Cancer treatment is a complex process demanding a comprehensive understanding of the disease mechanism, individual differences, and early diagnosis to achieve better therapeutic yield. Biomarker discovery, the core of precision medicine, has revolutionized the field of cancer biology by bridging multiple disciplines to identify the most appropriate targets for arresting or ideally, killing cancer cells. The interdisciplinary approaches have drastically improved the precision and speed of discoveries by integrating the data acquired at various biological levels into unified and explicit output for therapeutic development. This has led to the detection of novel proteins and neoepitopes rooted in an understudied portion of the proteomes called dark proteome or due to genomic mutation or epigenetic modulation of cancer stem cells. Computational models and artificial intelligence offer great opportunities to explore ever-growing datasets to find the most promising candidates, predict their structural configuration, and determine the possible contribution of the candidate biomarkers in the biological alteration of cells. Here, we discuss the necessity of understanding the context and the value of merging experimental techniques with computational models to study intercellular and molecular interactions. The interdisciplinary approaches facilitate the generation of therapeutic drugs with higher efficacy for clinical applications and detection probes for experimental and diagnostic purposes.