Bridging Molecules, Cells, and Data Gaps

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Author: Denis Avetisyan

A new framework learns comprehensive molecular representations, even with incomplete biological information, to improve property prediction.

Cellular responses to molecular disturbance are often incomplete, yet through successive refinement-augmentation, alignment, and hierarchical organization-multi-modal representations emerge, structuring a cohesive response from fragmented data.
Cellular responses to molecular disturbance are often incomplete, yet through successive refinement-augmentation, alignment, and hierarchical organization-multi-modal representations emerge, structuring a cohesive response from fragmented data.

This research introduces a cell-aware hierarchical multi-modal representation learning approach for robust molecular modeling and missing data imputation.

Predicting molecular properties requires moving beyond chemical structure alone, yet current methods struggle with incomplete biological data and fail to capture complex relationships across molecular, cellular, and genomic levels. To address these limitations, we present CHMR (Learning Cell-Aware Hierarchical Multi-Modal Representations for Robust Molecular Modeling), a novel framework that learns robust molecular representations by jointly modeling local-global dependencies and capturing biological hierarchies via tree-structured vector quantization. Extensive evaluation on nine benchmarks-spanning 728 tasks-demonstrates that CHMR outperforms state-of-the-art baselines, achieving average improvements of 3.6% on classification and 17.2% on regression. Could this hierarchy-aware, multimodal approach unlock more reliable and biologically grounded predictions across diverse biomedical applications?

The Inevitable Complexity of Biological Signals

The pursuit of systems-level understanding in biology increasingly relies on the convergence of diverse data types, ranging from a cell’s molecular composition and protein interactions to the dynamic readout of gene expression. However, effectively integrating these heterogeneous datasets presents a formidable challenge. Biological systems are inherently complex, and data acquisition often yields incomplete or noisy information, creating significant gaps in knowledge. These datasets differ substantially in scale, format, and underlying assumptions, requiring sophisticated computational approaches to harmonize and interpret them. Failure to address these integration challenges can lead to skewed interpretations and inaccurate models, hindering progress in critical areas like deciphering disease mechanisms and developing targeted therapies. The ability to build cohesive representations from disparate biological information is therefore paramount to unlocking the full potential of modern biomedical research.

Conventional analytical techniques frequently falter when applied to the complex landscape of multi-modal biological data due to its intrinsic variability and frequent gaps. Biological datasets, encompassing genomics, proteomics, and metabolomics, rarely present a complete picture; missing values are commonplace, and data types differ significantly in scale and distribution. This inherent heterogeneity introduces substantial challenges for algorithms designed for uniformity, often leading to skewed interpretations or inaccurate modeling of biological processes. Consequently, reliance on these traditional methods can generate biased representations of underlying systems, hindering reliable conclusions and limiting the potential for translational applications in fields like precision medicine and pharmaceutical development.

The convergence of multi-modal biological data is not merely a technical pursuit, but a foundational requirement for realizing the full potential of modern healthcare. Effectively integrating genomic, proteomic, imaging, and clinical datasets allows researchers to move beyond correlative studies and towards mechanistic understandings of disease. This deeper insight is particularly crucial in drug discovery, where identifying true drug targets and predicting efficacy necessitates a holistic view of biological systems. Furthermore, personalized medicine, with its promise of tailored therapies, relies heavily on the ability to accurately characterize individual patients through the synthesis of their unique multi-modal profiles. Without bridging the gaps in data integration, the promise of precisely targeted treatments and preventative strategies will remain largely unrealized, hindering progress towards a more proactive and effective healthcare paradigm.

The CHMR framework enhances robust molecular property prediction with missing data by augmenting modalities, aligning molecular and cellular information, capturing hierarchical biological semantics with tree-structured vector quantization, and improving generalization via cross-modal context propagation.
The CHMR framework enhances robust molecular property prediction with missing data by augmenting modalities, aligning molecular and cellular information, capturing hierarchical biological semantics with tree-structured vector quantization, and improving generalization via cross-modal context propagation.

Constructing a Hierarchical Representation of Life’s Complexity

The CHMR framework integrates data from diverse biological modalities – including molecular structures, cellular characteristics, and genomic information – into a cohesive representational space. This unification is achieved through a shared embedding process, allowing for the learning of molecular representations that are informed by relationships across these distinct data types. Rather than treating each modality in isolation, CHMR learns a joint representation that captures interdependencies, improving the robustness and generalizability of downstream analyses. This approach contrasts with traditional methods that often require separate analysis pipelines for each modality before attempting integration, and facilitates predictive modeling and knowledge discovery by leveraging the complementary information contained within each biological layer.

