Biomedical articles

(2022)

  1. https://doi.org/10.21203/rs.3.rs-1223538/v1
  2. https://doi.org/10.1007/978-981-15-7253-1_1
  3. https://doi.org/10.1016/j.matpr.2022.02.394
  4. https://doi.org/10.1016/j.ymeth.2022.03.001
  5. https://doi.org/10.1007/s00521-022-07121-8
  6. https://doi.org/10.3390/ijms23063044
  7. https://doi.org/10.1093/bib/bbac082
  8. https://doi.org/10.1093/bib/bbab434
  9. https://doi.org/10.1016/j.ygeno.2022.110384
  10. https://doi.org/10.1186/s43088-022-00216-0
  11. https://doi.org/10.1093/nar/gkac351
  12. https://doi.org/10.3389/fphar.2022.831791
  13. https://doi.org/10.3390/molecules27092626
  14. https://doi.org/10.1371/journal.ppat.1010345
  15. https://doi.org/10.3390/genes13040677
  16. https://doi.org/10.1016/j.ymeth.2022.04.003
  17. https://doi.org/10.37394/23208.2022.19.11
  18. https://doi.org/10.1093/bfgp/elab045
  19. https://doi.org/10.4018/978-1-7998-5351-0.ch043
  20. https://doi.org/10.3390/app12031344
  21. https://doi.org/10.1371/journal.pcbi.1009798
  22. https://doi.org/10.1007/s12539-021-00500-0
  23. https://doi.org/10.33774/chemrxiv-2021-39cd6-v2
  24. https://doi.org/10.1016/j.jbi.2022.104016
  25. https://doi.org/10.46300/91011.2022.16.16
  26. https://doi.org/10.1093/bioinformatics/btab611
  27. https://doi.org/10.1177/17483026211031163
  28. https://doi.org/10.1016/j.bspc.2021.103317
  29. https://doi.org/10.1002/open.202100248
  30. https://doi.org/10.1101/2022.02.08.479502

(2021)

  1. https://doi.org/10.3390/vaccines9070751
  2. https://doi.org/10.3390/ncrna7040077
  3. https://doi.org/10.3390/a14100283
  4. https://doi.org/10.1093/bib/bbaa401
  5. https://doi.org/10.1177/17483026211031163
  6. https://doi.org/10.46300/91011.2021.15.7
  7. https://doi.org/10.1007/s12539-020-00405-4
  8. https://doi.org/10.18483/ijSci.2433
  9. https://doi.org/10.3390/genes12020296
  10. https://doi.org/10.1093/nar/gkab122
  11. https://doi.org/10.3969/j.issn.1673-629X.2021.03.026
  12. https://doi.org/10.1016/j.ymthe.2021.04.004
  13. https://doi.org/10.1080/15476286.2021.1898160
  14. https://doi.org/10.1109/TNB.2021.3077710
  15. https://doi.org/10.18483/ijSci.2459
  16. https://doi.org/10.3389/fcell.2021.664669
  17. https://doi.org/10.7717/peerj.11456
  18. https://doi.org/10.1002/prot.26149
  19. https://doi.org/10.1155/2021/5515342
  20. https://doi.org/10.1088/1742-6596/1921/1/012042
  21. https://doi.org/10.3389/fgene.2021.670852
  22. https://doi.org/10.18483/ijSci.2482
  23. https://doi.org/10.1007/s12539-020-00405-4
  24. https://doi.org/10.3906/elk-2003-116
  25. https://doi.org/10.1371/journal.pone.0244948.g002
  26. https://doi.org/10.1016/j.ymeth.2021.01.007
  27. https://doi.org/10.1016/j.ygeno.2020.09.054
  28. https://doi.org/10.1093/bib/bbab011
  29. https://doi.org/10.1038/s41598-021-85856-5
  30. https://doi.org/10.1142/S0219720021500049
  31. https://doi.org/10.1155/2021/9969751
  32. https://doi.org/10.1007/s11103-021-01204-1
  33. https://doi.org/0.1093/bioinformatics/btaa972
  34. https://doi.org/10.1016/j.bspc.2021.103317
  35. https://doi.org/10.1016/j.imlet.2021.11.003
  36. https://doi.org/10.1093/bib/bbab434
  37. https://doi.org/10.1016/j.csbj.2021.09.025
  38. https://doi.org/10.1186/s43042-021-00192-7
  39. https://doi.org/10.1016/j.ygeno.2021.07.004
  40. https://doi.org/10.1007/978-3-030-79311-1_14
  41. https://doi.org/10.1093/bioinformatics/btab611
  42. https://doi.org/10.3390/ijms22179106
  43. https://doi.org/10.3390/app11167731
  44. https://doi.org/10.1016/j.compbiomed.2021.104650
  45. https://doi.org/10.1016/j.bspc.2021.102915
  46. https://doi.org/10.18483/ijSci.2495
  47. https://doi.org/10.3390/ijms22147361
  48. https://doi.org/10.1016/j.heliyon.2021.e07933

(2020)

  1. https://doi.org/10.1186/s12864-020-07166-w
  2. https://doi.org/10.1038/s41598-020-77824-2
  3. https://doi.org/10.1016/j.csbj.2020.11.030
  4. https://doi.org/10.1007/s12539-020-00399-z
  5. https://doi.org/10.1155/2020/8852258
  6. https://doi.org/10.1007/978-1-4939-7717-8_13
  7. https://doi.org/10.18483/ijSci.2390
  8. https://doi.org/10.1016/j.jbi.2020.103555
  9. https://doi.org/10.1016/j.omtn.2020.09.010
  10. https://doi.org/10.1016/j.csbj.2020.09.001
  11. https://doi.org/10.1093/bib/bbaa202
  12. https://doi.org/10.3389/fgene.2020.00090
  13. https://doi.org/10.1016/j.omtn.2019.11.014
  14. https://doi.org/10.1016/j.omtn.2020.06.004
  15. https://doi.org/10.1109/ACCESS.2020.2991477
