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A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
Published on: May 27, 2021
MGANSL: multi-network representation generating with generative adversarial network for synthetic lethality
Jinxin Li1, Xinguo Lu2, Zihao Li1
1College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan, China.
Background:
Cancer is a complex disease that arises from the simultaneous mutations of multiple biological molecules. An effective therapeutic strategy is to exploit synthetic lethality (SL) by targeting the SL partner of cancer driver genes. Computational approaches have emerged as efficient complements to traditional methods. Although some methods integrate heterogeneous sources to learn multi-network representations, they often neglect consistent information shared across different networks and specific characteristic specific to individual network. Therefore, a comprehensive representation learning framework for capturing both multi-network consistency and network-specific information of gene pair is needed.
Results:
We proposed a novel approach capturing Multi-network consistent and specific representation with Generative Adversarial Network for Synthetic Lethality prediction (MGANSL). MGANSL employs network-aligned and network-specific encoding modules to cooperatively learn comprehensive multi-network representations of gene pair. In particular, network-aligned encoding module can capture cross-modal consistent information via cross-network adversarial generation, and network-specific encoding module can capture single network specific information via intra-network adversarial generation.
Conclusions:
Comprehensive experiments conducted on two human synthetic lethality datasets demonstrate the superiority of proposed method in SL prediction. Moreover, the novel predicted SL associations could aid in designing anti-cancer drugs and providing potential drug targets.
Insights
This study introduces MGANSL, a novel computational method for predicting synthetic lethality (SL) by integrating multi-network gene pair information. MGANSL enhances anti-cancer drug discovery by identifying potential drug targets through superior SL prediction.
Area of Science:
- Computational Biology
- Genomics
- Bioinformatics
Background:
- Cancer arises from multiple mutations, necessitating targeted therapies like synthetic lethality (SL).
- SL involves targeting cancer driver genes with their synthetic lethal partners.
- Existing computational methods for SL prediction often fail to integrate consistent and specific information across multiple biological networks.
Purpose of the Study:
- To develop a comprehensive representation learning framework for gene pairs that captures both multi-network consistency and network-specific information.
- To improve the accuracy of synthetic lethality prediction using computational approaches.
Main Methods:
- Proposed MGANSL (Multi-network consistent and specific representation with Generative Adversarial Network for Synthetic Lethality prediction).
- Employed network-aligned and network-specific encoding modules for comprehensive multi-network representations.
- Utilized cross-network and intra-network adversarial generation to capture consistent and specific gene pair information.
Main Results:
- MGANSL effectively captures both consistent cross-network and specific intra-network information for gene pairs.
- Demonstrated the superiority of MGANSL in synthetic lethality prediction through experiments on human datasets.
- Identified novel synthetic lethal associations with potential for anti-cancer drug development.
Conclusions:
- The proposed MGANSL method significantly outperforms existing approaches in synthetic lethality prediction.
- The identified synthetic lethal associations can guide the development of novel anti-cancer therapeutics.
- MGANSL provides a promising computational tool for identifying potential drug targets in cancer treatment.
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