Complex diseases arise from the intricate interplay of multiple genetic and environmental risk factors. The phenomenon of simplexity, where simplicity at the phenotypic level coexists with complexity at lower organizational and molecular levels (Stewart and Cohen, 2000; Kauffman et al., 1993), suggests the existence of disease subtypes (Wallstrom et al., 2013) and emphasizes the uniqueness of each patient despite shared characteristics with others (Smith, 2011). Unfortunately, most approaches to studying complex diseases rely on identifying differences between groups based on average biomarker values, overlooking the intricate biological intricacies of these diseases. For this reason, it is necessary to use a more holistic approach that considers the molecular complexity of diseases, which involves thousands of genes across multiple cell types in different body parts (H. Zhang et al., 2019) and poses challenges for developing personalized, targeted therapies (Sierksma et al., 2020; Rouzier et al., 2005; Shipitsin et al,. 2007; Charitou et al., 2016; Khurana et al., 2013; Chan and Loscalzo, 2012).

Network biology is a rapidly developing area of research that recognizes that biological processes are not chiefly controlled by individual proteins or by discrete, unconnected linear pathways but rather by a complex system-level network of molecular interactions (X.-M. Zhang et al., 2021; Khurana et al., 2013; Charitou et al., 2016). Graph neural networks and deep-learning-based data integration models can predict disease progression and identify disease subtypes more accurately by integrating multimodal data from disparate sources, such as genetic, clinical, and imaging data (X.-M. Zhang et al., 2021; Zhou et al., 2022). Therefore, a more holistic approach that considers the molecular complexity of diseases and integrates multimodal data can provide a more comprehensive understanding of complex diseases, leading to the development of personalized, targeted therapies and improved patient outcomes in the era of precision medicine.

Cancer is a prime example of disease heterogeneity, where variability exists in various aspects, including driver mutations, making it challenging to identify causal mutations from an average view of the entire patient cohort (Lengerich et al., 2018). Moreover, diseases such as Autism spectrum disorders and epilepsy exhibit vast degrees of heterogeneity at multiple levels, including genotypes and phenotypes, resulting in diverse clinical differentiations and treatment responses (Lombardo et al., 2019). The clinical variability observed in diseases like Parkinson’s and Alzheimer’s further highlights the need to go beyond mean values and explore other approaches that capture the heterogeneous nature of complex diseases (Freudenberg-Hua et al., 2018; Ma et al., 2018).

Clinical studies of diseases often suffer from biases due to demographic, social, genetic, and ethnic factors, leading to the underrepresentation of specific population groups (Prosperi et al., 2018). This underrepresentation hampers the generalizability of conclusions to a larger population, hindering the development of effective treatments (Kessler et al., 2016; Popejoy and Fullerton, 2016; Popejoy et al., 2018; Gurdasani et al., 2019). The failure of numerous clinical trials and the lack of a cure for diseases like Alzheimer’s emphasize the need to account for population heterogeneity in trial design and consider the underlying biological mechanisms for disease subtyping (Devi and Scheltens, 2018).

While challenges exist in identifying biomarkers for heterogeneous diseases, scale-out learning approaches often need more specificity and may not be applicable in clinical practice (Khurana et al., 2013). Additionally, invasive and costly procedures or limited access to relevant tissues hinder studying central nervous system diseases (Koníčková et al., 2022). Therefore, it is necessary to adopt new approaches that precisely consider the underlying biological mechanisms in disease subtyping (Yin et al., 2019), incorporating clinical and omics analyses to improve treatment responses (Zhou et al., 2022; X.-M; Zhang et al., 2021).

The study of complex diseases is not only a scientific effort but also a public health concern. The increasing availability of drugs that can contribute to molecular-tailored treatments based on predictive biomarkers underscores the importance of improving our understanding of individual patients to enhance their quality of life (Zhou et al., 2022). To address these challenges, we require new approaches that exponentially scale up learning on complex diseases, enabling a deeper understanding of each individual and more effective interventions (X.-M. Zhang et al., 2021). By embracing these novel approaches, we can advance our knowledge of complex diseases, refine disease subtyping, and guide the selection of personalized treatment strategies to improve patient outcomes and enhance public health.

The challenges of using individualized networks in precision medicine include the requirement for molecular data, which is usually unavailable in clinical practice, and the need to develop new strategies to infer networks at the individual level (Van Der Wijst et al., 2018; R.-S. et al., 2023). The clinical application of precision medicine will likely require a fusion of approaches tailored to each clinical problem, which can be complex and require significant computational resources (Duffy, 2016). Furthermore, the statistical rigor of network predictions comes from the study design and the size of the datasets, which can be a limitation (Galindez et al., 2023). Current approaches may need more samples to infer coexpression networks that accurately capture the complexity of individualized networks. The search space of possible coexpression networks is vast and decreased uncertainty and reduced statistical power due to the small sample size may limit the generalizability of the constructed networks (Liesecke et al., 2019).