  16. https://doi.org/10.1093/bib/bbaa170
  17. https://doi.org/10.1002/cta.2838
  18. https://doi.org/10.1093/bib/bbaa124
  19. https://doi.org/10.1080/15257770.2020.1780442
  20. https://doi.org/10.1093/bib/bbaa099
  21. https://doi.org/10.1101/2020.06.08.140368
  22. https://doi.org/10.1016/j.omtn.2020.06.004
  23. https://doi.org/10.1371/journal.pone.0232332
  24. https://doi.org/10.1093/bib/bbaa304
  25. https://doi.org/10.1016/j.ygeno.2020.09.054
  26. https://doi.org/10.1093/bib/bbaa284
  27. https://doi.org/10.1101/794131
  28. https://doi.org/10.1093/bib/bbaa275
  29. https://doi.org/10.3390/ijms21197271
  30. https://doi.org/10.1038/s41598-020-72174-5
  31. https://doi.org/10.1007/s13721-020-00270-7
  32. http://dx.doi.org/10.14257/ijsip.2016.9.5.21
  33. https://doi.org/10.1093/bioinformatics/btz408
  34. https://doi.org/10.1186/s13072-020-00330-2
  35. https://doi.org/10.1101/2020.07.15.176933
  36. https://doi.org/10.1007/s00500-020-04942-4
  37. https://doi.org/10.21203/rs.3.rs-34908/v1
  38. https://doi.org/10.1080/15257770.2020.1780440
  39. https://doi.org/10.3389/fbioe.2020.00134
  40. https://doi.org/10.3390/pr8060638
  41. https://doi.org/10.18483/ijSci.2316
  42. https://doi.org/10.3389/fpls.2020.00004
  43. https://doi.org/10.1142/S0219720015500249
  44. https://doi.org/10.1016/j.ijbiomac.2019.12.009
  45. https://doi.org/10.1007/s13721-020-00230-1
  46. https://doi.org/10.1093/bib/bbaa401
  47. https://doi.org/10.1101/2020.12.19.423610
  48. https://doi.org/10.21203/rs.3.rs-114338/v1
  49. https://doi.org/10.1093/bib/bbz041

(1976-2019)

  1. An Integrated-OFFT Model for the Prediction of Protein Secondary Structure Class.
    Panda B, Majhi B, Thakur A.
    Curr Comput Aided Drug Des. 2019;15(1):45-54. doi: 10.2174/1573409914666180828105228.
  2. An integrated approach for identification of exon locations using recursive Gauss Newton tunedadaptive Kaiser window.
    Das L, Nanda S, Das JK
    Genomics. 2019 May;111(3):284-296. doi: 10.1016/j.ygeno.2018.10.008.
  3. Hybrid model for efficient prediction of poly(A) signals in human genomic DNA.
    Albalawi F, Chahid A, Guo X, Albaradei S, Magana-Mora A, Jankovic BR, Uludag M, Van Neste C, Essack M, Laleg-Kirati TM, Bajic VB.
    Methods. 2019 Aug 15;166:31-39. doi: 10.1016/j.ymeth.2019.04.001
  4. BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches.
    Liu B, Gao X, Zhang H.
    Nucleic Acids Res. 2019;47(20):e127. doi: 10.1093/nar/gkz740.
  5. Identification of DNA N6-methyladenine sites by integration of sequence features
    Hao-Tian Wang, Fu-Hui Xiao, Gong-Hua Li, Qing-Peng Kong
    Epigenetics Chromatin. 2020; 13: 8. doi: 10.1186/s13072-020-00330-2
  6. 4mCpred-EL: An Ensemble Learning Framework for Identification of DNA N4-methylcytosine Sites in the Mouse Genome.
    Manavalan B, Basith S, Shin TH, Lee DY, Wei L, Lee G
    Cells 2019;8(11):1332. doi: 10.3390/cells8111332.
  7. AggreRATE-Pred: A mathematical model for the prediction of change in aggregation rate upon point mutation.
    Rawat P, Prabakaran R, Kumar S, Gromiha MM
    Bioinformatics. 2019 Dec 1;35(23):4930-4937. doi: 10.1093/bioinformatics/btz408.
  8. Iterative feature representations improve N4-methylcytosine site prediction.
    Wei L, Su R, Luan S, Liao Z, Manavalan B, Zou Q, Shi X
    Nucleosides Nucleotides Nucleic Acids. 2019;38(5):321-337. doi: 10.1080/15257770.2018.1536270.
  9. Visual representation of DNA sequences for exon detection using non-parametric spectralestimation techniques.
    Dessouky AM, Taha TE, Dessouky MM, Eltholth AA, Hassan E, Abd El-Samie FE
    Sci Rep. 2019 Feb 15;9(1):2159. doi: 10.1038/s41598-018-38197-9.
  10. A signal processing method for alignment-free metagenomic binning: multi-resolution genomicbinary patterns.
    Kouchaki S, Tapinos A, Robertson DL
    Viruses. 2019 Apr 26;11(5). pii: E394. doi: 10.3390/v11050394
  11. The Utility of Data Transformation for Alignment, De Novo Assembly and Classification of Short Read Virus Sequences.
    Tapinos A, Constantinides B, Phan MVT, Kouchaki S, Cotten M, Robertson DL.
    Int J Mol Sci. 2019 Nov 11;20(22). pii: E5640. doi: 10.3390/ijms20225640.
  12. Novel Descriptors and Digital Signal Processing-Based Method for Protein Sequence Activity Relationship Study.
    Fontaine NT, Cadet XF, Vetrivel I.
    Mol Ther Nucleic Acids. 2019 Nov 21;19:293-303. doi: 10.1016/j.omtn.2019.11.014.