Tree-Structured Vector Quantization (Tree-VQ) is a method employed within the CHMR framework to model the relationships between molecules, cells, and genes by creating a hierarchical, discrete latent space. This is achieved through iterative vector quantization, where data is mapped to a codebook of vector embeddings organized as a tree structure. Each level of the tree represents a different level of abstraction, capturing dependencies from broad, general features to specific, detailed characteristics. The tree structure allows for efficient representation of complex, multi-scale data and facilitates the discovery of relationships between different biological entities by grouping similar data points at each level of the hierarchy. This discretization process reduces dimensionality while preserving key information regarding the underlying biological processes.

The CHMR framework mitigates the challenges posed by incomplete multi-omic datasets through a probabilistic modeling approach. Specifically, CHMR employs a generative model that imputes missing modality information during representation learning. This is achieved by learning shared representations across all available modalities and then using these representations to predict the values of missing features. The framework utilizes variational autoencoders to model the joint distribution of all modalities, allowing for effective imputation without requiring complete data for all samples. Evaluation metrics demonstrate that CHMR maintains performance and accuracy even with a substantial percentage of missing data, outperforming methods that require complete datasets or simple imputation strategies.

The CHMR framework generates molecular representations by explicitly modeling the interdependencies observed between molecules, cells, and genes within biological systems. This is achieved through a hierarchical structure that captures relationships at multiple levels of granularity, allowing the model to represent complex biological interactions beyond simple feature combinations. Consequently, the resulting representations are not merely descriptive of individual entities, but also encode information about their contextual relationships, leading to improved performance in downstream tasks such as drug discovery and disease prediction. The hierarchical nature of these representations also facilitates interpretability, as specific branches within the learned structure can be traced back to particular biological relationships and contributing factors.

Aligning Semantic Spaces for Coherent Biological Understanding

Semantic Consistency Alignment (SCA) addresses the challenge of differing data distributions between molecular and cellular modalities by minimizing the discrepancy between their respective representations. This is achieved through the application of two contrastive loss functions: InfoNCE and VICReg. InfoNCE, a noise-contrastive estimation loss, maximizes the mutual information between representations of paired molecular and cellular data points while minimizing it for unpaired data. VICReg, a variance-preserving regularization technique, encourages the learned representations to have high variance along informative dimensions and low variance along uninformative ones, further promoting consistency and preventing trivial solutions. By jointly optimizing these losses, SCA encourages the model to learn a shared embedding space where representations from both modalities are mutually aligned, improving the overall robustness and accuracy of downstream analyses.

Semantic Consistency Alignment (SCA) achieves mutual consistency between representations derived from different biological modalities by minimizing the distance between corresponding embeddings. This is accomplished through the application of contrastive and regularization losses – specifically, InfoNCE and VICReg – which encourage similar representations for biologically related entities across modalities. By enforcing this consistency, SCA reduces ambiguity and noise in the learned representations, directly contributing to increased accuracy and reliability in downstream analysis tasks such as cell type identification, disease state prediction, and gene regulatory network inference. The alignment process effectively integrates information from disparate sources, leading to a more robust and informative representation of the underlying biological system.

Context-Propagation Reconstruction (CPR) operates by constructing a biological graph representing relationships between entities, such as genes and proteins. Information is then propagated across this graph using random walks, effectively diffusing contextual signals from neighboring entities to enhance the representation of a target entity. This process allows the model to incorporate indirect relationships and dependencies, improving the fidelity of learned representations, particularly in scenarios where direct interactions are limited or noisy. The random walk methodology enables the capture of multi-hop relationships, thereby providing a more comprehensive contextual understanding than methods relying solely on immediate neighbors.

Context-Propagation Reconstruction (CPR) enhances the robustness of learned molecular and cellular representations by integrating information from a biological graph. This is achieved through the implementation of random walks on the graph, allowing for the propagation of contextual signals between neighboring entities. By leveraging these relationships, CPR effectively addresses scenarios involving incomplete data or noisy observations, common in complex biological systems. The propagated contextual information enables the model to infer missing or uncertain values, leading to more stable and reliable representations even under challenging conditions and ultimately improving performance in downstream analytical tasks.

A Framework for Accelerating Biological Discovery

A newly developed framework, CHMR, exhibits notable advancements in the prediction of molecular properties, a cornerstone of both drug discovery and materials science. Rigorous testing across 728 diverse tasks revealed an average performance improvement of 3.6% in classification challenges and a substantial 17.2% gain in regression tasks. This enhanced predictive capability stems from CHMR’s innovative approach to data representation, allowing it to more accurately model the complex relationships between molecular structure and resulting properties. Such improvements translate directly into more efficient screening of potential drug candidates and the accelerated design of novel materials with desired characteristics, ultimately promising a significant impact on scientific innovation.

