INFERENCE OF BIOLOGICAL INTERACTIONS ON
LARGE HETEROGENOUS GRAPH WITH
MACHINE LEARNING

Antoine Toffano, Jacques Pécréaux, Christophe Héligon

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Antoine Toffano, Jacques Pécréaux, Christophe Héligon. INFERENCE OF BIOLOGICAL INTERACTIONS ON LARGE HETEROGENOUS GRAPH WITH MACHINE LEARNING. JOBIM 2023, Jun 2023, Plouzané, France. hal-05246908

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# INFERENCE OF BIOLOGICAL INTERACTIONS ON LARGE HETEROGENOUS GRAPH WITH MACHINE LEARNING

Antoine Toffano¹, Jacques Pécréaux¹, Christophe Héligon¹

¹CNRS, Univ Rennes, IGDR - UMR 6290, 2 avenue du Professeur Léon Bernard, F-35000, Rennes, France

## Introduction

This work aims to uncover novel relationships between biological entities, including genes and phenotypes, by combining large heterogeneous data graphs with machine learning.

We use Semantic Web technologies to create computer-actionable graphs, integrating diverse biological data types.

The goal is to ultimately create a robust tool capable of handling sparse and noisy data.

## Data Model

<figure><img src="image_2.png" /></figure>

## Methodology

We train 4 different graph Embedding Models (EM) to learn patterns within the data.

A held out part of the graph is then transformed using the EM into corresponding node embeddings on which different binary classifiers are trained to predict link existence or not.

<figure><img src="image_3.png" /></figure>

This two step approach ensures accurate predictions and evaluations.

## Results - Embedding Models

We evaluate EMs based on their ability to correctly predict the best relation type between two nodes among all relations (Hit@1).

<figure><img src="image_4.png" /></figure>

Methods react differently to a change in the data, suggesting that they capture different aspects of the graph.

## Results - Binary Classifiers

We reach on average an Accuracy near 0.85 on the base unmodified dataset.

<figure><img src="image_5.png" /></figure>

However, a model with perfect Accuracy will not be able to predict new links, only retrieve what is already known.

<figure><img src="image_6.png" /></figure>

In a high Accuracy model, good candidates are found alongside model errors in the false positives predictions.

In turn, false negative predictions may reveal errors in the original data.

## Conclusion & Limitations

* We developed a pipeline that can train models to find missing information and errors in a graph.

* Our best performing models reach a high classifier Accuracy (>0.85).

* How to balance Accuracy and candidate prediction is unclear.

* Biological experimentation is necessary to test our predictions.

## Graph Embedding

In a nutshell, graph embedding transforms nodes and relations into vectors, allowing computers to better analyze them.

It can be done by aiming to achieve a low distance between the vector of the starting node *h* summed with the relation vector *r* and the vector of the end node *t* such that (*h* + *r* = *t*).

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<table><thead><tr><th>Dataset</th><th>Triples</th><th>Links</th><th>Entities</th></tr></thead><tbody><tr><td>Base</td><td>1 416 268</td><td>10</td><td>675 845</td></tr><tr><td>(+) GO Annotations</td><td>1 593 767</td><td>15</td><td>861 836</td></tr><tr><td>(-) Neg. Phenotype Association</td><td>755 100</td><td>9</td><td>367 875</td></tr><tr><td>No Reification</td><td>391 724</td><td>9</td><td>99 699</td></tr></tbody></table>

* Figure from 'A Survey on Knowledge Graphs: Representation, Acquisition and Applications', Ji et al., 2022.

## Contact:

antoine.toffano@gmail.com
christophe.heligon@univ-rennes.fr

igdr

GitHub: All code is publicly available on GitHub!

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atoffano/KGene2Pheno

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Université de Rennes