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Your search keyword '"Mal, Chittabrata"' showing total 16 results
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16 results on '"Mal, Chittabrata"'

15. List of contributors

Author

Agarwala, Niraj, Agrawal, Ruchi, Akash, Datta, Avishek, Dey, Aziz, Danishta, Bala, Asis, Barker, James, Behera, T.K., Bhattacharya, Surajit, Boora, Rekha, Borah, Rajiv, Chakrabartty, Ishani, Chandrawanshi, Nagendra, Chaubey, Tribhuvan, Chauhan, Narsingh, Choudhury, Pritam, Das, Jitu Mani, Deepa, Elbasiouny, Heba, Elbehiry, Fathy, Emamverdian, Abolghassem, Gani, Mudasir, Ghorbani, Abazar, Ghosh, Sudipta, Gill, Ritu, Gupta, Nakul, Gill, Sarvajeet Singh, Grewal, Sapna, Jain, Pradeep Kumar, James Benjamin, Allardyce, Jha, Pooja, Kalwan, Gopal, Karabulut, Fadime, Kaur, Prabhpreet, Kukreja, Ekta, Kumar, Ashish, Kumar, Rajesh, Majumdar, Amrito Ghosh, Mal, Chittabrata, Meral, Harun, Mishra, Bishwambhar, Mohanta, Tapan Kumar, Mohanta, Yugal Kishore, Mohanty, Priti Sundar, Naeem, M., Nagpal, Sharon, Nehra, Ashima, Nehra, Kiran, Nongbet, Amilia, Ozcan, Ali, Pallavi, Panda, Jibanjyoti, Pany, Biswajit, Pehlivan, Necla, Pudake, Ramesh Namdeo, Rafiq, Summira, Rai, Shivam Kumar, Rajkhowa, Rangam, Rani, Babita, Rohela, Gulab Khan, Roy, Sanket, Saini, Pawan, Saini, Pooja, Sen, Anuska, Shah, Ali Haider, Shamim, Mohammad Zaki, Sharaya, Ritu, Sharma, Neha, Sharma, Pinki, Si, Satyabrata, Singh, P.M., Singh, Rita, Singh, Vikas, Tripathi, Ravi Mani, Tuteja, Narendra, Upadhyay, Jnanendra, and Vero, Vezolu

Published
2024
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16. Identification of Culprit Genes for Different Diseases by Analyzing Microarray Data.

Author

Banerjee AK, Ghosh S, and Mal C

Subjects
Oligonucleotide Array Sequence Analysis methods, Programming Languages, Gene Library, Software, Gene Expression Profiling methods
Abstract

The identification of disease-causing genes is the first and most important step toward understanding the biological mechanisms underlying a disease. Microarray analysis is one such powerful method that is widely used to identify genes that are expressed differently in two or more conditions (disease vs. normal). Because of its large library of statistical R packages and user-friendly interface, the R programming language provides a platform for microarray analysis. In this chapter, we will go over how to identify disease-causing culprit genes from the raw microarray data, using various packages of R programming. The pipeline overviews the steps in microarray analysis, such as data pre-processing, normalization, and statistical analysis using visualization techniques such as heatmaps, box plots, and so on. To better understand the function of the altered genes, gene ontology and pathway analysis are performed., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Published
2024
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