Skip to main content

Advertisement

SpringerLink
Log in

Enhanced prediction of anti-tubercular peptides from sequence information using divergence measure-based intuitionistic fuzzy-rough feature selection

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Tuberculosis is one of the leading causes of millions of deaths across the world, mainly due to growth of drug-resistant strains. Anti-tubercular peptides may facilitate an alternate way to combat antibiotic tolerance. This study describes a novel approach for enhancing the prediction of anti-tubercular peptides by feature extraction from sequence of the peptides, selection of optimal features from the extracted features, and selection of suitable learning algorithm. Firstly, we extract different sequence features by using iFeature web server. Then, the optimal features are obtained by using a novel divergence measure-based intuitionistic fuzzy rough sets-assisted feature selection technique. Furthermore, an attempt has been made to develop models using different machine learning techniques for enhancing the prediction of anti-tubercular (or anti-mycobacterial peptides) with other antibacterial peptides (ABP) as well non-antibacterial peptides (non-ABP). Moreover, the best prediction result is obtained by vote-based classifier. Using 80:20 percentage split, the proposed method performs well, with sensitivity of 92.0%, 96.4%, specificity of 83.3%, 88.4%, overall accuracy of 87.80%, 92.90%, Mathews correlation coefficient of 0.757, 0.857, AUC of 0.922, 0.914, and g-means of 87.5%, 92.3% for anti-tubercular and ABP (primary dataset), anti-tubercular and non-ABP (secondary dataset), respectively. Finally, we have evaluated the performances of different machine learning algorithms by using the reduced training sets as produced by our proposed feature selection technique as well as already existing intuitionistic fuzzy rough set based and ensemble feature selection technique. Moreover, the performance of our proposed approach is evaluated on few benchmark and AMP datasets. From the experimental results, it can be observed that our proposed method is outperforming the previous methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Ashraf M, Zaman M, Ahmed M (2019) To ameliorate classification accuracy using ensemble vote approach and base classifiers. In: Abraham A, Dutta P, Mandal JK, Bhattacharya A, Dutta S (eds) Emerging technologies in data mining and information security. Springer, Berlin, pp 321–334

    Chapter  Google Scholar 

  • Atanasov KT (1999) Intuitionistic fuzzy sets: theory and applications (Studies in Fuzziness and Soft Computing), vol 35. Physica-Verlag, Heidelberg

    Book  Google Scholar 

  • Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96

    Article  MATH  Google Scholar 

  • Atanassov KT (1989) More on intuitionistic fuzzy sets. Fuzzy Sets Syst 33(1):37–45

    Article  MathSciNet  MATH  Google Scholar 

  • Barnagarwala T (2014) TB hospital staff live under shadow of dreaded disease, The Indian Express. Uttar Pradesh, India: IE Online Media Services

  • Bhasin M, Raghava GPS (2004) Classification of nuclear receptors based on amino acid composition and dipeptide composition. J Biol Chem 279(22):23262–23266

    Article  Google Scholar 

  • Bhat ZS, Rather MA, Maqbool M, Lah HU, Yousuf SK, Ahmad Z (2017) Cell wall: a versatile fountain of drug targets in Mycobacterium tuberculosis. Biomed Pharmacother 95:1520–1534

    Article  Google Scholar 

  • Blake C, Merz C (1998) UCI repository of machine learning databases

  • Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140

    MATH  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  MATH  Google Scholar 

  • Bustince H, Mohedano V (1997) About the intuitionistic fuzzy set generators. Notes Intuit Fuzzy Sets 3:21–27

    MathSciNet  MATH  Google Scholar 

  • Cai CZ (2003) SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Res 31(13):3692–3697

    Article  Google Scholar 

  • Cai CZ, Han LY, Ji ZL, Chen YZ (2004) Enzyme family classification by support vector machines. Proteins: Struct, Funct, Bioinf 55(1):66–76

    Article  Google Scholar 

  • Chakrabarty K, Gedeon T, Koczy L (2003) Intuitionistic fuzzy rough set. Wiley, Hoboken, pp 211–214