  13. Is There Any Sequence Feature in the RNa Pseudouridine Modification Prediction Problem?
    Dou L, Li X, Ding H, Xu L, Xiang H
    Int J Biol Macromol. 2019 Dec 2. pii: S0141-8130(19)38547-2. doi: 10.1016/j.ijbiomac.2019.12.009.
  14. i4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genome.
    Hasan MM, Manavalan B, Khatun MS, Kurata H
    Mol Ther Nucleic Acids. 2019 Jun 7;16:733-744. doi: 10.1016/j.omtn.2019.04.019.
  15. Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation.
    Manavalan B, Basith S, Shin TH, Wei L, Lee G
    Curr Comput Aided Drug Des. 2019;15(1):45-54. doi:10.2174/1573409914666180828105228.
  16. An Integrated-OFFT Model for the Prediction of Protein Secondary Structure Class.
    Panda B, Majhi B, Thakur A
    Comput Struct Biotechnol J. 2019 Mar 19;17:406-414. doi: 10.1016/j.csbj.2019.03.007.
  17. A degeneration-reducing criterion for optimal digital mapping of genetic codes.
    Skutkova H, Maderankova D, Sedlar K, Jugas R, Vitek M.
    Bioinform Biol Insights. 2019 Jun 4;13:1177932219850172. doi: 10.1177/1177932219850172.
  18. Time-Frequency Approach Appllied to finding Interaction Region in Pathogenic Proteins.
    Arenas AF, Arango-Plaza N, Arenas JC, Salcedo GE
    Bioinformatics. 2019 Apr 1;35(7):1125-1132. doi: 10.1093/bioinformatics/bty752.
  19. DeepGSR: an optimized deep-learning structure for the recognition of genomic signals and regions.
    Kalkatawi M, Magana-Mora A, Jankovic B, Bajic VB
    Brief Bioinform. 2019 Apr 24. pii: bbz041. doi: 10.1093/bioinformatics/bty752.
  20. iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data.
    Chen Z, Zhao P, Li F, Marquez-Lago TT, Leier A, Revote J, Zhu Y, Powell DR, Akutsu T, Webb GI, Chou KC, Smith AI, Daly RJ, Li J, Song J
    BMC Genomics. 2019 Apr 3;20(1):267. doi: 10.1186/s12864-019-5571-y.
  21. ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels.
    Randhawa GS, Hill KA, Kari L
    BMC Genomics; 20(1):267. doi: 10.1186/s12864-019-5571-y.
  22. Computational design and characterization of nanobody-derived peptides that stabilize the active conformation of the β2-adrenergic receptor (β2-AR).
    Sencanski M, Glisic S, Šnajder M, Veljkovic N, Poklar Ulrih N, Mavri J, Vrecl M.
    Sci Rep. 2019 Nov 12;9(1):16555. doi: 10.1038/s41598-019-52934-8
  23. Evolution of H5-Type Avian Influenza A Virus Towards Mammalian Tropism in Egypt, 2014 to 2015.
    Mahmoud SH, Mostafa A, El-Shesheny R, Seddik MZ, Khalafalla G, Shehata M, Kandeil A, Pleschka S, Kayali G, Webby R, Veljkovic V, Ali MA.
    Pathogens. 2019 Nov 7;8(4). pii: E224. doi: 10.3390/pathogens8040224.
  24. Functional characterization of β2-adrenergic and insulin receptor heteromers.
    Susec M, Sencanski M, Glisic S, Veljkovic N, Pedersen C, Drinovec L, Stojan J, Nøhr J, Vrecl M.
    Neuropharmacology. 2019 Jul 1;152:78-89. doi: 10.1016/j.neuropharm.2019.01.025.
  25. Antibody Epitope Specificity for dsDNA Phosphate Backbone Is an Intrinsic Property of the Heavy Chain Variable Germline Gene Segment Used.
    Srdic-Rajic T, Kohler H, Jurisic V, Metlas R.
    Front Immunol. 2018 Oct 18;9:2378. doi: 10.3389/fimmu.2018.02378.
  26. A machine learning approach for reliable prediction of amino acid interactions and its applicationin the directed evolution of enantioselective enzymes.
    Cadet F, Fontaine N, Li G, Sanchis J, Ng Fuk Chong M, Pandjaitan R, Vetrivel I, Offmann B, Reetz MT
    Sci Rep. 2018 Nov 13;8(1):16757. doi: 10.1038/s41598-018-35033-y.
  27. A survey of recently emerged genome-wide computational enhancer predictor tools.
    Lim LWK, Chung HH, Chong YL, Lee NK
    Comput Biol Chem. 2018 Jun;74:132-141. doi: 10.1016/j.compbiolchem.2018.03.019.
  28. Application of fourier transform and proteochemometrics principles to protein engineering.
    Cadet F, Fontaine N, Vetrivel I, Ng Fuk Chong M, Savriama O, Cadet X, Charton P
    BMC Bioinformatics. 2018 Oct 16;19(1):382. doi: 10.1186/s12859-018-2407-8.
  29. 70ProPred:predictor for discovering sigma70 promoters based on combining multiple features.
    He W, Jia C, Duan Y, Zou Q
    BMC Syst Biol. 2018 Apr 24;12(Suppl 4):44. doi: 10.1186/s12918-018-0570-1.
  30. Sc-ncDNAPred: A Sequence-Based Predictor for Identifying Non-coding DNA in Saccharomyces cerevisiae.
    He W, Ju Y, Zeng X, Liu X, Zou Q
    Front Microbiol. 2018 Sep 12;9:2174. doi: 10.3389/fmicb.2018.02174.
  31. LncFinder: an integrated platform for long non-coding RNA identification coding RNA identification utilizing sequence intrinsic composition, structural information and physicochemical property.
    Han S, Liang Y Ma Q, Xu Y, Zhang Y, Du W, Wang C, Li Y
    Brief Bioinform. 2018 Jul 31. doi: 10.1093/bib/bby065.
  32. NucPosPred: Predicting species-specific genomic nucleosome positioning via four different modes of general PseKNC.
    Jia C, Yang Q, Zou Q.
