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OnahPmi/README.md

Biography

Hello, my name is Emmanuel Onah, a passionate individual with a deep interest in Python, Machine Learning, and Bioinformatics. My enthusiasm lies in the realm of data analytics and machine learning, specifically focusing on Cheminformatics. I am dedicated to developing predictive models and algorithms that can revolutionize the understanding of Ligand-protein interactions. By leveraging these tools, I aim to accelerate the process of drug lead identification and optimization, enabling faster and more informed decision-making. I am eagerly seeking collaboration opportunities with like-minded professionals to create innovative programs that have the power to reshape and elevate existing methodologies. My ultimate goal is to contribute to the field of Bioinformatics, where I believe technology can play a vital role in transforming the way we approach complex biological problems.

Feel free to connect with me at https://www.linkedin.com/in/emmanuelonah/. You can reach me via onahemma111@gmail.com as I am always open to engaging in exciting Bioinformatics projects. You can also explore my GitHub profile for a glimpse into my work and accomplishments. Together, let's embark on a journey to create remarkable solutions that push the boundaries of scientific exploration and improve lives.

Google Scholar Link: https://scholar.google.com/citations?view_op=list_works&hl=en&user=oKh6VTYAAAAJ
Research Gate Link: https://www.researchgate.net/profile/Emmanuel-Onah-4

Publications

Onah E, Eze UJ, Abdulraheem AS, Ezigbo UG, Amorha KC (2024). Optimizing Unsupervised Feature Engineering and Predictive Models for Thyroid Cancer Recurrence Prediction. Preprints 2024, 2024092121. https://doi.org/10.20944/preprints202409.2121.v1

Ibezim A, Onah E, Osigwe SC, Okoroafor PU, Ukoha OP, de Siqueira-Neto JL, Ntie-Kang F, & Ramanathan K. (2024). Potential dual inhibitors of Hexokinases and mitochondrial complex I discovered through machine learning approach. Scientific African, 24, e02226.
https://doi.org/10.1016/j.sciaf.2024.e02226.

Onah E, Uzor PF, Ugwoke IC, et al. Prediction of HIV-1 protease cleavage site from octapeptide sequence information using selected classifiers and hybrid descriptors. BMC Bioinformatics. 2022;23(1):466. doi:10.1186/s12859-022-05017-x.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9641908/

Ibezim A, Onah E, Dim EN, Ntie-Kang F. A computational multi-targeting approach for drug repositioning for psoriasis treatment. BMC Complement Med Ther. 2021;21(1): 193. doi:10.1186/s12906-021-03359-2.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8258956/

Onah, E., Ugwoke, I., Eze, U., Eze, H., Musa, S., Ndiana-Abasi, S., Okoli, O., Ekeh, I., & Edet, A. (2021). Search for Structural Scaffolds Against SARS-COV-2 Mpro: An In Silico Study. Journal of Fundamental and Applied Sciences, 13(2), 740-769. https://jfas.info/index.php/JFAS/article/view/987.

Projects

  1. ChemFetchTool (Link ⟶ ChemFetchTool)

  2. HIV1-LogRex Webserver (Link ⟶ HIV1-LogRex Webserver)

  3. Multi Ligands Docking with AutoDock Vina (Link ⟶ Multi Ligands Docking with AutoDock Vina)

Pinned Loading

  1. ChemFetchTool Public

    ChemFetchTool is a web-based tool employing the PubChem PUG REST API endpoint for automated retrieval of molecular properties given only the compound name(s)

    Python 1

  2. HIV-1-LogRex Public

    HIV1-LogRex implements the logistic regression model for HIV-1 protease octapeptide cleavage site prediction and varied algorithms for octapeptide descriptors (amino acid binary profile (AABP), phy…

    Python

  3. Thyroid-Cancer-Recurrence-Prediction-Project Public

    This Repository contains the code base of our Studies Titled: `Optimizing Unsupervised Feature Engineering and Predictive Models for Thyroid Cancer Recurrence Prediction.`

    Jupyter Notebook

  4. HIV-1-Protease-Cleavage-Site-Prediction-Project Public

    This repo contains the jupyter notebooks for the prediction of HIV-1 protease cleavage site using simple machine learning algorithms

    Jupyter Notebook

  5. Multiple-Ligand-Docking-with-Vina-Executables Public

    This repository contain executables for performing multi ligand docking (virtual screening) with AutoDock vina