\section{Conclusion}
\label{sec:discussion}
In this paper, we presented IntraPair InterCluster (IPIC), a contrastive approach for multimodal learning on biological datasets without paired samples. By combining intra-treatment matching (or pairing) and inter-treatment clustering, IPIC produces biologically-meaningful embeddings using only treatment-group labels. Our experiments on four unpaired multimodal datasets demonstrated that IPIC consistently outperformed baseline methods designed for paired data, highlighting its potential for biological applications where destructive measurement techniques often prevent the collection of paired samples. Looking ahead, IPIC's flexibility opens opportunities in domains where direct pairings between modalities are impractical. Addressing challenges like noisy, high-dimensional data and incorporating domain-specific priors could further expand its impact, driving insights across fields from healthcare to environmental science and personalized medicine.



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