Di Meng

Di Meng

Computer Science PhD

University College Dublin

di.meng@ucdconnect.ie

Biography

I am a PhD researcher in computational biology, specializing in intrinsically disordered protein (IDP) structure and function prediction. Since 2021, I have been pursuing my PhD at the School of Computer Science, University College Dublin, Ireland, under the supervision of Dr. Gianluca Pollastri. My research focuses on applying deep learning and bioinformatics to enhance the analysis and prediction of protein disorder. I aim to complete my PhD by September 2025.

Before my PhD, I worked in various software development and data science roles across multiple companies, gaining experience in algorithm development, machine learning, and computational modeling.

Beyond research, I have a strong interest in cooking and enjoy running, casual exercise, and Yoga.

Interests

  • Deep Learning
  • Bioinformatics
  • Debugging
  • Yoga
  • Running

Education

  • PhD in Computer Science, 2021-2025

    University College Dublin, Ireland

  • Visiting Scholar, 2024

    University of Padova, Italy

  • Visiting Scholar, 2022

    National University of General San Martín, Argentina

  • Master in Computer Science, 2020

    University College Dublin, Ireland

Publications

PUNCH: An Interactive Web Server for Predicting Intrinsically Disordered Regions in Protein Sequences

PUNCH is a freely accessible web server designed for the rapid and accurate prediction of intrinsically disordered regions (IDRs) in protein sequences …

PUNCH2: EXPLORE THE STRATEGY FOR INTRINSICALLY DISORDERED PROTEIN PREDICTOR

PUNCH2 and its lighter variant, PUNCH2-light, demonstrate high efficiency and accuracy in benchmarking against top predictors on the CAID2 & CAID3 datasets …

Porter 6: Protein Secondary Structure Prediction by Leveraging Pre-Trained Language Models (PLMs)

Protein secondary structure prediction (PSSP) is a fundamental component of protein structure prediction, …

PaleAle 6.0: Prediction of Protein Relative Solvent Accessibility by Leveraging Pre-Trained Language Models (PLMs)

Predicting the relative solvent accessibility (RSA) of a protein is critical to understanding its 3D structure and biological function. RSA prediction, …
Comming Soon: DLD, PASTA3, ...

Software

PUNCH

Built on PUNCH2-Light, enables fast and accurate intrinsically disordered region prediction.

DLD

An independent-domain linker (IDL) dataset includes 1,919 IDLs.

DeepPredict

State-of-the-art protein secondary structure and RSA prediction.

PASTA3

Comming soon ...

Predicts Amyloid STructural Aggregation.

Contact

2025 Di Meng.