M. KAMEL Mohamed

MCB

Directory of teachers

Department

Informatics Department

Research Interests

Algorithms Bioinformatics Optimisation Machine Learning Deep Learning

Contact Info

University of M'Sila, Algeria

Email : mohamed.kamel@univ-msila.dz

On the Web:

  • Google Scholar N/A
  • ResearchGate
    ResearchGate N/A
  • ORCID N/A
  • SC
    Scopus N/A

Recent Publications

2024-07-03

A robust two-step algorithm for community detection based on node similarity

The rapid development of the internet and social network platforms has given rise to a new field of research, social network analysis. This field of research has many fundamental problems, one of which is community detection. The objective of this research is to understand hidden connections among individuals. However, uncovering these connections are still challenging, despite the existence of several methods. In this paper, we propose a new algorithm called MCCD (Modified Cosine for Community Detection) for community detection in social networks based on node similarity. Our algorithm consists of two steps. In the first step, we use a novel cosine similarity formula to identify initial communities. In the second step, we merge these communities based on a new similarity measure. MCCD can be used in two different ways. The first way uses K as an input to identify the exact communities. The second way does not require K and aims to provide the best partitioning by maximizing modularity. Our algorithm has been tested on a variety of artificial and real-world networks, and the experimental results demonstrate its superiority over existing methods in detecting communities.
Citation

M. KAMEL Mohamed, (2024-07-03), "A robust two-step algorithm for community detection based on node similarity", [national] The Journal of Supercomputing , Springer US

Read Full Paper
2021

REP2: A Web Server to Detect Common Tandem Repeats in Protein Sequences

Ensembles of tandem repeats (TRs) in protein sequences expand rapidly to form domains well suited for interactions with proteins. For this reason, they are relatively frequent. Some TRs have known structures and therefore it is advantageous to predict their presence in a protein sequence. However, since most TRs diverge quickly, their detection by classical sequence comparison algorithms is not very accurate. Previously, we developed a method and a web server that used curated profiles and thresholds for the detection of 11 common TRs. Here we present a new web server (REP2) that allows the analysis of TRs in both individual and aligned sequences. We provide currently precomputed analyses for a selection of 78 UniProt reference proteomes. We illustrate how these data can be used to study the evolution of TRs using comparative genomics. REP2 can be accessed at http://cbdm-01.zdv.uni-mainz.de/~munoz/rep/
Citation

M. KAMEL Mohamed, (2021), "REP2: A Web Server to Detect Common Tandem Repeats in Protein Sequences", [national] Journal of Molecular Biology , Elsevier, Academic Press

Read Full Paper
2019

Repeatability in protein sequences

Low complexity regions (LCRs) in protein sequences have special properties that are very different from those of globular proteins. The rules that define secondary structure elements do not apply when the distribution of amino acids becomes biased. While there is a tendency towards structural disorder in LCRs, various examples, and particularly homorepeats of single amino acids, suggest that very short repeats could adopt structures very difficult to predict. These structures are possibly variable and dependant on the context of intra- or inter-molecular interactions. In general, short repeats in LCRs can induce structure. This could explain the observation that very short (non-perfect) repeats are widespread and many define regions with a function in protein interactions. For these reasons, we have developed an algorithm to quickly analyze local repeatability along protein sequences, that is, how close a protein fragment is from a perfect repeat. Using this algorithm we identified that the proteins of the yeast Saccharomyces cerevisiae are depleted in short repeats (approximate or not) of odd-length, while the human proteins are not, that the fish Danio rerio has many proteins with repeats of length two and that the plant Arabidopsis thaliana has an unusually large amount of repeats of length seven. Our method (REpeatability Scanner, RES, accessible at http://cbdm-01.zdv.uni-mainz.de/~munoz/res/) allows to find regions with approximate short repeats in protein sequences, and helps to characterize the variable use of LCRs and compositional bias in different organisms.
Citation

M. KAMEL Mohamed, (2019), "Repeatability in protein sequences", [national] Journal of Structural Biology , Elsevier, Academic Press

Read Full Paper
← Back to Researchers List