Comparative analysis of prokaryotic and eukaryotic transcription factors using machine-learning techniques

被引:3
|
作者
Chowdhury, Nilkanta [1 ]
Bagchi, Angshuman [1 ]
机构
[1] Univ Kalyani, Dept Biochem & Biophys, Kalyani 741235, Nadia, India
关键词
Prokaryotic and Eukaryotic Organisms; DNA binding proteins; Transcription factors; Distribution of amino acid residues;
D O I
10.6026/97320630014315
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
The DNA-protein interactions play vital roles in the central dogma of molecular biology. Proper interactions between DNA and protein would lead to the onset of various biological phenomena like transcription, translation, and replication. However, the mechanisms of these well-known processes vary between prokaryotic and eukaryotic organisms. The exact molecular mechanisms of these processes are unknown. Therefore, it is of interest to report the comparative estimate of the different properties of the DNA binding proteins from prokaryotic and eukaryotic organisms. We analyzed the different sequence-based features such as the frequency of amino acids and amino acid groups in the proteins of prokaryotes and eukaryotes by statistical measures. The general pattern of differences between the various DNA binding proteins for the development of a prediction system to discriminate between these proteins between prokaryotes and eukaryotes is documented.
引用
收藏
页码:315 / 326
页数:12
相关论文
共 50 条
  • [1] Mental Health Predictive Analysis Using Machine-Learning Techniques
    Jain, Vanshika
    Kumari, Ritika
    Bansal, Poonam
    Dev, Amita
    [J]. SMART TRENDS IN COMPUTING AND COMMUNICATIONS, VOL 4, SMARTCOM 2024, 2024, 948 : 103 - 115
  • [2] Machine-Learning Techniques for Customer Retention: A Comparative Study
    Sabbeh, Sahar F.
    [J]. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2018, 9 (02) : 273 - 281
  • [3] DVFS Binning Using Machine-Learning Techniques
    Chang, Keng-Wei
    Huang, Chun-Yang
    Mu, Szu-Pang
    Huang, Jian-Min
    Chen, Shi-Hao
    Chao, Mango C-T
    [J]. 2018 IEEE INTERNATIONAL TEST CONFERENCE IN ASIA (ITC-ASIA 2018), 2018, : 31 - 36
  • [4] DeepReg: a deep learning hybrid model for predicting transcription factors in eukaryotic and prokaryotic genomes
    Ledesma-Dominguez, Leonardo
    Carbajal-Degante, Erik
    Moreno-Hagelsieb, Gabriel
    Perez-Rueda, Ernesto
    [J]. SCIENTIFIC REPORTS, 2024, 14 (01)
  • [5] Extreme Learning Machine for Eukaryotic and Prokaryotic Promoter Prediction
    Vesapogu, Praveen Kumar
    Surampudi, Bapi Raju
    [J]. PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON FUZZY AND NEURO COMPUTING (FANCCO - 2015), 2015, 415 : 313 - 322
  • [6] Video Recommendation System Using Machine-Learning Techniques
    Meesala Sravani
    Ch Vidyadhari
    S Anjali Devi
    [J]. JournalofHarbinInstituteofTechnology(NewSeries), 2024, 31 (04) : 24 - 33
  • [7] Improving sequence tagging using machine-learning techniques
    Jiang, Wei
    Wang, Xiao-Long
    Guan, Yi
    [J]. PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2006, : 2636 - +
  • [8] Investigation of the factors affecting reverse osmosis membrane performance using machine-learning techniques
    Odabasi, Cagla
    Dologlu, Pelin
    Gulmez, Fatih
    Kusoglu, Gizem
    Caglar, Omer
    [J]. COMPUTERS & CHEMICAL ENGINEERING, 2022, 159
  • [9] Mortality Prediction using Machine Learning Techniques: Comparative Analysis
    Verma, Akash
    Goyal, Shreya
    Thakur, Shridhar Kumar
    Gupta, Archit
    Gupta, Indrajeet
    [J]. PROCEEDINGS OF THE 2019 IEEE 9TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING (IACC 2019), 2019, : 230 - 234
  • [10] A comparative analysis of data sets using Machine Learning techniques
    Abhilash, C.B.
    Rohitaksha, K.
    Biradar, Shankar
    [J]. Souvenir of the 2014 IEEE International Advance Computing Conference, IACC 2014, 2014, : 24 - 29