Mass spectrometry is a powerful tool to identify, quantify, and characterize diverse biological molecules. As a result, it has become a mainstay in modern biology enabling studies ranging from the structural characterization of a single lipid, to the identification and quantification of complete proteomes. With the increasing abundance of high-throughput mass spectrometry experiments, the major challenge has shifted from acquisition to interpretation. In most cases, these high-throughput experiments require inference beyond the directly measured characteristics of the analyte to draw insightful biological conclusions. However, this can be difficult, particularly when the outcomes of an experiment are not clearly defined. To address this challenge, we developed novel experimental and computational methods that utilize high-throughput mass spectrometry techniques to answer specific biological questions. We first describe a method called serial dilution-affinity purification-mass spectrometry (SDAP-MS) to identify and characterize protein-protein interactions. When investigating interactions to a protein of interest, researchers must decide between high-throughput screening methods that merely yield binary results, or low-throughput approaches that yield insight into the biochemical properties of these interactions. The SDAP-MS approach alleviates this burden by providing a high-throughput screening method capable of estimating equilibrium binding constants, which we demonstrate using two LDL receptor family members, LRP1 and LRP1B. We then describe a technique to discern the protein cargo of exosomes from contaminants co-isolated during purification. Exosomes are microvesicles and potential carriers of biomarkers and, as a result, it is vital to ensure that the proteins attributed to the exosome are not artifacts from the isolation strategy. To assess this, we present an approach that uses proteomics and machine learning to investigate the enrichment of proteins across multiple stages of exosome isolation, culminating in a score that indicates the confidence of each protein being of exosomal origin. Finally, we develop a machine learning approach to identify microbial pathogens from glycolipid mass spectra, enabling their rapid diagnosis. We show that Acinetobacter baumannii and Klebsiella pneumoniae can be identified from a library containing 48 additional organisms in both isolate and polymicrobal specimens. Together, these studies present methods that result in valuable insight beyond analyte identification and quantification by mass spectrometry.