    J Theor Biol. 2018 Aug 7;450:15-21. doi: 10.1016/j.jtbi.2018.04.025
  33. Zika virus infection elicits auto-antibodies to C1q.
    Koma T, Veljkovic V, Anderson DE, Wang LF, Rossi SL, Shan C, Shi PY, Beasley DW, Bukreyeva N, Smith JN, Hallam S, Huang C, von Messling V, Paessler S.
    Sci Rep. 2018 Jan 30;8(1):1882. doi: 10.1038/s41598-018-20185-8.
  34. NucPosPred: Predicting species-specific genomic nucleosome positioning via four different modes of general PseKNC.
    Jia C, Yang Q, Zou Q
    Bioinformatics. 2018 Nov 15;34(22):3835-3842. doi: 10.1093/bioinformatics/bty458.
  35. iEnhancer-EL: identifying enhancers and their strength with ensemble learning approach.
    Liu B, Li K, Huang DS, Chou KC
    J Theor Biol. 2018 Aug 7;450:15-21. doi: 10.1016/j.jtbi.2018.04.025
  36. Prediction of Influenza A virus infections in humans using an Artificial Neural Network learning approach.
    Chrysostomou C, Partaourides H, Seker H.
    Conf Proc IEEE Eng Med Biol Soc. 2017 Jul;2017:1186-1189. doi: 10.1109/EMBC.2017.8037042.
  37. Influence of Tuning Element Relief Patches on Pain as Analyzed by the Resonant Recognition Model.
    Cosic I, Cosic D.
    IEEE Trans Nanobioscience. 2017 Dec;16(8):822-827. doi: 10.1109/TNB.2017.2775645
  38. On DNA numerical representations for genomic similarity computation.
    Mendizabal-Ruiz G, Román-Godínez I, Torres-Ramos S, Salido-Ruiz RA, Morales JA
    PLoS One. 2017 Mar 21;12(3):e0173288. doi: 10.1371/journal.pone.0173288.
  39. The neglected functions of intrinsically disordered proteins and the origin of life.
    Jaeken L
    BMC Bioinformatics. 2017 Aug 29;18(1):379. doi: 10.1186/s12859-017-1792-8.
    Prog Biophys Mol Biol. 2017 Jul;126:31-46. doi: 10.1016/j.pbiomolbio.2017.03.002.
  40. EL_PSSM-RT: DNA-binding residue prediction by integrating ensemble learning with PSSM Relation Transformation.
    Zhou J, Lu Q, Xu R, He Y, Wang H
    BMC Bioinformatics. 2017 Aug 29;18(1):379. doi: 10.1186/s12859-017-1792-8.
  41. A deep learning method for lincRNA detection using auto-encoder algorithm.
    Yu N, Yu Z, Pan Y
    BMC Bioinformatics 2017 Dec 6;18(Suppl 15):511. doi: 10.1186/s12859-017-1922-3.
  42. Biophotonic markers of malignancy: Discriminating cancers using wavelength-specific biophotons.
    Murugan NJ, Rouleau N, Karbowski LM, Persinger MA.
    Biochem Biophys Rep. 2017 Nov 20;13:7-11. doi: 10.1016/j.bbrep.2017.11.001.
  43. EnhancerPred2.0: predicting enhancers and their strength based on position-specific trinucleotide propensity and electron-ion interaction potential feature selection.
    He W, Jia C.
    Mol Biosyst. 2017 Mar 28;13(4):767-774. doi: 10.1039/c7mb00054e.
  44. Environmental Light and Its Relationship with Electromagnetic Resonances of Biomolecular Interactions, as Predicted by the Resonant Recognition Model.
    Cosic I, Cosic D, Lazar K.
    Int J Environ Res Public Health. 2016 Jun 29;13(7). pii: E647. doi: 10.3390/ijerph13070647.
  45. Prediction of intrinsically disordered regions in proteins using signal processing methods:application to heat-shock proteins.
    Vojisavljevic V, Pirogova E
    Med Biol Eng Comput. 2016 Dec;54(12):1831-1844.
  46. Analysis of Tumor Necrosis Factor Function Using the Resonant Recognition Model.
    Cosic I, Cosic D, Lazar K.
    Cell Biochem Biophys. 2016 Jun;74(2):175-80. doi: 10.1007/s12013-015-0716-3.
  47. A Complex Prime Numerical Representation of Amino Acids for Protein Function Comparison.
    Chen D, Wang J, Yan M, Bao FS.
    J Comput Biol. 2016 Aug;23(8):669-77. doi: 10.1089/cmb.2015.0178
  48. Prediction of potential barcoding sites on ITS1 by wavelet transform.
    Maggi N, Ruggiero C, Arrigo P
    J Biomol Struct Dyn. 2016;34(4):814-23. doi: 10.1080/07391102.2015.1056550.
  49. Predicting hot spots in protein interfaces based on protrusion index, pseudo hydrophobicity and electron-ion interaction pseudopotential features.
    Xia J, Yue Z, Di Y, Zhu X, Zheng CH
    Oncotarget. 2016 Apr 5;7(14):18065-75. doi: 10.18632/oncotarget.7695.
  50. Possible repurposing of seasonal influenza vaccine for prevention of Zika virus infection.
    Veljkovic V, Paessler S.
    F1000Res. 2016 ;5:190. doi: 10.12688/f1000research.8102.2.
  51. Common molecular mechanism of the hepatic lesion and the cardiac parasympathetic regulation in chronic hepatitis C infection: a critical role for the muscarinic receptor type 3.
    Glišić S, Cavanaugh DP, Chittur KK, Sencanski M, Perovic V, Bojić T.
    BMC Bioinformatics. 2016 Mar 22;17:139. doi: 10.1186/s12859-016-0988-7.
  52. Pomegranate (Punica granatum): a natural source for the development of therapeutic compositions of food supplements with anticancer activities based on electron acceptor molecular characteristics
    Nicolson G, Glisic S, Perovic V, Veljkovic N, Veljkovic V.
