Biological systems function through complex interactions and diverse physiological processes that encompass various types of biomolecules such as DNAs, RNAs, proteins, and gene expression regulatory elements. Each element within this intricate system possesses unique profiles across different omics data types. Achieving a comprehensive understanding of these systems requires an integrated, multi-omic perspective. This emphasizes the importance of integration approaches capable of capturing the complex, often non-linear interactions that define biological systems and addressing the challenges associated with amalgamating heterogeneous data across various omics views [14, 15, 16, 17, 18].

Continual emergence of innovative omics technologies, propelled by biotechnological advancements, enables researchers to access multi-layered information from various biological components. Given the high correlation among these bio-entities, integrative analysis of diverse omics data, encompassing the genome, epigenome, transcriptome, proteome, metabolome, and more, is essential. Multi-omics analysis facilitates the systematic exploration of molecular information at each biological layer, offering a powerful tool for understanding complex biological processes. However, this approach poses challenges in handling the exponentially increasing volume of multi-omics data. Efficient algorithms are crucial to reduce data dimensionality while uncovering the intricacies behind cancer’s complex biological processes.

The innovation in biotechnologies, particularly the availability of diverse high-throughput technologies, has resulted in a burgeoning accumulation of omics data, marking the current era as one of ‘big data’ in biology. Extracting meaningful knowledge from these vast omics datasets remains a formidable challenge in bioinformatics. Therefore, there is an urgent need for improved solutions and innovative methods to efficiently handle and derive meaningful insights from these enormous datasets. Recent advancements in integrated analysis and computational modeling of multi-omics data have started to illuminate this challenging aspect.

Recent progress in the integration of AI-driven models and omics has brought together the realms of AI and biology research. This convergence allows for the extraction of patterns from expanding biological databases, curated annotations, or a combination of both. Once these patterns are assimilated, they can be leveraged to yield innovative insights into mechanistic biology and the design of biomolecules.

AI involves the development of intelligent computer programs capable of emulating human intelligence in sensing, reasoning, acting, and adapting. Machine Learning (ML) represents the initial subset of AI-driven algorithms and applications, with Deep Learning (DL) emerging as the subsequent phase of ML [19, 20]. DL utilizes extensive datasets and intricate algorithms to train models, distinguishing itself from ML by employing neural networks with a level of complexity that mirrors the human brain—referred to as Deep Neural Networks (DNNs). DNNs enhance AI capabilities by adeptly processing complex biological data and predicting diverse molecular activities measured through high-throughput functional Omics assays. These networks have the capacity to unveil patterns, such as sequence motifs of large biomolecules. In practice, DL has demonstrated its proficiency in analyzing complementary multi-modal data streams, while AI has showcased its ability to provide decisive interpretations of voluminous and intricate datasets. Consequently, AI-based analysis emerges as the most effective tool for unraveling biological complexity [21, 22, 23, 24].

DL-based Models have played a substantial role in understanding the regulatory code that governs gene expression in genomic data. This encompasses the investigation of diverse elements involved in gene expression regulation [25], such as transcription factor binding sites [26, 27, 28], transcription start sites (TSSs) [28], missing data extrapolations [29], and other related genomic components [25, 30]. This understanding has been particularly enhanced by the existence of extensive regulatory sequence datasets that are essential for the “AI-learning” process [14, 15, 16, 17, 18].

In contrast to genomics, the interpretation of proteomics, especially the prediction of protein tertiary structure and function from sequence data, has notably lagged behind. This discrepancy is primarily attributed to the delayed progress of proteomic technologies and the inherent uniqueness of the biophysical and biochemical properties among various proteins. Historically, the lack of proteomics data has hindered progress, but in recent years, this trend has undergone a significant reversal due to the widespread adoption of newly developed high-throughput proteomics technologies, resulting in a substantial increase in deposited “protein data sets” [22, 31, 32, 33].

These breakthrough technologies, contributing to the exponential growth of protein sequence data, now provide a robust foundation for the application of AI-assisted computations, particularly through DL-based models. Processes such as extracting physical contacts from the evolutionary protein record, distilling sequence-structure patterns from known protein structures, incorporating templates from homologs in the Protein Data Bank (PDB), and refining coarsely predicted structures into finely resolved ones have all been redefined using neural networks. Collectively, this transformation has yielded algorithms capable of predicting single protein domains with a median accuracy.

DL-based models have demonstrated exceptional performance in interpreting diverse and intricate inquiries. They have been effectively employed in various applications, such as misinformation detection in Cyber-Physical Social Services [34]; traffic safety management and vehicle navigation in intelligent transportation systems [35]; ontology matching to address the semantic heterogeneity problem [36]; online measures for protecting patient health information, ensuring compliance with HIPAA in healthcare systems [37], and the development of quick and reliable diagnosis tools for early-stage detection and treatment of liver cancer [38].

