Dr. M Taseer Suleman

PhD Theme/Topic: “Deep Computational Intelligence for Predicting Post-Transcriptional Modifications Across the Epitranscriptome”

Supervisor: Dr. M Taseer Suleman/ Assistant Professor
Contact #:+923157522862
Email: taseersuleman.bulc@bahria.edu.pk
Campus/School/Dept: BULC/CS
RAC Approved Supervisor for Research Areas: Bioinformatics, Pattern Recognition, Machine Learning

Supervisory Record:
PhD Produced:0
PhD Enrolled:0
MS/MPhil Produced:0
MS/MPhil Enrolled:0
Co-Supervisor: NA

Topic Brief Description:

Prediction of Post-Transcriptional Modification Sites in RNA Using Computational Intelligence involves the application of advanced machine learning and artificial intelligence (AI) techniques to identify specific locations on RNA sequences where chemical modifications, such as methylation or acetylation, occur after transcription. These modifications play critical roles in regulating RNA stability, localization, and function. By leveraging computational models, including sequence-based feature extraction, pattern recognition, and classification algorithms, this approach enhances the accuracy and efficiency of predicting modification sites. This research area is vital for understanding RNA biology, disease mechanisms, and therapeutic development.

Research Objectives/Deliverables:

1. Design and implement computational models to accurately predict post-transcriptional modification sites in RNA sequences.
2. Identify and extract biologically relevant sequence features, such as k-mer patterns, secondary structure, and physicochemical properties, to improve model interpretability and accuracy.
3. Ensure the model is scalable to different species and RNA types with minimal retraining.
4. Create a user-friendly computational tool or web server for researchers to predict RNA modification sites.

Research Questions:

1. How can computational intelligence techniques be optimized to accurately predict post-transcriptional modification sites in RNA?
2. Which ML/DL architecture offers the most effective performance for identifying post-transcriptional RNA modification sites?
3. What sequence features, structural properties, or physicochemical characteristics contribute most to model performance in predicting RNA modification sites?
4. To what extent can computationally predicted modification sites reveal potential RNA-based biomarkers or therapeutic intervention points?

Candidate’s Eligibility Profile:

1. The applicant must have an MS/MPhil/Equivalent degree in CS/IT/SE/AI with CGPA > 3.0. Besides, applicants must have a strong background in statistical analysis, machine learning and related fields.
2. Experience with programming languages such as Python, R C/C++, is advantageous. Candidates should thrive in an international environment and have excellent communication skills to actively contribute to team research efforts.
3. Proficiency in spoken and written English is essential. We value independence and responsibility while promoting teamwork and collaboration among colleagues.