Modeling Gene Interactions in HIV Research

Scientists are working hard to find better ways to fight viruses and protect public health. One focus is on creating treatments that can stop infections before they start or lessen their severity. This is especially important for groups that are more at risk. However, a big challenge lies in finding specific genes that can be targeted for these treatments. Researching therapies can take a lot of time and money. Therefore, identifying promising genes can help streamline the validation studies and clinical trials needed for effective therapies. By studying these genes, researchers hope to discover common ways that viruses infect cells, which could be useful for other viruses as well.

This article discusses three different models we used to identify which genes might be useful for targeting in the fight against Human Immunodeficiency Virus (HIV). HIV is a good focus because scientists have already gathered a lot of information about it and its genes. This makes it easier for researchers to look into how the virus interacts with human genes.

#Gene Interactions and Their Importance

To better understand how genes interact, we looked at pairwise epistasis, which is how genes affect each other’s function. We used two main methods to analyze these interactions.

#Graph-Based Method

The first method involves using a large database called the Scalable Precision Medicine Oriented Knowledge Engine (SPOKE). This database includes more than 20,000 human genes and over a million types of gene relationships. By creating a graphical representation of the connections between these genes, researchers can better understand how they work together.

In this method, each gene is represented as a vector, which is a way to show information numerically. We focused on 356 genes known to be related to HIV and stored their representations in our model. This allowed us to analyze how gene pairs interact with each other.

#Geneformer Model

The second method we used is called Geneformer, which is a type of neural network that has learned from a huge dataset of single-cell gene information. This model helps researchers understand how different genes work together and their roles in HIV.

Geneformer also ranks genes based on their expression in different cells. This ranking helps identify which genes are most important in distinguishing different cell states. We used these ranked genes to create a deeper understanding of how they contribute to the interactions related to HIV.

#Validating Our Models

For our research, we compared our Predictive Models based on a dataset that contains information about 63,012 gene interactions closely linked to HIV. This dataset was created to study genetic interactions in detail. We focused on a specific section of this dataset that allows us to categorize gene pairs based on whether they help suppress or enhance HIV.

Using this data, we set a threshold to distinguish between the two categories.

#Results and Discussion

Our predictive models give a simple yes-or-no answer to whether a gene pair is connected to HIV suppression or enhancement. We set a threshold based on the average response, which helps maintain balance between the two categories. The models show most gene pairs do not have a significant interaction, while some pairs are linked to HIV suppression.

#FastRP Model Results

The first model, based on FastRP embeddings using the SPOKE database, achieved about 70% accuracy in predicting outcomes. This model categorizes gene pairs without any fine-tuning. Despite its simplicity, it showed promise in identifying pairs that might worsen HIV.

#Geneformer Results

The second model, which relied on Geneformer embeddings, produced similar predictions with roughly the same level of accuracy. This finding was surprising since the two methods employed different approaches but yielded nearly identical outcomes.

#Comparing Model Performance

We compared the performance of both models using a graphical representation called the ROC curve, which helps understand how well the models predict outcomes. While both models performed well, Geneformer showed a slight edge according to the results.

#Addressing Order Invariance

A key issue we faced was the order of gene pairs in our models. The same pair of genes can provide different results depending on the order they are presented. This inconsistency can mislead our understanding of genetic interactions.

To resolve this issue, we implemented a Siamese Network, which is designed to measure the similarity between input pairs irrespective of their order. This network structure is used in various machine learning scenarios, including image recognition and protein interactions.

#Siamese Network Implementation

The Siamese network consists of two identical branches for processing the gene pairs. This structure ensures both order and symmetry are considered during analysis. We found that training this network improved our predictive accuracy slightly from 70% to around 71%.

This new model also successfully eliminated the prediction inconsistencies we saw previously, showing perfect agreement regardless of the order of gene pairs.

#Conclusion

In summary, we tested three different models to classify gene pairs linked to HIV suppression and enhancement. The first two models, using FastRP and Geneformer embeddings, provided strong foundational results. However, they were limited in capturing the nuances of gene interactions due to their reliance on fixed input structures.

The introduction of the Siamese network brought order-invariance to the analysis, significantly improving consistency in predictions. This work highlights how computational models can assist in understanding the interactions between viruses and human genes, paving the way for more effective therapies.

Next, these models can be adapted for other diseases and could be used in cases where datasets are sparse. We have only scratched the surface with binary classifications; there is a possibility of expanding the models to include more complex classifications for greater accuracy in understanding genetic interactions.

Our research underscores the importance of using advanced computational tools to accelerate the discovery of effective treatment strategies in the fight against viral infections.