When utilizing either machine learning or more advanced deep learning models for predictions in AI-assisted analysis, they are initially trained on a set of known data. Subsequently, based on this training, the “trained” model attempts to predict outcomes for new data sets. However, a common error that may occur during this process is overfitting, where the model can provide accurate predictions for the training data but not for new data. An overfit model may produce inaccurate predictions and may not perform well with various types of new data. To address this critical and potential limitation, we performed a comparative predictive analysis based on deep learning. Specifically, we simultaneously employed two DL-based TF prediction models: the Convolutional Neural Network (CNN) model and our newly developed Large Language Model (LLM), the biased-SVM model, as outlined below. The congruent outcomes from both models (Fig. 1) not only validate our TF predictions but also reduce the risk of overfitting. Moreover, to continue minimizing the risk of overfitting linked to DL models, our plan is to consistently expand our pool of known TFs. Following this, we will engage in both in-vitro and in-vivo experiments to reevaluate the cellular functions of these TFs within CCM isoforms.

As previously noted, DL-based models exhibit potential in predicting functional genomics and can generate protein sequences with foreseeable functions across extensive protein families. Furthermore, the utilization of DL-based models for predicting 3D structures of large proteins is becoming a noteworthy research area. Beyond their contributions to protein structure and function analysis, DL-based models possess significant potential for diverse facets of proteomic analysis, including enzymatic activities [39], post-translational modification (PTM) status [30], protein classification [15, 30], and even design and engineering for novel protein species [20, 33].

Recently, there have been growing concerns about the constraints and shortcomings of DL-based models, especially concerning their effectiveness and interpretability. Although recent DL models have demonstrated enhanced accuracy in predicting variant effects, they encounter challenges when analyzing all coding variants in proteins, often depending on close homologs or DL-based algorithms. A notable limitation of DL models is evident in the importance scores within DNN attribution maps, which can sometimes be spurious, introducing uncertainty into the reliability of model selection, even for well-performing DNNs.

To tackle these challenges, there is a need for modified approaches that measure the consistency of crucial features across a population of attribution maps for proteomic tasks. In response, a novel model is introduced, leveraging well-trained protein DL-derived language models, defined as Large Language Model (LLM) [31, 40, 41, 42, 43]. LLMs, initiated from ensembles of evolutionarily related protein sequences, capture representations of protein families, facilitating the generation of functional protein sequences. When applied to protein sequence data, these models advance predictions related to structure, function, and mutational effects. Given their exceptional performance, LLMs have been seamlessly integrated into common benchtop proteomic domains, such as AlphaFold [44]. These models generate functional protein sequences from ensembles of evolutionarily related protein sequences, capturing representations of protein families and acquiring knowledge about constraints associated with protein structure and function. Pre-trained natural language processing models, fine-tuned for in-domain tasks, transfer learned knowledge from a vast natural language corpus to protein domains. The use of protein language models as foundational structures, pre-trained on extensive natural language corpora, streamlines the handling of protein sequences, enabling them to encapsulate information about protein quaternary states.

Building on this direction, we utilized our newly developed a Large Language Model (LLM), the biased-SVM model [13], to explore the cellular functions of Cerebral Cavernous Malformation (CCM) proteins. Our focus was specifically on CCM gene isoforms with nucleocytoplasmic shuttling, serving as transcription factors (TFs) in the regulation of gene expression. The outcomes validate the enhanced performance of the biased-SVM model compared to the conventional DL-based TF prediction model [13, 45, 46, 47, 48]. In a comparative DL-based predictive analysis, we identified 11 isoforms across all CCM proteins (CCM1-3) and validated the TF functionality of 8 isoforms derived from CCM1 and CCM2 proteins. This groundbreaking discovery challenges the prevailing paradigm, which primarily emphasizes the involvement of CCM proteins solely in endothelial cellular functions amid various potential cellular signal cascades during angiogenesis. While the phenomena of inside-out and outside-in signaling in integrin signaling of endothelial cells during angiogenesis have long been recognized, the underlying mechanism remains largely unexplored [4, 49, 50, 51]. Additionally, despite having previously linked CSC signaling and integrin signaling [4], the current discovery that many CCM isoforms act as TFs may represent the missing link required for understanding endothelial cell and angiogenesis dynamics. Nevertheless, addressing this substantial knowledge gap necessitates extensive experiments. To further our understanding of the CCM signaling complex in endothelial cell biology and angiogenesis, it is imperative to validate the identified candidate TFs within CCM isoforms. This validation, particularly in CCM gene knockout cell lines in vitro and CCM gene knockout mice in vivo, is crucial for advancing our comprehension of the intricate processes involved. We are confident that the continuation of this project, along with the implementation of the proposed experiments, will enable a deeper understanding of endothelial cell signaling, potentially shaping the trajectory of angiogenesis. The insights gleaned from this research hold the potential to profoundly impact future strategies for the treatment, prevention, or even the cure of cerebral vascular diseases.