    Funct Foods Health Dis 2016;6:769. doi: 10.31989/ffhd.v6i12.289
  53. In silico Therapeutics for Neurogenic Hypertension and Vasovagal Syncope.
    Bojić T, Perović VR, Glišić S.
    Front Neurosci. 2016 Jan 21;9:520. doi: 10.3389/fnins.2015.00520.
  54. Improved algorithm for analysis of DNA sequences using multiresolution transformation.
    Inbamalar TM, Sivakumar R.
    ScientificWorldJournal. 2015;2015:786497. doi: 10.1155/2015/786497.
  55. CoGI: Towards Compressing Genomes as an Image.
    Xie X, Zhou S, Guan J.
    IEEE/ACM Trans Comput Biol Bioinform. 2015;12(6):1275-85. doi: 10.1109/TCBB.2015.2430331.
  56. PRBP: Prediction of RNA-Binding Proteins Using a Random Forest Algorithm Combined with an RNA-Binding Residue Predictor.
    Ma X, Guo J, Xiao K, Sun X.
    IEEE/ACM Trans Comput Biol Bioinform. 2015;12(6):1385-93. doi: 10.1109/TCBB.2015.2418773.
  57. In Silico Prediction and Experimental Confirmation of HA Residues Conferring Enhanced Human Receptor Specificity of H5N1 Influenza A Viruses.
    Schmier S, Mostafa A, Haarmann T, Bannert N, Ziebuhr J, Veljkovic V, Dietrich U, Pleschka S.
    Sci Rep. 2015 Jun 19;5:11434. doi: 10.1038/srep11434.
  58. Algorithm, applications and evaluation for protein comparison by Ramanujan Fourier transform.
    Zhao J, Wang J, Hua W, Ouyang P.
    Mol Cell Probes. 2015 Dec;29(6):396-407. doi: 10.1016/j.mcp.2015.08.003
  59. Prediction of Tubulin Resonant Frequencies Using the Resonant Recognition Model (RRM).
    Cosic I, Lazar K, Cosic D.
    IEEE Trans Nanobioscience. 2015 Jun;14(4):491-496. doi: 10.1109/TNB.2014.2365851
  60. In silico analysis suggests interaction between Ebola virus and the extracellular matrix.
    Veljkovic V, Glisic S, Muller CP, Scotch M, Branch DR, Perovic VR, Sencanski M, Veljkovic N, Colombatti A.
    Front Microbiol. 2015 Feb 19;6:135. doi: 10.3389/fmicb.2015.00135.
  61. Novel Cosic resonance (standing wave) solutions for components of the JAK-STAT cellular signaling pathway: A convergence of spectral density profiles.
    Karbowski LM, Murugan NJ, Persinger MA.
    FEBS Open Bio. 2015 Mar 25;5:245-50. doi: 10.1016/j.fob.2015.03.004
  62. A new signal characterization and signal-based Chou’s PseAAC representation of protein sequences.
    Sanchez V, Peinado AM, Pérez-Córdoba JL, Gómez AM.
    J Bioinform Comput Biol. 2015 Oct;13(5):1550024. (doi: 10.1142/S0219720015500249)
  63. Demonstration of a direct interaction between β2-adrenergic receptor and insulin receptor by BRET and bioinformatics.
    Mandić M, Drinovec L, Glisic S, Veljkovic N, Nøhr J, Vrecl M.
    PLoS One. 2014 Nov 17;9(11):e112664. doi: 10.1371/journal.pone.011266
  64. Molecular phylogeny analysis using correlation distance and spectral distance.
    Sabarish RA, Thomas T.
    Int J Data Min Bioinform. 2014;10(4):391-406. DOI:10.1504/ijdmb.2014.064890
  65. Shifting wavelengths of ultraweak photon emissions from dying melanoma cells: their chemical enhancement and blocking are predicted by Cosic’s theory of resonant recognition model for macromolecules.
    Dotta BT, Murugan NJ, Karbowski LM, Lafrenie RM, Persinger MA.
    Naturwissenschaften. 2014 Feb;101(2):87-94. doi: 10.1007/s00114-013-1133-3.
  66. SFAPS: an R package for structure/function analysis of protein sequences based on informational spectrum method.
    Deng SP, Huang DS.
    Methods. 2014 Oct 1;69(3):207-12. doi: 10.1016/j.ymeth.2014.08.004
  67. A simple three-step method for design and affinity testing of new antisense peptides: an example of erythropoietin.
    Štambuk N, Manojlović Z, Turčić P, Martinić R, Konjevoda P, Weitner T, Wardega P, Gabričević M
    Int J Mol Sci. 2014 May 26;15(6):9209-23. doi: 10.3390/ijms15069209.
  68. Influenza vaccine as prevention for cardiovascular diseases: possible molecular mechanism.
    Veljkovic V, Glisic S, Veljkovic N, Bojic T, Dietrich U, Perovic VR, Colombatti A.
    Vaccine. 2014 Nov 12;32(48):6569-75. doi: 10.1016/j.vaccine.2014.07.0
  69. Fuzzy rules for describing subgroups from Influenza A virus using a multi-objective evolutionary algorithm.
    Carmona CJ, Chrysostomou C, Seker H, del Jesusa NJ.
    Applied Soft Computing 2013;13:3439.
  70. Feature-based classification of amino acid substitutions outside conserved functional protein domains.
    Gemovic B, Perovic V, Glisic S, Veljkovic N.
    ScientificWorldJournal. 2013 Nov 17;2013:948617. doi: 10.1155/2013/948617.
  71. A digital signal processing-based bioinformatics approach to identifying the origins of HIV-1 non B subtypes infecting US Army personnel serving abroad.
    Nwankwo N.