Obtaining many samples with comprehensive genomic data can be challenging, especially for rare diseases or specific patient populations. With limited sample sizes, the statistical power to detect meaningful coexpression relationships may be reduced, leading to false positives or missing important connections. One approach to address these limitations is leveraging existing knowledge from larger datasets or databases, incorporating prior knowledge about gene-gene interactions, regulatory relationships, or functional annotations. Integrating multi-omics data from different modalities (e.g., genomics, transcriptomics, proteomics) could provide a more comprehensive view of individual-specific networks. Collaboration among researchers and data sharing can help increase sample sizes and improve the statistical power of coexpression network inference (Escorcia-Rodríguez et al., 2023). The development of novel statistical methods specifically designed for analyzing individualized coexpression networks can improve the accuracy and reliability of the inferred networks (Yu et al., 2018).

Finally, developing more sophisticated algorithms and computational methods can help extract meaningful information from smaller sample sizes and incorporate prior knowledge, improving the accuracy and robustness of individualized coexpression networks (Colby et al., 2018). For example, Liesecke et al. proposed the idea of conserved coexpression links between two genes over several datasets, reinforcing the coexpression relationship (Liesecke et al., 2019). However, there are still challenges to overcome. When merging expression data, the size increase should outweigh the noise inclusion, and graph structure should be considered when integrating the inferences (Escorcia-Rodríguez et al., 2023). The potential bias introduced by relying on external datasets should also be considered, as they may only partially represent the specific biological context of the individual sample. Moreover, methods inferring coexpression networks should no longer be assessed solely based on standard performance metrics and graph structural properties.

Overall, while individualized networks have the potential to advance precision medicine, they require careful validation and interpretation of results to ensure they are biologically meaningful and clinically relevant. For other hand, the cost of using transcriptomic data has decreased over time, making it more accessible for researchers and clinicians, and it is important to consider the potential benefits of, and funding opportunities for research in personalized medicine; for this reason, it is addressing these challenges and limitations is crucial for their success and from a perspective. Stratification makes possible the design of new clinical trials to reevaluate previously tested drugs without such stratification and determine possible new therapies or treatments for each molecular subtype of patients (Rajewsky et al., 2020).

Personalized medicine, with its focus on individualized medical treatment based on patient characteristics, has the potential to revolutionize healthcare by improving patient outcomes and enhancing the quality of care. Developing individualized therapy protocols considering patient heterogeneity can minimize patient suffering while maximizing treatment effectiveness; this necessitates the refinement of disease categorization to understand the biological differences among subtypes better and guide personalized treatment strategies.

Novel individualized gene coexpression networks offer a paradigm shift in studying complex diseases by revealing patient-specific gene expression patterns and modules. By integrating multimodal information and considering patient-specific characteristics, these networks enhance our understanding of disease pathogenesis, treatment response, and diagnostic accuracy. They provide a more comprehensive understanding of complex diseases, refine disease subtyping, and guide the selection of personalized treatment strategies to improve patient outcomes.

Network medicine, which integrates diverse biological networks, is emerging as a powerful approach to offer a systems-level understanding of disease mechanisms and underlying causes. By analyzing gene-gene interactions in individual samples and systematically comparing them, we can identify pathways, subtypes of disease states, and key components in the networks that can be targeted in clinical practice. Multiscale mathematical and computational tools and integrating genomic and clinical data enable the construction of individualized networks with single-individual resolution.

While the potential impact of individualized coexpression networks on clinical practice is significant, further research and interdisciplinary collaboration are needed to realize their transformative powerfully. Standardization and robustness of data-gathering approaches, including imaging, multi-omic approaches, and clinical information, are critical for scalability to larger patient cohorts. Deep-learning-based data integration models hold promise in accurately predicting disease progression and identifying disease subtypes by leveraging multimodal data from various sources.

Addressing the limitations of current approaches to infer coexpression networks requires leveraging existing knowledge, integrating multi-omics data, collaborative efforts among researchers, and developing novel statistical methods and improved algorithms. These potential solutions represent promising directions for overcoming current limitations and advancing the inference of individualized coexpression networks.

In conclusion, individualized coexpression networks have the potential to significantly advance our knowledge of complex diseases, refine disease subtyping, and guide the selection of personalized treatment strategies. By integrating diverse biological networks and considering patient-specific characteristics, these networks enhance our understanding of disease mechanisms and improve patient outcomes in the era of precision medicine. As we continue to explore the transformative potential of network medicine, interdisciplinary collaboration, further research, and methodological advancements are vital to fully harness the power of individualized coexpression networks and improve healthcare outcomes for patients.