    Google Scholar 

  • Charoenkwan P, Yana J, Schaduangrat N, Nantasenamat C, Hasan MM, Shoombuatong W (2020) iBitter-SCM: identification and characterization of bitter peptides using a scoring card method with propensity scores of dipeptides. Genomics 112(4):2813–2822. https://doi.org/10.1016/j.ygeno.2020.03.019

    Article  Google Scholar 

  • Chen H, Yang H (2011) One new algorithm for intuitiontistic fuzzy-rough attribute reduction. J Chin Comput Syst 32(3):506–510

    MathSciNet  Google Scholar 

  • Chen D, Hu Q, Yang Y (2011a) Parameterized attribute reduction with Gaussian kernel based fuzzy rough sets. Inf Sci 181(23):5169–5179

    Article  MATH  Google Scholar 

  • Chen Z, Chen Y-Z, Wang X-F, Wang C, Yan R-X, Zhang Z (2011b) Prediction of ubiquitination sites by using the composition of k-spaced amino acid pairs. PLoS ONE 6(7):e22930

    Article  Google Scholar 

  • Chen D, Kwong S, He Q, Wang H (2012a) Geometrical interpretation and applications of membership functions with fuzzy rough sets. Fuzzy Sets Syst 193:122–135

    Article  MathSciNet  MATH  Google Scholar 

  • Chen D, Zhang L, Zhao S, Hu Q, Zhu P (2012b) A novel algorithm for finding reducts with fuzzy rough sets. IEEE Trans Fuzzy Syst 20(2):385–389

    Article  Google Scholar 

  • Chen Z, Zhou Y, Song J, Zhang Z (2013) hCKSAAP_UbSite: improved prediction of human ubiquitination sites by exploiting amino acid pattern and properties. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 1834(8):1461–1467

    Article  Google Scholar 

  • Chen Z, Zhao P, Li F, Leier A, Marquez-Lago TT, Wang Y, Webb GI, Smith AI, Daly RJ, Chou K-C, Song J (2018) iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics 34(14):2499–2502

    Article  Google Scholar 

  • Chou K-C (2001) Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins Struct Funct Genet 43(3):246–255

    Article  Google Scholar 

  • Chou KC (2004) Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 21(1):10–19

    Article  Google Scholar 

  • Çoker D (1998) Fuzzy rough sets are intuitionistic L-fuzzy sets. Fuzzy Sets Syst 96(3):381–383

    Article  MathSciNet  MATH  Google Scholar 

  • Cornelis C, De Cock M, Kerre EE (2003) Intuitionistic fuzzy rough sets: at the crossroads of imperfect knowledge. Expert Syst 20(5):260–270

    Article  Google Scholar 

  • De SK, Biswas R, Roy AR (2016) Intuitionistic fuzzy database. IEEE, New York, p 43-31

    Google Scholar 

  • Degang C, Suyun Z (2010) Local reduction of decision system with fuzzy rough set. Fuzzy Sets Syst 161(13):1871–1883

    Article  MathSciNet  MATH  Google Scholar 

  • Ding C, Yuan L-F, Guo S-H, Lin H, Chen W (2012) Identification of mycobacterial membrane proteins and their types using over-represented tripeptide compositions. J Proteom 77:321–328

    Article  Google Scholar 

  • Dubchak I, Muchnik I, Holbrook SR, Kim SH (1995) Prediction of protein folding class using global description of amino acid sequence. Proc Natl Acad Sci 92(19):8700–8704

    Article  Google Scholar 

  • Dubchak I, Muchnik I, Mayor C, Dralyuk I, Kim S-H (1999) Recognition of a protein fold in the context of the SCOP classification. Struct Funct Genet 35(4):401–407

    Article  Google Scholar 

  • Dubois D, Prade H (1990) Rough fuzzy sets and fuzzy rough sets. Int J General Syst 17(2–3):191–209

    Article  MATH  Google Scholar 

  • Dubois D, Prade H (1992) Putting rough sets and fuzzy sets together. Intelligent decision support. Springer, Cham, pp 203–232

    MATH  Google Scholar 

  • Esmail H, Maryam J, Habibolla L (2013) Rough set theory for the intuitionistic fuzzy information. Syst Int J Modern Math Sci 6(3):132–143

