Alternative Splicing (AS) is a process that is believed to have links to some diseases in humans. Although AS was discovered in the 1970s, not much research has been conducted on its role in functional implications on the proteome level. This study aims to use PIPE, a protein-protein interaction prediction algorithm, along with a tissue expression dataset to build a pipeline that differentiates between AS isoform products by analyzing their sequence, functional, and tissue expression changes. The study found that there is a statistically significant overlap between PIPE-predicted protein-protein interaction (PPI) network changes and tissue expression changes of alternatively spliced isoforms (ASIs) relative to their canonical isoforms (CIs) with a p-value of 0.0000825. Finally, our study results suspect that LMO2, THOC2, and UBE2L3 are genes that have links to different disease such as basel-type breast cancer, intellectual disability (ID) and numerous autoimmune diseases mirroring similar findings from literature studies.