Biological datasets are rarely complete; measurements are often missing due to experimental limitations, cost, or inherent data complexity. The CHMR framework distinguishes itself through a robust capacity to effectively process information even when certain data modalities are absent. This is achieved through a novel architecture that doesn’t require complete input, instead intelligently integrating available information to construct meaningful representations. Consequently, CHMR proves particularly adaptable to the messy reality of real-world biological data, offering a significant advantage over methods that demand comprehensive datasets. This capability unlocks the potential to analyze a wider range of biological samples and accelerate discoveries even with incomplete information, representing a substantial step towards more practical and versatile biological data analysis.

Evaluations using the Biogen dataset demonstrate a substantial performance gain with CHMR, which achieved a 17.2% reduction in Mean Absolute Error (MAE) when contrasted with the next best performing method. This improvement signifies a considerable leap in predictive accuracy for complex biological data, suggesting CHMR’s capacity to model intricate relationships often present in pharmaceutical research. The magnitude of this reduction isn’t merely incremental; it indicates a fundamentally more effective approach to analyzing data crucial for understanding disease mechanisms and identifying potential drug candidates, potentially accelerating the pace of discovery in the field.

Evaluations on standardized benchmark datasets reveal that CHMR demonstrates a significant advancement in performance metrics. Specifically, the framework achieved an average Area Under the Curve (AUC) of 82.2%, indicating its enhanced ability to distinguish between positive and negative cases in predictive modeling. This result notably surpasses the performance of competing methods, InfoAlign, which registered an average AUC of 79.1%, and MOL-Mamba, with 80.8%. This improvement in AUC underscores CHMR’s potential to deliver more accurate and reliable predictions in various scientific applications, ultimately strengthening its utility as a foundational tool for biological research and beyond.

Continued development of the CHMR framework prioritizes expanding its capacity to process increasingly large and complex biological datasets, a crucial step toward unlocking insights from the wealth of genomic and proteomic information becoming available. Researchers intend to move beyond current benchmarks, applying CHMR’s multimodal capabilities to diverse areas such as personalized medicine, disease modeling, and the identification of novel drug targets. This includes investigations into complex biological systems and the integration of data from various sources – including imaging, clinical records, and environmental factors – to create a more holistic understanding of life processes. The ultimate goal is to establish CHMR as a versatile tool for accelerating scientific discovery across the biological sciences, fostering innovation in both fundamental research and translational applications.

The development of CHMR signifies a substantial leap towards creating biological representations that are not only more precise in predicting molecular properties, but also offer enhanced clarity and resilience. By effectively integrating diverse data modalities – even in the presence of missing information – this framework generates a more holistic understanding of biological systems. This capability moves beyond simple prediction, enabling researchers to dissect complex relationships and validate hypotheses with greater confidence. Consequently, the potential for accelerating scientific discovery is significant, promising faster progress in fields like drug development, personalized medicine, and materials science, as researchers gain access to more reliable and insightful data-driven tools.

The pursuit of robust molecular modeling, as demonstrated by CHMR, necessitates a considered approach to system evolution. Each iteration of the framework-from initial design to refinement through missing modality imputation and hierarchical modeling-can be viewed as a commitment to graceful decay. As Edsger W. Dijkstra observed, “It’s always possible to decompose a system into smaller parts.” This decomposition, mirrored in CHMR’s handling of complex molecular relationships, isn’t merely about simplification, but about building a system capable of adapting and maintaining integrity even as data becomes incomplete or conditions shift. Delaying improvements to address these data gaps is, indeed, a tax on ambition, hindering the potential for accurate molecular property prediction.

What’s Next?

The presented framework, while demonstrating efficacy in navigating incomplete biological datasets, merely delays the inevitable entropic drift inherent in all representational systems. The imputation of missing modalities, a necessary corrective at this juncture, highlights a fundamental limitation: the reliance on proxies for true, often inaccessible, ground truth. Uptime is temporary; the architecture functions as long as the imputed data holds predictive power, a power invariably diminished by the passage of time and the accumulation of unforeseen interactions.

Future iterations will inevitably confront the question of how to model not just the static relationships between molecules, cells, and genes, but the dynamic flows of information within those systems. Current approaches treat hierarchy as a fixed structure, yet biological organization is inherently fluid and responsive. The challenge lies in building representations that acknowledge this impermanence, embracing latency as the unavoidable tax every request for information must pay.

Ultimately, the pursuit of “robust” molecular modeling is a temporary stay of execution. The true metric isn’t predictive accuracy, but rather the grace with which the system ages – how readily it adapts, or fails to, as the underlying biological landscape shifts. Stability is an illusion cached by time, and the lifespan of any representational scheme is dictated not by its initial brilliance, but by its capacity to decay predictably.

Original article: https://arxiv.org/pdf/2511.21120.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-30 05:40