    Google Scholar 

  • Feng Z-P, Zhang C-T (2000) Prediction of membrane protein types based on the hydrophobic index of amino acids. J Protein Chem 19(4):269–275

    Article  Google Scholar 

  • Frank E, Witten IH (1998) Generating accurate rule sets without global optimization

  • Grabisch M, Murofushi T, Sugeno M (2000) Fuzzy measures and integrals-theory and applications. Physica Verlag, Berlin

    MATH  Google Scholar 

  • Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software. ACM SIGKDD Explor Newslett 11(1):10

    Article  Google Scholar 

  • Han LY (2004) Prediction of RNA-binding proteins from primary sequence by a support vector machine approach. RNA 10(3):355–368

    Article  Google Scholar 

  • Horne DS (1988) Prediction of protein helix content from an autocorrelation analysis of sequence hydrophobicities. Biopolymers 27(3):451–477

    Article  Google Scholar 

  • Houben RM, Dodd PJ (2016) The global burden of latent tuberculosis infection: a re-estimation using mathematical modelling. PLoS Med 13(10):e1002152

    Article  Google Scholar 

  • Hu Q, Yu D, Xie Z (2006) Information-preserving hybrid data reduction based on fuzzy-rough techniques. Pattern Recogn Lett 27(5):414–423

    Article  Google Scholar 

  • Hu Q, Zhang L, Chen D, Pedrycz W, Yu D (2010) Gaussian kernel based fuzzy rough sets: model, uncertainty measures and applications. Int J Approx Reason 51(4):453–471

    Article  MATH  Google Scholar 

  • Huang B, Li HX, Wei D-K (2012) Dominance-based rough set model in intuitionistic fuzzy information systems. Knowl-Based Syst 28:115–123

    Article  Google Scholar 

  • Huang B, Zhuang Y-L, Li H-X, Wei D-K (2013) A dominance intuitionistic fuzzy-rough set approach and its applications. Appl Math Model 37(12–13):7128–7141

    Article  MathSciNet  MATH  Google Scholar 

  • Iancu I (2014) Intuitionistic fuzzy similarity measures based on Frank t-norms family. Pattern Recogn Lett 42:128–136

    Article  Google Scholar 

  • Jain P, Tiwari AK, Som T (2020) A fitting model based intuitionistic fuzzy rough feature selection. Eng Appl Artif Intell 89:103421

    Article  Google Scholar 

  • Jena S, Ghosh S, Tripathy B (2002) Intuitionistic fuzzy rough sets. Notes on Intuitionistic Fuzzy Sets 8(1):1–18

    MathSciNet  MATH  Google Scholar 

  • Jensen R, Shen Q (2004a) Fuzzy–rough attribute reduction with application to web categorization. Fuzzy Sets Syst 141(3):469–485

    Article  MathSciNet  MATH  Google Scholar 

  • Jensen R, Shen Q (2004b) Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches. IEEE Trans Knowl Data Eng 16(12):1457–1471

    Article  Google Scholar 

  • Jensen R, Shen Q (2005) Fuzzy-rough data reduction with ant colony optimization. Fuzzy Sets Syst 149(1):5–20

    Article  MathSciNet  MATH  Google Scholar 

  • Jensen R, Shen Q (2007) Fuzzy-rough sets assisted attribute selection. IEEE Trans Fuzzy Syst 15(1):73–89

    Article  Google Scholar 

  • Jensen R, Shen Q (2008) Computational intelligence and feature selection: rough and fuzzy approaches. Wiley, Hoboken

    Book  Google Scholar 

  • Jensen R, Shen Q (2009) New approaches to fuzzy-rough feature selection. IEEE Trans Fuzzy Syst 17(4):824–838

    Article  Google Scholar 

  • Kalmegh S (2015) Analysis of weka data mining algorithm reptree, simple cart and randomtree for classification of indian news. Int J Innov Sci Eng Technol 2(2):438–446

    Google Scholar 

  • Kawashima S (2000) AAindex: amino acid index database. Nucleic Acids Res 28(1):374