    Curr HIV Res. 2013 Jun;11(4):271-80. doi:10.2174/1570162×113119990046
  72. Phosphocholine-binding antibody activities are hierarchically encoded in the sequence of the heavy-chain variable region: dominance of self-association activity in the T15 idiotype.
    Srdiċ-Rajiċ T, Kekoviċ G, Davidoviċ DM, Metlas R.
    Int Immunol. 2013 Jun;25(6):345-52. doi: 10.1093/intimm/dxs156.
  73. A fast algorithm for exonic regions prediction in DNA sequences.
    Saberkari H, Shamsi M, Heravi H, Sedaaghi MH.
    J Med Signals Sens. 2013;3:139.
  74. Signal-processing-based bioinformatics approach for the identification of influenza A virus subtypes in neuraminidase genes.
    Chrysostomou C, Seker H.
    Conf Proc IEEE Eng Med Biol Soc. 2013;2013:3066-9. doi: 10.1109/EMBC.2013.6610188.
  75. Novel phylogenetic algorithm to monitor human tropism in Egyptian H5N1-HPAIV reveals evolution toward efficient human-to-human transmission.
    Perovic VR, Muller CP, Niman HL, Veljkovic N, Dietrich U, Tosic DD, Glisic S, Veljkovic V.
    PLoS One. 2013;8:e61572. doi: 10.1371/journal.pone.0061572
  76. Wavelet analysis in current cancer genome research: a survey.
    Meng T, Soliman AT, Shyu ML, Yang Y, Chen SC, Iyengar SS, Yordy JS, Iyengar P
    IEEE/ACM Trans Comput Biol Bioinform. 2013 Nov-Dec;10(6):1442-59. doi: 10.1109/TCBB.2013.134.
  77. HIV progression to AIDS: bioinformatics approach to determining the mechanism of action.
    Nwankwo N, Seker H.
    Curr HIV Res. 2013 Jan;11(1):30-42.
  78. Phosphocholine-binding antibody activities are hierarchically encoded in the sequence of the heavy-chain variable region: dominance of self-association activity in the T15 idiotype.
    Srdiċ-Rajiċ T, Kekoviċ G, Davidoviċ DM, Metlas R.
    Int Immunol. 2013 Jun;25(6):345-52. doi: 10.1093/intimm/dxs156.
  79. Imidazoline-1 receptor ligands as apoptotic agents: pharmacophore modeling and virtual docking study.
    Nikolic K, Veljkovic N, Gemovic B, Srdic-Rajic T, Agbaba D.
    Comb Chem High Throughput Screen. 2013 May;16(4):298-319.
  80. Conserved synthetic peptides from the hemagglutinin of influenza viruses induce broad humoral and T-cell responses in a pig model.
    Vergara-Alert J, Argilaguet JM, Busquets N, Ballester M, Martín-Valls GE, Rivas R, López-Soria S, Solanes D, Majó N, Segalés J, Veljkovic V, Rodríguez F, Darji A.
    PLoS One. 2012;7(7):e40524. doi: 10.1371/journal.pone.0040524.
  81. Investigation of cytotoxicity of negative control peptides versus bioactive peptides on skin cancer and normal cells: a comparative study.
    Almansour NM, Pirogova E, Coloe PJ, Cosic I, Istivan TS.
    Future Med Chem. 2012 Aug;4(12):1553-65. doi: 10.4155/fmc.12.98.
  82. Assessment of hepatitis C virus protein sequences with regard to interferon/ribavirin combination therapy response in patients with HCV genotype 1b.
    Glisic S, Veljkovic N, Jovanovic Cupic S, Vasiljevic N, Prljic J, Gemovic B, Perovic V, Veljkovic V.
    Protein J. 2012 Feb;31(2):129-36. doi: 10.1007/s10930-011-9381-6.
  83. A bioactive peptide analogue for myxoma virus protein with a targeted cytotoxicity for human skin cancer in vitro.
    Almansour NM, Pirogova E, Coloe PJ, Cosic I, Istivan TS.
    J Biomed Sci. 2012 Jul 17;19:65. doi: 10.1186/1423-0127-19-65.
  84. A Nonlinear Pattern Recognition of Pandemic H1N1 Using a State Space Based Methods.
    Mabrouk MS.
    Avicenna J Med Biotechnol. 2011 Jan;3(1):25-9.
  85. IEEE Trans Biomed Eng. 2013 Nov;60(11):2993-3002. doi: 10.1109/TBME.2011.2161306.
    Protein interaction hotspot identification using sequence-based frequency-derived features.
    Nguyen QT, Fablet R, Pastor D.
  86. Effects of windowing and zero-padding on Complex Resonant Recognition Model for protein sequence analysis.
    Chrysostomou C, Seker H, Aydin N.
    Conf Proc IEEE Eng Med Biol Soc. 2011;2011:4955-8. doi: 10.1109/IEMBS.2011.6091228.
  87. Biological effects of a de novo designed myxoma virus peptide analogue: evaluation of cytotoxicity on tumor cells.
    Istivan TS, Pirogova E, Gan E, Almansour NM, Coloe PJ, Cosic I.
    PLoS One. 2011;6(9):e24809. doi: 10.1371/journal.pone.0024809.
  88. Advances in methods for therapeutic peptide discovery, design and development.
    Pirogova E, Istivan T, Gan E, Cosic I.
    Curr Pharm Biotechnol. 2011 Aug;12(8):1117-27 DOI:10.2174/138920111796117436
  89. A signal processing-based bioinformatics approach to assessing drug resistance: human immunodeficiency virus as a case study.
    Nwankwo N, Seker H.
    Conf Proc IEEE Eng Med Biol Soc. 2010;2010:1836-9. doi: 10.1109/IEMBS.2010.5626439.
  90. Computational studies of the interaction between the HIV-1 integrase tetramer and the cofactor LEDGF/p75: insights from molecular dynamics simulations and the informational spectrum method.
    Tintori C, Veljkovic N, Veljkovic V, Botta M.
    Proteins. 2010 Dec;78(16):3396-408. doi: 10.1002/prot.22847.
