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Identification of synthetic lethality based on a functional network by using machine learning algorithms

JiaRui Li1, Lin Lu2, Yu-Hang Zhang3

  • 1School of Life Sciences, Shanghai University, Shanghai, China.

Journal of Cellular Biochemistry
|August 21, 2018
PubMed

Insights

This study introduces a computational method to predict synthetic lethal interactions, crucial for cancer therapy. The approach uses machine learning and gene function features to identify these interactions, aiding future research.

Area of Science:

  • Computational biology
  • Genomics
  • Cancer research

Background:

  • Synthetic lethality, the combination of mutations causing cell death, is a promising avenue for cancer therapy.
  • Systematic identification of synthetic lethal interactions is challenging due to complex influencing factors.
  • Advances in RNA interference (RNAi) and CRISPR/Cas9 gene editing facilitate the search for synthetic lethal interactions.

Purpose of the Study:

  • To develop a novel computational method for predicting synthetic lethal interactions.
  • To identify critical functional features that accurately predict synthetic lethality.
  • To provide a basis for further characterization of synthetic lethal genetic combinations.

Main Methods:

  • Encoding protein-coding genes using gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for functional representation.
  • Utilizing machine learning algorithms, including a random forest-based prediction engine.
  • Selecting 2120 features to build the predictive model.

Main Results:

  • Achieved a Matthews correlation coefficient (MCC) of 0.532 in predicting synthetic lethal interactions.
  • Identified top 15 features, many of which show potential roles in synthetic lethality based on prior research.
  • Demonstrated the efficacy of the proposed computational method in predicting synthetic lethal interactions.

Conclusions:

  • The developed computational method accurately predicts synthetic lethal interactions.
  • The identified functional features offer insights into the mechanisms underlying synthetic lethality.
  • This work provides a foundation for future investigations into synthetic lethality in cancer.
Keywords:
maximum relevance and minimum redundancy (mRMR)random forest (RF)synthetic lethality

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