    Article  Google Scholar 

  • Kawashima S, Pokarowski P, Pokarowska M, Kolinski A, Katayama T, Kanehisa M (2007) AAindex: amino acid index database, progress report 2008. Nucleic Acids Res 36:D202–D205

    Article  Google Scholar 

  • Kittler J, Hatef M, Duin RPW, Matas J (1998) On combining classifiers. IEEE Trans Pattern Anal Mach Intel 20(3):226–239

    Article  Google Scholar 

  • Kubat M, Holte R, Matwin S (1997) Learning when negative examples abound. Springer, Berlin, pp 146–153

    Google Scholar 

  • Kumar P, Vadakkepat P, Poh LA (2011) Fuzzy-rough discriminative feature selection and classification algorithm, with application to microarray and image datasets. Appl Soft Comput 11(4):3429–3440

    Article  Google Scholar 

  • Kuncheva LI (2004) Combining pattern classifiers. Wiley, Hoboken

    Book  MATH  Google Scholar 

  • Li L-Q, Wang X-L, Liu Z-X, Xie W-X (2019) A novel intuitionistic fuzzy clustering algorithm based on feature selection for multiple object tracking. Int J Fuzzy Syst 21:1613–1628

    Article  Google Scholar 

  • Lin Z, Pan X-M (2001) Accurate prediction of protein secondary structural content. J Protein Chem 20(3):217–220

    Article  Google Scholar 

  • Ling CX, Huang J, Zhang H (2003) AUC: a better measure than accuracy in comparing learning algorithms. Lecture notes in computer science. Springer, Berlin, pp 329–341

    Google Scholar 

  • Lu Y-L, Lei Y-J, Hua JX (2009) Attribute reduction based on intuitionistic fuzzy rough set. Control Decis 3:003

    MathSciNet  Google Scholar 

  • Manavalan B, Govindaraj RG, Shin TH, Kim MO, Lee G (2018a) iBCE-EL: a new ensemble learning framework for improved linear B-cell epitope prediction. Front Immunol 9:1695

    Article  Google Scholar 

  • Manavalan B, Basith S, Shin TH, Wei L, Lee G (2018b) mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation. Bioinformatics 35(16):2757–2765

    Article  Google Scholar 

  • Manavalan B, Basith S, Shin TH, Wei L, Lee G (2019) AtbPpred: a robust sequence-based prediction of anti-tubercular peptides using extremely randomized trees. Comput Struct Biotechnol J 17:972–981

    Article  Google Scholar 

  • Montes I, Janis V, Montes S (2011) An axiomatic definition of divergence for intuitionistic fuzzy sets. In: Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-11), pp 547–553. https://doi.org/10.2991/eusflat.2011.38

  • Montes I, Pal NR, Janiš V, Montes S (2015) Divergence measures for intuitionistic fuzzy sets. IEEE Trans Fuzzy Syst 23(2):444–456

    Article  Google Scholar 

  • Nanda S, Majumdar S (1992) Fuzzy rough sets. Fuzzy Sets Syst 45(2):157–160

    Article  MathSciNet  MATH  Google Scholar 

  • Neumann U, Genze N, Heider D (2017) EFS: an ensemble feature selection tool implemented as R-package and web-application. BioData mining 10(1):1–9

    Article  Google Scholar 

  • Platt J (1998) Sequential minimal optimization: a fast algorithm for training support vector machines

  • Rizvi S, Naqvi HJ, Nadeem D (2002) Rough intuitionistic fuzzy sets. Springer, Berlin, pp 101–104

    Google Scholar 

  • Rodriguez JJ, Kuncheva LI, Alonso CJ (2006) Rotation forest: a new classifier ensemble method. IEEE Trans Pattern Anal Mach Intel 28(10):1619–1630

    Article  Google Scholar 

  • Ross Quinlan J (1993) C4. 5: programs for machine learning. Mach Learn 16(3):235–240

    Google Scholar 

  • Saha I, Maulik U, Bandyopadhyay S, Plewczynski D (2011) Fuzzy clustering of physicochemical and biochemical properties of amino acids. Amino Acids 43(2):583–594

    Article  Google Scholar 

  • Samanta S, Mondal T (2001) Intuitionistic fuzzy rough sets and rough intuitionistic fuzzy sets. J Fuzzy Math 9(3):561–582