  91. Discrete wavelet transform de-noising in eukaryotic gene splicing.
    George TP, Thomas T.
    BMC Bioinformatics. 2010 Jan 18;11 Suppl 1:S50. doi: 10.1186/1471-2105-11-S1-S50.
  92. Review of studies on modulating enzyme activity by low intensity electromagnetic radiation.
    Vojisavljevic V, Pirogova E, Cosic I.
    Conf Proc IEEE Eng Med Biol Soc. 2010;2010:835-8. doi: 10.1109/IEMBS.2010.5626786
  93. Ataxin active site determination using spectral distribution of electron ion interaction potentials of amino acids.
    Pirogova E, Vojisavljevic V, Cáceres JL, Cosic I.
    Med Biol Eng Comput. 2010 Apr;48(4):303-9. doi: 10.1007/s11517-010-0587-0
  94. A new approach to revealing functional residues from analysis of protein primary structure.
    Vojisavljevic V, Pirogova E, Davidovic D, Cosic I.
    Conf Proc IEEE Eng Med Biol Soc. 2009;2009:4731-4. doi: 10.1109/IEMBS.2009.5334193.
  95. System identification: DNA computing approach.
    Huang CH, Jan HY, Lin CL, Lee CS.
    ISA Trans. 2009 Jul;48(3):254-63. doi: 10.1016/j.isatra.2009.01.006
  96. Investigating the interaction between oncogene and tumor suppressor protein.
    Pirogova E, Akay M, Cosic I.
    IEEE Trans Inf Technol Biomed. 2009 Jan;13(1):10-5. doi: 10.1109/TITB.2008.2003338.
  97. Amino Acids. 2009 Jul;37(2):415-25. doi: 10.1007/s00726-008-0170-2.
    Prediction of protein structural classes by Chou’s pseudo amino acid composition: approachedusing continuous wavelet transform and principal component analysis.
    Li ZC, Zhou XB, Dai Z, Zou XY.
  98. 14-3-3 ligand prevents nuclear import of c-ABL protein in chronic myeloid leukemia.
    Mancini M, Veljkovic N, Corradi V, Zuffa E, Corrado P, Pagnotta E, Martinelli G, Barbieri E, Santucci MA.
    Traffic. 2009 Jun;10(6):637-47. doi: 10.1111/j.1600-0854.2009.00897.x.
  99. Discovery of new therapeutic targets by the informational spectrum method.
    Veljkovic N, Glisic S, Prljic J, Perovic V, Botta M, Veljkovic V.
    Curr Protein Pept Sci. 2008 Oct;9(5):493-506. DOI:10.2174/138920308785915245
  100. EMILINs interact with anthrax protective antigen and inhibit toxin action in vitro.
    Doliana R, Veljkovic V, Prljic J, Veljkovic N, De Lorenzo E, Mongiat M, Ligresti G, Marastoni S, Colombatti A.
    Matrix Biol. 2008 Mar;27(2):96-106. DOI:10.1016/j.matbio.2007.09.008
  101. Lipoprotein lipase: A bioinformatics criterion for assessment of mutations as a risk factor for cardiovascular disease.
    Glisic S, Arrigo P, Alavantic D, Perovic V, Prljic J, Veljkovic N.
    Proteins. 2008 Feb 15;70(3):855-62. DOI:10.1002/prot.21581
  102. Is DNA code periodicity only due to CUF-codons usage frequency?
    Zoltowski M.
    Conf Proc IEEE Eng Med Biol Soc. 2007;2007:1383-6. DOI:10.1109/IEMBS.2007.4352556
  103. Bioactive peptide design using the Resonant Recognition Model.
    Cosic I, Pirogova E.
    Nonlinear Biomed Phys. 2007 Jul 19;1(1):7. DOI:10.1186/1753-4631-1-7
  104. The effect of electromagnetic radiation (550-850 nm) on 1-lactate dehydrogenase kinetics.
    Vojisavljevic V, Pirogova E, Cosic I.
    Int J Radiat Biol. 2007 Apr;83(4):221-30. DOI:10.1080/09553000701227565
  105. In silico criterion for prediction of effects of p53 gene missense mutations on p53-Mdm2 feedback loop.
    Veljkovic N, Perovic V.
    Protein Pept Lett. 2006;13(8):807-14. DOI:10.2174/092986606777841181
  106. Selection of amino acid parameters for Fourier transform-based analysis of proteins.
    Lazović J.
    Comput Appl Biosci. 1996 Dec;12(6):553-62. DOI:10.1093/bioinformatics/12.6.553
    A coding measure scheme employing electron-ion interaction pseudopotential (EIIP).
    Nair AS, Sreenadhan SP.
    Bioinformation. 2006 Oct 7;1(6):197-202.
  107. CMDWave: conserved motifs detection using wavelets.
    Riaz T, Li KB, Tang F, Krishnan A.
    In Silico Biol. 2005;5(4):415-8.
  108. Y-box binding protein, YB-1, as a marker of tumor aggressiveness and response to adjuvant chemotherapy in breast cancer.
    Huang J, Tan PH, Li KB, Matsumoto K, Tsujimoto M, Bay BH.
    Int J Oncol. 2005 Mar;26(3):607-13.
  109. Localization of recognition site between transforming growth factor-beta1 (TGF-beta1) and TGF beta receptor type II: possible implications in breast cancer.
    Ivanović V, Demajo M, Todorović-Raković N, Nikolić-Vukosavljević D, Nesković-Konstantinović Z, Krtolica K, Veljković V, Prljić J, Dimitrijević B.
    Med Hypotheses. 2004;62(5):727-32. DOI:10.1016/j.mehy.2003.11.027
  110. RRM analysis of protoporphyrinogen oxidase.
    Sauren M, Pirogova E, Cosic I.
    Australas Phys Eng Sci Med. 2004 Dec;27(4):174-9. DOI:10.1007/bf03178646
  111. HIV-1 gp120 and immune network.
    Metlas R, Veljkovic V.
