Similarity search in knowledge graphs using meta paths

Relational data in biological systems – such as the cellular interactome, single cell similarity graphs, gene co-expression networks, and patient interaction networks – can be represented by graph structures. Biological networks are often comprised of diverse data modalities; thus, they are poorly modeled by homogenously typed networks. Instead, interconnected objects from various modalities can be represented as a single multigraph with heterogeneous knowledge-informed node and edge types. We develop metapaths, an R software package to implement meta paths and perform meta path-based similarity search in biological knowledge graphs.

Meta paths are a general graph-theoretic approach for flexible similarity search in large networks. While they are widely used in biomedical network analysis, there is currently no package available in R that would offer a wide range of support for meta paths.

Meta paths are sequences of node types that define a walk from the origin node to the destination node. Informative metapaths in knowledge graphs (KGs) are often engineered by hand based on domain knowledge or expertise (e.g., the meta path DRS is clinically meaningful, since it describes associations between a disease and the side effects of its treatments, whereas the meta path PSF would not be). Alternatively, optimal meta paths can be discovered in an unsupervised fashion by feature selection metrics (e.g., maximal spanning tree, Laplacian score, or ranking based on meta path frequency or uniqueness), among other approaches. Once informative meta paths for a given KG have been defined, these meta paths define the semantics of the relationships between nodes in the KG, enabling down-stream machine learning analyses such as link prediction, node classification, and subgraph prediction.

Although various algorithms exist to model meta path-based node simi-larities in a KG, a unifying framework is lacking to compute and compare these similarity scores. We introduce metapaths/ which introduces meta paths in the R ecosystem. The metapaths package enables the computation of meta-path-based similarity search in heterogeneous KGs.

Publication

metapaths: similarity search in heterogene-ous knowledge graphs via meta paths
Ayush Noori, Amelia L.M. Tan, Michelle M. Li, and Marinka Zitnik
In Review 2022 [arXiv]

@article{noori2022metapaths,
  title={Metapaths: similarity search in heterogeneous knowledge graphs via meta paths},
  author={Noori, Ayush and Tan, Amelia L.M. and Li, Michelle M. and Zitnik, Marinka},
  journal={arXiv: 2209.0000},
  volume={},
  number={},
  pages={},
  year={2022},
  publisher={}
}

Code

Implementation in R together with documentation and examples of usage is available in the GitHub repository.

Authors

Latest News

Sep 2022:   New Paper in Nature Chemical Biology

Our paper on artificial intelligence foundation for therapeutic science is published in Nature Chemical Biology.

Sep 2022:   Self-Supervised Pre-Training at NeurIPS 2022

New paper on self-supervised contrastive pre-training accepted at NeurIPS 2022. Project page. Thankful for this collaboration with the Lincoln National Laboratory.

Sep 2022:   Best Paper Honorable Mention Award at IEEE VIS

Our paper on user-centric AI of drug repurposing received the Best Paper Honorable Mention Award at IEEE VIS 2022. Thankful for this collaboration with Gehlenborg Lab.

Sep 2022:   Multimodal Representation Learning with Graphs

Aug 2022:   On Graph AI for Precision Medicine

The recording of our tutorial on using graph AI to advance precision medicine is available. Tune into four hours of interactive lectures about state-of-the-art graph AI methods and applications in precision medicine.

Aug 2022:   Evaluating Explainability for GNNs

New preprint! We introduce a resource for broad evaluation of the quality and reliability of GNN explanations, addressing challenges and providing solutions for GNN explainability. Project website.

Jul 2022:   New Frontiers in Graph Learning at NeurIPS

Excited to organize the New Frontiers in Graph Learning workshop at NeurIPS.

Jul 2022:   AI4Science at NeurIPS

We are excited to host the AI4Science meeting at NeurIPS discussing AI-driven scientific discovery, implementation and verification of AI in science, the influence AI has on the conduct of science, and more.

Jul 2022:   Graph AI for Precision Medicine at ISMB

Jul 2022:   Welcoming Fellows and Summer Students

Welcoming a research fellow Julia Balla and three Summer students, Nicholas Ho, Satvik Tripathi, and Isuru Herath.

Jun 2022:   Broadly Generalizable Pre-Training Approach

Excited to share a preprint on self-supervised method for pre-training. Project website with evaluation on eight datasets, including electrodiagnostic testing, human daily activity recognition, and health state monitoring.

Jun 2022:   Welcoming New Postdocs

Excited to welcome George Dasoulas and Huan He, new postdocs joining us this Summer.

May 2022:   George Named the 2022 Wojcicki Troper Fellow

May 2022:   New preprint on PrimeKG

New preprint on building knowledge graphs to enable precision medicine applications.

May 2022:   Building KGs to Support Precision Medicine

Apr 2022:   Webster on the Cover of Cell Systems

Webster is on the cover of April issue of Cell Systems. Webster uses cell viability changes following gene perturbation to automatically learn cellular functions and pathways from data.

Apr 2022:   NASA Space Biology

Dr. Zitnik will serve on the Science Working Group at NASA Space Biology.

Mar 2022:   Yasha's Graduate Research Fellowship

Yasha won the National Defense Science and Engineering Graduate (NDSEG) Fellowship. Congratulations!

Mar 2022:   AI4Science at ICML 2022

We are excited to be selected to organize the AI4Science meeting at ICML 2022. Stay tuned for details. http://www.ai4science.net/icml22

Mar 2022:   Graph Algorithms in Biomedicine at PSB 2023

Excited to be organizing a session on Graph Algorithms at PSB 2023. Stay tuned for details.

Zitnik Lab  ·  Artificial Intelligence in Medicine and Science  ·  Harvard  ·  Department of Biomedical Informatics