    MathSciNet  MATH  Google Scholar 

  • Saravanan V, Gautham N (2015) Harnessing computational biology for exact linear B-cell epitope prediction: a novel amino acid composition-based feature descriptor. OMICS: A J Integr Biol 19(10):648–658

    Article  Google Scholar 

  • Sheeja T, Kuriakose AS (2018) A novel feature selection method using fuzzy rough sets. Comput Ind 97:111–121

    Article  Google Scholar 

  • Shen J, Zhang J, Luo X, Zhu W, Yu K, Chen K, Li Y, Jiang H (2007) Predicting protein-protein interactions based only on sequences information. Proc Natl Acad Sci 104(11):4337–4341

    Article  Google Scholar 

  • Shreevastava S, Tiwari AK, Som T (2018a) Intuitionistic fuzzy neighborhood rough set model for feature selection. Int J Fuzzy Syst Appl 7(2):75–84

    Google Scholar 

  • Shreevastava S, Tiwari A, Som T (2018b) Feature subset selection of semi-supervised data: an intuitionistic fuzzy-rough set-based concept. Springer, Berlin, pp 303–315

    Google Scholar 

  • Singh S, Shreevastava S, Som T, Jain P (2019) Intuitionistic fuzzy quantifier and its application in feature selection. Int J Fuzzy Syst 21(2):441–453

    Article  MathSciNet  Google Scholar 

  • Sokal RR, Thomson BA (2005) Population structure inferred by local spatial autocorrelation: an example from an Amerindian tribal population. Am J Phys Anthropol 129(1):121–131

    Article  Google Scholar 

  • Spänig S, Heider D (2019) Encodings and models for antimicrobial peptide classification for multi-resistant pathogens. BioData Mining 12(1):7

    Article  Google Scholar 

  • Squeglia F, Ruggiero A, Berisio R (2018) Chemistry of peptidoglycan in Mycobacterium tuberculosis life cycle: an off-the-wall balance of synthesis and degradation. Chem—A Eur J 24(11):2533–2546

    Article  Google Scholar 

  • Suyun Z, Tsang E, Degang C (2009) The model of fuzzy variable precision rough sets. IEEE Trans Fuzzy Syst 17(2):451–467

    Article  Google Scholar 

  • Tan A, Wu W-Z, Qian Y, Liang J, Chen J, Li J (2018) Intuitionistic fuzzy rough set-based granular structures and attribute subset selection. IEEE Trans Fuzzy Syst 27(3):527–539

    Article  Google Scholar 

  • Teng T, Liu J, Wei H (2015) Anti-Mycobacterial Peptides: from Human to Phage. Cell Physiol Biochem 35(2):452–466

    Article  Google Scholar 

  • Thakur N, Qureshi A, Kumar M (2012) AVPpred: collection and prediction of highly effective antiviral peptides. Nucleic Acids Res 40(W1):W199–W204

    Article  Google Scholar 

  • Tiwari AK, Shreevastava S, Shukla KK, Subbiah K (2018a) New approaches to intuitionistic fuzzy-rough attribute reduction. J Intel Fuzzy Syst 34(5):3385–3394

    Article  Google Scholar 

  • Tiwari AK, Shreevastava S, Som T, Shukla KK (2018b) Tolerance-based intuitionistic fuzzy-rough set approach for attribute reduction. Expert Syst Appl 101:205–212

    Article  Google Scholar 

  • Tomii K, Kanehisa M (1996) Analysis of amino acid indices and mutation matrices for sequence comparison and structure prediction of proteins. Protein Eng Des Select 9(1):27–36

    Article  Google Scholar 

  • Tsang EC, Degang C, Yeung DS, Xi-Zhao W, Lee J (2008) Attributes reduction using fuzzy rough sets. IEEE Trans Fuzzy Syst 16(5):1130–1141

    Article  Google Scholar 

  • Usmani SS, Bhalla S, Raghava GP (2018a) Prediction of antitubercular peptides from sequence information using ensemble classifier and hybrid features. Front Pharmacol 9:954

