Md Abid Hasan

PhD Candidate

Advisor: Prof. Stefano Lonardi

Algorithms and Computational Biology Lab
Department of Computer Science and Engineering
Engineering Building Unit II, Room 362
University of California
Riverside CA 92521



About me

I am Md Abid Hasan. I prefer Abid. I was raised in Dhaka, Bangladesh. I obtained my Bachelors and Masters in Computer Science and Enginnering from Islamic University of Technology (IUT). It is one of the most prestigious engineering universities in Bangladesh. After graduating top of my class, I worked as a lecturer at the same university. During this period I completed my masters. In 2014, I moved to USA for my Ph.D. at University of California Riverside. Currently I am a fifth-year Ph.D. student at the Algorithms and Computational Biology lab. On summer 2018, I had the opportunity to work as a Bioinformatics intern at Roche Sequencing Solutions at Pleasanton, CA. My research interests are Computational Biology, Machine learning and statistical model in bioinformatics, Cancer genomics, and Deep learning in bioinformatics.

Current Research

Gene essentiality prediction in 30 bacterial species.

Essential genes are critical for the survival of an organism. The prediction of essential genes in bacteria can provide targets for the design of novel antibiotic compounds or antimicrobial strategies. We have built a deep neural network classifier (DeeplyEssential) to classify essential and non-essential genes in these bacteria species. Our model only uses sequence-based features to predict the gene essentiality. Moreover we also expose and study a hidden performance bias that affected the previous classifier.

Fig: DeeplyEssential model for gene essentiality prediction

HiC interaction prediction with epigenetics data

On this ongoing project, we are trying to predict HiC interaction between two regions using epigenetic signals. HiC interaction can have major roles in tissue-specific expression and how regulatory sequence variants impact complex phenotype including diseases such as cancer diabetes and obesity. There are very few HiC datasets available at high resolution since the experiment is very expensive. As a result, the computational approach for predicting long-range interaction.

In this work, we are building feature matrices from the epigenetic signals which then processes by a convolutional neural network for predicting the interaction between two regions. In this work, we will be investigating combinatorial relationships among features, and the ability of the predicted interactions to identify, topologically associated domains (TADs), loops, contact counts in different cell lines etc.

Fig: HiC interaction prediction model


  1. MA Hasan, S Lonardi "DeeplyEssential: A Deep Neural Network for Predicting Essential Genes in Microbes" [Submitted]
  2. MA Hasan, S Lonardi "mClass: Cancer Type Classification with Somatic Point Mutation Data", RECOMB International conference on Comparative Genomics, 131-145, 2018, Quebec, Canada.
  3. A. Polishko, M. A. Hasan , W. Pan, Evelien M. B. Karine L. R. Stefano L. “ThIEF: Finding Genome-wide Trajectories of Epigenetic Marks”, 17th International Workshop on Algorithms in Bioinformatics (WABI) 2017, Boston, USA.
  4. Hasan M A, Hasan M K and Mottalib M A Linear Regression based Feature Selection for Microarray Data Classification, Int. J. Data Mining and Bioinformatics, 2015, Vol 11, Issue 2, pp. 167-179.
  5. K M Mutakabbir, S S Mahin, M A Hasan, Mining Frequent Pattern Within a Genetic Sequence Using Unique Pattern Indexing and Mapping Techniques, IEEE International Conference on Informatics, Electronics & Vision (ICIEV), pp. 1–5, 2014, Dhaka, Bangladesh.
  6. Hasan A, Adnan M A, High Dimensional Microarray Data Classification Using Correlation Based Feature Selection, International Conference on Biomedical Engineering (ICoBE), pp. 319 – 321, 2012, Penang, Malaysia.