survey methods to identify cancer-driver genes, from single-driver studies to pathway and network analyses. In addition, we review methods inferring complex combinations of mutations for clinical tasks and using mutations integrated with multi-omics data to better predict cancer phenotypes. We examine the use of these tools for either discovery or prediction, including prediction of tumor origin, treatment outcomes, prognosis, and cancer typing. We further discuss the main limitations preventing widespread clinical integration of computational tools for the diagnosis and treatment of cancer. We end by proposing solutions to address these challenges using recent advances in machine learning.

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Mining influential genes based on deep learning

Cited by 9 publications

References 34 publications

Interpretation of convolutional neural networks reveals crucial sequence features involving in transcription during fiber development

Interpretation of convolutional neural networks reveals crucial sequence features involving in transcription during fiber development

Interpretable meta-learning of multi-omics data for survival analysis and pathway enrichment

Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications

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