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About

About

Md Wakil Ahmad is a dedicated and ambitious student currently pursuing a Master's degree in Data Science and Engineering. With a solid foundation in Computer Science from his undergraduate studies, Wakil has developed a strong passion for harnessing data to generate insights and solve complex problems. Throughout his academic journey, Wakil has honed his skills in statistical analysis, machine learning, data visualization, and computational modeling. He is proficient in a variety of programming languages and tools including Python, R, SQL, and Hadoop, as well as data visualization tools such as Tableau. Wakil's inquisitive nature has led him to work on a variety of data-driven projects involving predictive modeling, natural language processing, and data mining. He is particularly drawn to the application of data science techniques in the realm of environmental sustainability, with the aim to contribute positively to this critical field.

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Details

Details

  • Name

    Md Wakil Ahmad
  • Role

    Research Assistant
  • Since

    13th March 2023
001
Publications

2024

Accurate Prediction of Lysine Methylation Sites Using Evolutionary and Structural-Based Information

Authors
Arafat, ME; Ahmad, MW; Shovan, SM; Ul Haq, T; Islam, N; Mahmud, M; Kaiser, MS;

Publication
COGNITIVE COMPUTATION

Abstract
Methylation is considered one of the proteins' most important post-translational modifications (PTM). Plasticity and cellular dynamics are among the many traits that are regulated by methylation. Currently, methylation sites are identified using experimental approaches. However, these methods are time-consuming and expensive. With the use of computer modelling, methylation sites can be identified quickly and accurately, providing valuable information for further trial and investigation. In this study, we propose a new machine-learning model called MeSEP to predict methylation sites that incorporates both evolutionary and structural-based information. To build this model, we first extract evolutionary and structural features from the PSSM and SPD2 profiles, respectively. We then employ Extreme Gradient Boosting (XGBoost) as the classification model to predict methylation sites. To address the issue of imbalanced data and bias towards negative samples, we use the SMOTETomek-based hybrid sampling method. The MeSEP was validated on an independent test set (ITS) and 10-fold cross-validation (TCV) using lysine methylation sites. The method achieved: an accuracy of 82.9% in ITS and 84.6% in TCV; precision of 0.92 in ITS and 0.94 in TCV; area under the curve values of 0.90 in ITS and 0.92 in TCV; F1 score of 0.81 in ITS and 0.83 in TCV; and MCC of 0.67 in ITS and 0.70 in TCV. MeSEP significantly outperformed previous studies found in the literature. MeSEP as a standalone toolkit and all its source codes are publicly available at https://github.com/arafatro/MeSEP.