Biological mechanisms stem from complex intracellular interactions spanning across different levels of regulation. Mapping these interactions is fundamental for the understanding of all types of biological conditions, including complex diseases. Each experimental approach carries its own bias and noise. Combining heterogeneous data sources reduces noise and gives a broader sense of the interactions between genes known as functional association, where both direct and indirect interactions are captured.

FunCoup is one of the most comprehensive functional association databases, providing networks for 22 organisms in all domains of life. FunCoup uses a naïve Bayesian integration approach to combine 11 different data types and increases the coverage by transferring associations between species via orthologs. Additional insights into the mechanisms of a gene network are provided through tissue specificity filtering and directed regulatory links.

Even though FunCoup provides a comprehensive map of the intracellular machinery, gaining insights into conditions such as diseases requires a functional level analysis rather than a gene level analysis. Thus, studying genes that are involved in a condition from a functional perspective requires the usage of pathway enrichment analysis. Several approaches exist, from basic gene overlap to more elaborate analyses that use functional association networks. ANUBIX is a novel network-based analysis (NBA) method that overcomes the high false positive rate issue that previous state-of-the-art NBA approaches have. Additionally, even with accurate methods, a commonly ignored problem is that gene sets derived from experiments are often noisy or contain multiple mechanisms, mixing different pathways which weakens their association to the condition under study. To increase the sensitivity of pathway analysis, we developed a pipeline to cluster gene sets into more homogeneous parts with the aim of unraveling all the mechanisms activated in the studied condition. To facilitate the usage of these tools, we built a web server called PathBIX, a user-friendly platform that allows interactive analysis of all species in FunCoup against multiple pathway databases.

Understanding biological systems in complex organisms is important in life science in order to comprehend the interplay of genes, proteins, and compounds causing complex diseases. As biological systems are intricate, bioinformatics tools, models, and algorithms are of the utmost importance to understand the bigger picture and decipher biological meaning from the vast amounts of information available from biological experiments and predictions. Bioinformatics programs and algorithms do not only depend on information from experiments, but also on information generated from other tools in order to draw accurate conclusions and make predictions. 

Prediction of orthologs, genes having a common ancestry, separated by a speciation event, are important building blocks for a wide variety of tools and analysis pipelines, as they can be used to transfer gene function between species. Orthologs can for example be used to map genes of model organisms to genes in humans in studies of drug targets. They are extensively used in functional association networks in order to transfer information between species. Functional association networks are models of associations between genes or proteins, where associations can be derived from experimental evidence of different types, from the species itself, or transferred from other species using orthologs. The networks can be used to explore the context and neighbors of a gene, but also for a variety of higher-level analyses, e.g. network-based pathway enrichment analysis. In pathway enrichment analysis the networks can be utilized to contextualize experimental gene sets and annotate them with biological functions. As these tools depend on each other, it is of great importance that the networks used in pathway enrichment analysis are comprehensive and accurate, and that the orthologs used in the networks are relevant and significant. 

In this thesis, the development and improvement of five bioinformatics tools within three areas of bioinformatics are presented. Despite the tools residing within slightly different areas, they all rely on each other, and can all on different levels improve our understanding of biological functions and biological meaning, from the level of orthology analysis to functional association networks to pathway enrichment analysis.

This thesis explores how bioinformatics advances the study of complex diseases by providing system-level models that capture intricate gene-protein interactions. Traditional reductionist methods focusing on isolated genes or proteins fail to explain the broader dysfunctions of complex diseases. The rise of diseases like cancer, cardiovascular and neurological disorders, and autoimmune conditions underscores the need for approaches leveraging high-throughput technologies and network-based models for comprehensive understanding.

Central to this research are functional association networks, which map direct and indirect functional relationships between genes and proteins. These networks integrate genomic, transcriptomic, proteomic, and functional data. Functional association networks are valuable resources in biological and biomedical research, enabling e.g. discovery of interaction partners, pathway enrichment analysis, and disease module identification. This thesis presents the 5th and 6th releases of FunCoup, a comprehensive resource of functional association networks, with upgrades in data integration, network framework, and user access via a redesigned website and Cytoscape app.

FunCoup 5 expanded to 22 species and generated around 70 million interaction links from 11 evidence types. Innovations included directed regulatory links, a SARS-CoV-2 virus-host network, and tissue-specific filtering for context-sensitive research.

FunCoup 6 expanded to all 640 species in InParanoiDB, with over 100 million links for the 23 primary species, including 1 million directed transcription factor-target links. A key improvement is the shift to a bin-free Bayesian training framework, using kernel density estimation of likelihoods of interaction from ten evidence types. New tools like MaxLink and TOPAS were added for disease module and drug target identification, together with built-in KEGG pathway enrichment via ANUBIX and EASE. Rebuilt in Python with a redesigned website, FunCoup 6 offers enhanced flexibility, scalability, and user access.

The FunCoup Cytoscape app uses the RESTful API to integrate FunCoup’s resources into Cytoscape, enabling network visualization and analysis across all 640 species with powerful Cytoscape tools and plugins.

This thesis highlights applications in pathway enrichment analysis (EA) and disease module detection. Network-based EA tools simplify the analysis of differentially expressed genes by linking them to known pathways, providing a systems-level understanding of molecular changes. A new benchmark compared 14 EA methods across 82 datasets from 26 diseases, showing that network-based methods like ANUBIX outperformed traditional approaches, offering more accurate insights and guidance for method selection.

TOPAS, a new approach, uncovers complex molecular interactions using functional association networks and disease-gene association databases. Complex diseases arise from genetic and environmental factors creating clusters of dysfunction within networks. These "disease modules" reveal genetic interactions in disease phenotypes and help identify novel therapeutic targets. By clustering disease-related proteins, this work advances network medicine, showcasing the power of computational techniques in understanding complex diseases.

This thesis contributes to network biology and network medicine, advancing multi-omics data integration into functional association networks, with a focus on FunCoup and network-based applications.