    Int Rev Immunol. 2004 Sep-Dec;23(5-6):413-22 DOI:10.1080/08830180490432758
  112. Design of peptide mimetics of HIV-1 gp120 for prevention and therapy of HIV disease.
    Veljkovic N, Branch DR, Metlas R, Prljic J, Vlahovicek K, Pongor S, Veljkovic V.
    J Pept Res. 2003 Oct;62(4):158-66. DOI:10.1034/j.1399-3011.2003.00081.x
  113. Resonant recognition model of neuropeptide Y family: hot spot amino acid distribution in the sequences.
    Murakami M.
    J Protein Chem. 2000 Oct;19(7):609-13. DOI:10.1023/a:1007143113887
  114. The resonant recognition model (RRM) predicts amino acid residues in highly conserved regions of the hormone prolactin (PRL).
    Hejase de Trad C, Fang Q, Cosic I.
    Biophys Chem. 2000 Apr 14;84(2):149-57 DOI:10.1016/s0301-4622(00)00109-5
  115. Protein structure analysis using the resonant recognition model and wavelet transforms.
    Fang Q, Cosic I.
    Australas Phys Eng Sci Med. 1998 Dec;21(4):179-85.
  116. Preliminary expansion of the resonant recognition model to incorporate multi variable analysis.
    Birch S, West R, Cosic I.
    Australas Phys Eng Sci Med. 1995 Dec;18(4):197-207.
  117. Macromolecular bioactivity: is it resonant interaction between macromolecules?–Theory and applications.
    Cosic I.
    IEEE Trans Biomed Eng. 1994 Dec;41(12):1101-14. DOI:10.1109/10.335859
  118. In vitro inhibition of the actions of basic FGF by a novel 16 amino acid peptide.
    Cosic I, Drummond AE, Underwood JR, Hearn MT.
    Mol Cell Biochem. 1994 Jan 12;130(1):1-9. DOI:10.1007/bf01084262
  119. Application of artificial neural networks for prokaryotic transcription terminator prediction.
    Nair TM, Tambe SS, Kulkarni BD.
    FEBS Lett. 1994 Jun 13;346(2-3):273-7. DOI:10.1016/0014-5793(94)00489-7
  120. Studies on protein-DNA interactions using the resonant recognition model. Application to repressors and transforming proteins.
    Cosic I, Hearn MT.
    Eur J Biochem. 1992 Apr 15;205(2):613-9. DOI:10.1111/j.1432-1033.1992.tb16819.x
  121. Spectral and sequence similarity between vasoactive intestinal peptide and the second conserved region of human immunodeficiency virus type 1 envelope glycoprotein (gp120): possible consequences on prevention and therapy of AIDS.
    Veljkovic V, Metlas R, Raspopovic J, Pongor S.
    Biochem Biophys Res Commun. 1992 Dec 15;189(2):705-10. DOI:10.1016/0006-291x(92)92258-y
  122. Resonant recognition model and protein topography. Model studies with myoglobin, hemoglobin and lysozyme.
    Cosic I, Hodder AN, Aguilar MI, Hearn MT.
    Eur J Biochem. 1991 May 23;198(1):113-9. DOI:10.1111/j.1432-1033.1991.tb15993.x
  123. The global average DNA base composition of coding regions may be determined by the electron-ion interaction potential.
    Lalović D, Veljković V.
    Biosystems. 1990;23(4):311-6. DOI:10.1016/0303-2647(90)90013-q
  124. The relationship of the resonant recognition model to effects of low-intensity light on cell growth.
    Cosic I, Vojisavljevic V, Pavlovic M.
    Int J Radiat Biol. 1989 Aug;56(2):179-91 DOI:10.1080/09553008914551331
  125. Prediction of “hot spots” in interleukin-2 based on informational spectrum characteristics of growth-regulating factors. Comparison with experimental data.
    Cosic I, Pavlovic M, Vojisavljevic V.
    Biochimie. 1989 Mar;71(3):333-42. DOI:10.1016/0300-9084(89)90005-9
  126. Identification of nanopeptide from HTLV-III, ARV-2 and LAVBRU envelope gp120 determining binding to T4 cell surface protein.
    Veljković V, Metlas R.
    Cancer Biochem Biophys. 1988 Nov;10(2):91-106.
  127. Enhancer binding proteins predicted by informational spectrum method.
    Cosić I, Nesić D, Pavlović M, Williams R.
    Biochem Biophys Res Commun. 1986 Dec 15;141(2):831-8. DOI:0.1016/s0006-291x(86)80248-0
  128. Is it possible to analyze DNA and protein sequences by the methods of digital signal processing?
    Veljković V, Cosić I, Dimitrijević B, Lalović D.
    IEEE Trans Biomed Eng. 1985 May;32(5):337-41. DOI:10.1109/TBME.1985.325549
  129. Correlation between the carcinogenicity of organic substances and their spectral characteristics.
    Veljković V, Lalović DI.
    Experientia. 1978 Oct 15;34(10):1342-3. DOI:10.1007/bf01981460
  130. Cytostatic activity of organic compounds and their average quasi-valence number.
    Veljković V, Ajdacić V.
    Experientia. 1978 May 15;34(5):639-41. DOI:10.1007/bf01937008
  131. Antibiotic activity of organic compounds and their average quasi-valence number.
    Ajdacić V, Veljković V.
    Experientia. 1978 May 15;34(5):633-5. DOI:10.1007/bf01937005
  132. Simple theoretical criterion of chemical carcinogenicity.
    Veljković V, Lalović DI.
    Experientia. 1977 Sep 15;33(9):1228-9 DOI:10.1007/bf01922345
  133. Theoretical prediction of mutagenicity and carcinogenicity of chemical substances.
    Veljkovic VJ, Lalovic DI.
    Cancer Biochem Biophys. 1976;1(6):295-8.