MSc Thesis Defense Announcement of Alexandru Filip:"DeePSLiM: A Deep Learning Approach to Identify Predictive Short-linear Motifs for Protein Sequence Classification "

Monday, December 14, 2020 - 13:00 to 15:00


The School of Computer Science is pleased to present… 

MSc Thesis Defense by: Alexandru Filip 

Date: Monday December 14, 2020 
Time:  1:00 pm – 3:00pm 
Passcode: If interested in attending this event, please contact the Graduate Secretary at for the passcode.


With the increasing quantity of biological data, it is important to develop algorithms that can quickly find patterns in large databases of DNA, RNA and protein sequences. Previous research has been very successful at applying deep learning methods to the problems of motif detection as well as classification of biological sequences. There are, however, limitations to these approaches. Most are limited to finding motifs of a single length. In addition, most research has focused on DNA and RNA, both of which use a four letter alphabet. A few of these have attempted to apply deep learning methods on the larger, twenty letter, alphabet of proteins. 
    We present an enhanced deep learning model, called DeePSLiM, capable of detecting predictive, short linear motifs (SLiM) in protein sequences. The model is a shallow network that can be trained quickly on large amounts of data. The SLiMs are predictive because they can be used to classify the sequences into their respective families. The model was able to reach scores of 94.5\% on accuracy, precision, recall, F1-Score and Matthews-correlation coefficient, as well as 99.9\% area under the receiver operator characteristic curve (AUROC). 
Keywords: Protein, Motif Discovery, Short Linear Motif, Neural Network, Machine learning 

Thesis Committee:  

Internal Reader:              Dr. Asish Mukhopadhyay 
External Reader:             Dr. Mohamed Belalia 
Advisor:                           Dr. Luis Rueda and Dr. Alioune Ngom 
Chair:                               Dr. Xiaobu Yuan 

 MSc Thesis Defense Announcement  


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