    Article  Google Scholar 

  • Usmani SS, Kumar R, Kumar V, Singh S, Raghava GPS (2018) AntiTbPdb: a knowledgebase of anti-tubercular peptides. Database

  • Velayati AA, Farnia P, Hoffner S (2018) Drug-resistant Mycobacterium tuberculosis: epidemiology and role of morphological alterations. J Glob Antimicrob Resist 12:192–196

    Article  Google Scholar 

  • W. H. Organization (2016) Global tuberculosis control: WHO report 2016. Report No, WHO/HTM/TB/2016.13. Geneva, World Health Organization

  • W. H. Organisation (2017) Global tuberculosis report 2017, WHO Geneva, Switzerland

  • Wang C, Shao M, He Q, Qian Y, Qi Y (2016) Feature subset selection based on fuzzy neighborhood rough sets. Knowl-Based Syst 111:173–179

    Article  Google Scholar 

  • Wang J, Li J, Yang B, Xie R, Marquez-Lago TT, Leier A, Hayashida M, Akutsu T, Zhang Y, Chou K-C, Selkrig J, Zhou T, Song J, Lithgow T (2018a) Bastion3: a two-layer ensemble predictor of type III secreted effectors. Bioinformatics 35(12):2017–2028

    Article  Google Scholar 

  • Wang J, Yang B, Leier A, Marquez-Lago TT, Hayashida M, Rocker A, Zhang Y, Akutsu T, Chou K-C, Strugnell RA, Song J, Lithgow T (2018b) Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors. Bioinformatics 34(15):2546–2555

    Article  Google Scholar 

  • Wang C, Huang Y, Shao M, Fan X (2019a) Fuzzy rough set-based attribute reduction using distance measures. Knowl-Based Syst 164:205–212

    Article  Google Scholar 

  • Wang C, Shi Y, Fan X, Shao M (2019b) Attribute reduction based on k-nearest neighborhood rough sets. Int J Approx Reason 106:18–31

    Article  MathSciNet  MATH  Google Scholar 

  • Yager RR (1979) On the measure of fuzziness and negation part I: membership in the unit interval. Taylor & Francis, London

    MATH  Google Scholar 

  • Yager RR (1980) On a general class of fuzzy connectives. Fuzzy Sets Syst 4(3):235–242

    Article  MathSciNet  MATH  Google Scholar 

  • Yi H-C, You Z-H, Zhou X, Cheng L, Li X, Jiang T-H, Chen Z-H (2019) ACP-DL: a deep learning long short-term memory model to predict anticancer peptides using high-efficiency feature representation. Mol Ther – Nucleic Acids 17:1–9

    Article  Google Scholar 

  • Zhang Z (2016) Attributes reduction based on intuitionistic fuzzy rough sets. J Intel Fuzzy Syst 30(2):1127–1137

    Article  MATH  Google Scholar 

  • Zhang X, Zhou B, Li P (2012) A general frame for intuitionistic fuzzy rough sets. Inf Sci 216:34–49

    Article  MathSciNet  MATH  Google Scholar 

  • Zhang L, Zhan J, Xu Z, Alcantud JCR (2019) Covering-based general multigranulation intuitionistic fuzzy rough sets and corresponding applications to multi-attribute group decision-making. Inf Sci 494:114–140

    Article  Google Scholar 

Download references

Acknowledgements

This research work is funded by UGC Research Fellowship, India (Grant No: 3600/(PWD)(NET-NOV2017)) awarded to first author.

Author information

Authors and Affiliations

  1. Department of Mathematical Sciences, Indian Institute of Technology, Banaras Hindu University, Varanasi, 221005, India

    Pankhuri Jain & Tanmoy Som

  2. Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, 221005, India

    Anoop Kumar Tiwari

Authors
  1. Pankhuri Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anoop Kumar Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tanmoy Som
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anoop Kumar Tiwari.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jain, P., Tiwari, A.K. & Som, T. Enhanced prediction of anti-tubercular peptides from sequence information using divergence measure-based intuitionistic fuzzy-rough feature selection. Soft Comput 25, 3065–3086 (2021). https://doi.org/10.1007/s00500-020-05363-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-05363-z

Keywords

Search

Navigation