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Prof. Malik Yousefmalik

E-Mail: malik.yousef@gmail.com
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Associate Professor 
The Head of the Galilee Center for Digital Health Research (GalilDHR)   
Zefat Academic College
Data Science
Bioinformatics
Text Classification
Big Data

My general research interest encompasses the development of novel bioinformatics based machine learning techniques to resolve problems in bioinformatics/computational biology and text classification. I am interested in applying these computational tools to the analysis of complex and varied biomedical data, in order to establish gene-based diagnostic tests and therapeutic strategies for improving public health, by helping to understand the genetic foundation of diseases. These discoveries will further enable the development of unifying global perspectives and principles in biology that can be applied to the advancement of medical research. My postdoctoral work at the Wistar Institute Cancer Center provided me with the opportunity to work with a number of research groups on clinical projects ranging from the development of cancer diagnostics and identifying potential targets to studies on infectious diseases including extensive collaboration with investigators working on HIV.  During this time, I also developed a research program focused on identifying miRNAs and their targets.

Within bioinformatics/computational biology, my present research can be divided into four basic domains:

  • (A) Developing computational tools and novel algorithms for gene expressions datasets. My current focus on developing novel approaches based biological domain knowledge for features ranking and classifications.
  • (B) Developing computational tools and novel algorithms for microRNA (miRNA) and target identification. Studying miRNAs and their targets is an important area of research because of their role in gene expression regulation.
  • (C) Developing novel classification algorithms based ensemble clustering.
  • (D) Developing text classification based topics ranking especially biomedical text topics based machine learning.