Michael Akpabey

I am a highly skilled computational biologist with a Master of Science in Computational Biology and Bioinformatics from the University of Michigan. With a GPA of 3.9, I have a strong academic background in data science, multi-omics, and machine learning. My expertise lies in programming languages such as Python, R, Unix/Bash, SQL, and Perl. I am also proficient in machine learning frameworks like TensorFlow, PyTorch, and Pandas.

Throughout my academic journey, I have worked on several cutting-edge projects that have contributed to the field of bioinformatics. For example, I developed TransEpiScape, a state-of-the-art bioinformatics tool that visualizes relationships between human transcriptomic profiles and epigenetic landscapes. This tool has enhanced scientific insights on gene regulation. I also developed GCN_scRNA, a tool that predicts the metastatic potential of cancer cells based on ECM signatures and spatial coordinates, contributing to early diagnosis and precision medicine efforts.

In my current role as a Graduate Research Associate at the University of Michigan, I have developed robust pipelines and workflows for the analysis of RNAseq and epigenetics data. This work has revealed links between toxic chemical exposures and adverse health outcomes for vulnerable populations. I am proficient in data analysis, visualization, and biostatistical modeling, and I have contributed to assay validation and experimental design. I am also experienced in automating and improving analysis methods through innovative algorithm development.