Multimodal Learning on Graphs for Disease Relation Extraction

Disease knowledge graphs are a way to connect, organize, and access disparate information about diseases with numerous benefits for artificial intelligence (AI). To create knowledge graphs, it is necessary to extract knowledge from multimodal datasets in the form of relationships between disease concepts and normalize both concepts and relationship types.

We introduce REMAP, a multimodal approach for disease relation extraction and classification. The REMAP machine learning approach jointly embeds a partial, incomplete knowledge graph and a medical language dataset into a compact latent vector space, followed by aligning the multimodal embeddings for optimal disease relation extraction.

Systematized knowledge is becoming the backbone of AI, creating opportunities to inject semantics into AI and fully integrate it into machine learning algorithms. While prior semantic knowledge can assist in extracting disease relationships from text, existing methods can not fully leverage multimodal datasets. REMAP is a multimodal approach for extracting and classifying disease relationships by fusing structured knowledge and text information. REMAP provides a flexible neural architecture to easily find, access, and validate AI-driven relationships between disease concepts.

We introduce REMAP (Relation Extraction with Multimodal Alignment Penalty), a multimodal approach for extracting and classifying disease-disease relations. REMAP is a flexible multimodal algorithm that jointly learns over text and graphs with a unique capability to make predictions even when a disease concept exists in only one data type.

REMAP specifies graph-based and text-based deep transformation functions that embed each data type separately and optimize unimodal embedding spaces such that they capture the topology of a disease knowledge graph or the text semantics of disease concepts. To achieve data fusion, REMAP aligns unimodal embedding spaces through a novel alignment penalty loss using shared disease concepts as anchors. This way, REMAP can effectively model data type-specific distributions and diverse representations while also aligning embeddings of distinct data types. Further, REMAP can be jointly trained on both graph and text data types but evaluated on either of the two modalities alone.

In summary, the main contributions of this study are:

  • We develop REMAP, a flexible multimodal approach for extracting and classifying disease-disease relations. REMAP fuses knowledge graph embeddings with deep language models and can flexibly accommodate missing data types, which is necessary to facilitate REMAP’s validation and transition into biomedical implementation.

  • We rigorously evaluate REMAP for extraction and classification of disease-disease relations. To this end, we create a training dataset using distant supervision and a high-quality test dataset of gold-standard annotations provided by three domain experts, all medical doctors. Our evaluations show that REMAP achieves 88.6% micro-accuracy and 81.8% micro-F1 score on the human annotated dataset, outperforming text-based methods by 10 and 17.2 percentage points, respectively. Further, REMAP achieves the best performance, 89.8% micro-accuracy and 84.1% micro-F1 score, surpassing graph-based methods by 8.4 and 10.4 percentage points, respectively.

Examining the relation between hypogonadism and Goldberg-Maxwell

Hypogonadism and Goldberg-Maxwell syndrome are separate and distinct diseases. However, physicians find it challenging to differentiate in their diagnosis of these two diseases, especially in light of Goldberg-Maxwell syndrome being a rare disease. Differential diagnosis is a process wherein a doctor differentiates between two or more conditions that could be behind a person’s symptoms.

The figure below illustrates how a prediction of “differential diagnosis” (DDx) relationship between hypogonadism and Goldberg-Maxwell syndrome changes from incorrect to a correct prediction when using multimodal learning. REMAP correctly recognizes diseases from text and unifies them with disease concepts in the knowledge graph.

For example, hypogonadism and Goldberg-Maxwell syndrome have many outgoing edges of type differential diagnosis. Further, meta-paths connect these diseases in the graph, such as second-order “differential diagnosis → differential diagnosis” meta-path, and their outgoing degrees in the graph are relatively high. In the case of joint learning, the text-based model can extract part of the disease representation from the graph modality to update its internal representations and thus improve text-based classification of relations.


Multimodal Learning on Graphs for Disease Relation Extraction
Yucong Lin, Keming Lu, Sheng Yu, Tianxi Cai, and Marinka Zitnik
In Review 2022

Title = {Multimodal Learning on Graphs for Disease Relation Extraction},
author = {Lin, Yucong and Lu, Keming and Yu, Sheng and Cai, Tianxi and Zitnik, Marinka},
booktitle = {In Review},
year      = {2022}


Datasets and Pytorch implementation of REMAP are available in the GitHub repository.


Latest News

Jan 2023:   GNNDelete at ICLR 2023

Jan 2023:   New Network Principle for Molecular Phenotypes

Dec 2022:   Can we shorten rare disease diagnostic odyssey?

New preprint! Geometric deep learning for diagnosing patients with rare genetic diseases. Implications for using deep learning on sparsely-labeled medical datasets. Thankful for this collaboration with Zak Lab. Project website.

Nov 2022:   Can AI transform the way we discover new drugs?

Our conversation with Harvard Medicine News highlights recent developments and new features in Therapeutics Data Commons.

Oct 2022:   New Paper in Nature Biomedical Engineering

New paper on graph representation learning in biomedicine and healthcare published in Nature Biomedical Engineering.

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

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