Why do you think your paper is highly cited?

Nuclear proteins operating in related pathways, or those that share common functionality, tend to be localized in specific compartments within the nucleus. Protein subnuclear localization prediction has tremendous biological significance as the mislocalization of proteins can lead to genetic diseases and cancer. Our support vector machine-based methodologies have yielded more accurate predictions.

Does it describe a new discovery, methodology, or synthesis of knowledge?

This paper studies different methods for extracting knowledge from sequence information in the form of features for machine learning algorithms. These include evolutionary information in the form of position specific scoring matrix (PSSM) features, pseudo amino acid composition features (as proposed by Kuo-Chen Chou at the Gordon Life Science Institute in San Diego) and five factor solution score features (as derived by William R. Atchley of the Department of Genetics at North Carolina State University) using nearly 500 amino acid properties.

Would you summarize the significance of your paper in layman's terms?

Our methodology can speed up the process of protein annotation and subsequent relevant discoveries.

How did you become involved in this research, and were there any problems along the way?

We have been working on important in-silico predictions of protein and gene functions for the past several years and found this particular problem to be potentially crucial. We found that the biggest difficulty was in determining how to choose a relevant dataset. Hong-Bin Shen and Kuo-Chen Chou's previous work on this issue, at the Institute of Image Processing and Pattern Recognition of Shanghai's Jiaotong University, led to our utilization of their same dataset, which demonstrated excellent prediction results.

Where do you see your research leading in the future?

Considering its biological implications, we believe more research attention will be diverted towards protein subnuclear localization prediction problems. Newer methods for mathematically representing protein sequences may be proposed to further increase prediction accuracy.

Do you foresee any social or political implications for your research?

Our work could speed up the nuclear protein annotation process, which may help the medical community, particularly those individuals working in the fields of human genetics and cancer research.

Valadi K. Jayaraman, Ph.D.
Senior Scientist, Chemical Engineering and Process Development Division
National Chemical Laboratory (NCL)
Pune, India

Bhaskar D. Kulkarni, Ph.D.
Deputy Director & Head, Chemical Engineering and Process Development Division
National Chemical Laboratory (NCL)
Pune, India

Piyushkumar Mundra
Ph.D. student
School of Computer Engineering
Nanyang Technological University
Singapore

Madhan Kumar, B.Tech.
Software Engineer
Cognizant Technology Solutions Corporation
Chennai, India

Krishna Kumar Kandaswamy
Ph.D. student
University of Luebeck
Luebeck, Germany

Keywords: nuclear proteins, protein subnuclear localization prediction, mislocalization of proteins, support vector machine-based methodologies, machine learning algorithms, position specific scoring matrix, pseudo amino acid composition features, five factor solution score features, protein subnuclear localization prediction problems, nuclear protein annotation process.