Biomedical articles

(2024)

  1. https://doi.org/10.1007/s00500-023-09577-9
  2. https://doi.org/10.1186/s12864-024-10077-9
  3. https://doi.org/10.1080/15476286.2024.2315384
  4. https://doi.org/10.1016/j.compbiomed.2024.108166
  5. https://doi.org/10.3934/mbe.2024169
  6. https://doi.org/10.1093/bioinformatics/btae004
  7. https://doi.org/10.18483/ijSci.2743
  8. https://doi.org/10.2174/0115748936287244240117065325
  9. https://doi.org/10.3390/biophysica4010005
  10. https://doi.org/10.2174/0115748936287244240117065325
  11. https://doi.org/10.1007/s10910-023-01568-3

(2023)

  1. https://doi.org/10.3389/fgene.2023.1334132
  2. https://doi.org/10.1002/agm2.12274
  3. https://doi.org/10.36371/port.2023.4.8
  4. https://doi.org/10.1016/j.compbiolchem.2023.107974
  5. https://doi.org/10.1101/2023.09.01.555875
  6. https://doi.org/10.1038/s41598-023-45461-0
  7. https://doi.org/10.1186/s12864-023-09796-2
  8. https://doi.org/10.1186/s13040-023-00348-8
  9. https://doi.org/10.1016/j.biosystems.2023.105094
  10. https://doi:10.3389/fmicb.2023.1277099
  11. https://doi:10.1088/1742-6596/2432/1/012022
  12. https://doi:10.3934/mbe.2024012
  13. https://doi:10.3390/molecules28248097
  14. https://10.3389/fgene.2023.1334132
  15. https://10.3389/fimmu.2023.1267755
  16. https://doi.org/10.1016/j.dsp.2023.104137
  17. https://doi.org/10.1021/acs.jcim.3c00366
  18. https://doi.org/10.1007/s12539-023-00572-0
  19. https://doi.org/10.3390/ijms24097878
  20. https://doi.org/10.1016/j.omtn.2023.04.015
  21. https://doi.org/10.1186/s12859-023-05232-0
  22. https://doi.org/10.1186/s11658-023-00427-y
  23. https://doi.org/10.1016/j.omtn.2023.01.014
  24. https://doi.org/10.1016/j.bej.2023.108990
  25. https://doi.org/10.1016/j.compbiomed.2023.107030
  26. https://doi.org/10.3390/biomimetics8020218
  27. https://doi.org/10.1093/nar/gkad404
  28. https://doi.org/10.1016/j.bspc.2022.104362
  29. https://doi.org/10.1016/j.omtn.2023.02.026
  30. https://doi.org/10.3390/app13053326
  31. https://doi.org/10.3390/ijms23158314
  32. https://doi.org/10.3390/biomedicines11020237
  33. https://doi.org/10.1101/2023.08.03.551768
  34. https://doi.org/10.1007/s12064-023-00402-3
  35. https://doi.org/10.1371/journal.pone.0290845
  36. https://doi.org/10.20944/preprints202308.0123.v1
  37. https://doi.org/10.1016/j.ijbiomac.2023.123180
  38. https://doi.org/10.1016/j.bspc.2022.104362

(2022)

  1. https://doi.org/10.3390/molecules27217280
  2. https://doi.org/10.1016/j.csbj.2022.11.026
  3. https://doi.org/10.1101/2022.12.07.519510
  4. https://doi.org/10.1093/bib/bbac546
  5. https://doi.org/10.1093/bib/bbac514
  6. https://doi.org/10.1080/13102818.2022.2122871
  7. https://doi.org/10.1109/ATSIP55956.2022.9805882
  8. https://doi.org/10.1007/978-3-031-11713-8_24
  9. https://doi.org/10.1016/j.bspc.2022.104362
  10. https://doi.org/10.3389/fmicb.2022.1061122
  11. https://doi.org/10.1093/bib/bbac467
  12. https://doi.org/10.3389/fmicb.2022.1042127
  13. https://doi.org/10.3390/app122010216
  14. https://doi.org/10.21203/rs.3.rs-1997163/v1
  15. https://doi.org/10.22369/issn.2153-4136/9/2/4
  16. https://doi.org/10.1093/bioinformatics/btac671
  17. https://doi.org/10.31083/j.fbl2709269
  18. https://doi.org/10.17341/gazimmfd.1022624
  19. https://doi.org/10.3389/fmolb.2022.898627
  20. https://doi.org/10.3390/ijms231911026
  21. https://doi.org/10.32604/biocell.2022.016655
  22. https://doi.org/10.3934/mbe.2022644
  23. https://10.1109/ACCESS.2020.3020592
  24. https://doi.org/10.3390/app122010216
  25. https://doi.org/10.3390/molecules27217280
  26. https://doi.org/10.21203/rs.3.rs-1223538/v1
  27. https://doi.org/10.1007/978-981-15-7253-1_1
  28. https://doi.org/10.1016/j.matpr.2022.02.394
  29. https://doi.org/10.1016/j.ymeth.2022.03.001
  30. https://doi.org/10.1007/s00521-022-07121-8
  31. https://doi.org/10.3390/ijms23063044
  32. https://doi.org/10.1093/bib/bbac082
  33. https://doi.org/10.1093/bib/bbab434
  34. https://doi.org/10.1016/j.ygeno.2022.110384
  35. https://doi.org/10.1186/s43088-022-00216-0
  36. https://doi.org/10.1093/nar/gkac351
  37. https://doi.org/10.3389/fphar.2022.831791
  38. https://doi.org/10.3390/molecules27092626
  39. https://doi.org/10.1371/journal.ppat.1010345
  40. https://doi.org/10.3390/genes13040677
  41. https://doi.org/10.1016/j.ymeth.2022.04.003
  42. https://doi.org/10.37394/23208.2022.19.11
  43. https://doi.org/10.1093/bfgp/elab045
  44. https://doi.org/10.4018/978-1-7998-5351-0.ch043
  45. https://doi.org/10.3390/app12031344
  46. https://doi.org/10.1371/journal.pcbi.1009798
  47. https://doi.org/10.1007/s12539-021-00500-0
  48. https://doi.org/10.33774/chemrxiv-2021-39cd6-v2
  49. https://doi.org/10.1016/j.jbi.2022.104016
  50. https://doi.org/10.46300/91011.2022.16.16
  51. https://doi.org/10.1093/bioinformatics/btab611
  52. https://doi.org/10.1177/17483026211031163
  53. https://doi.org/10.1016/j.bspc.2021.103317
  54. https://doi.org/10.1002/open.202100248
  55. https://doi.org/10.1101/2022.02.08.479502
  56. https://doi.org/10.1038/s41598-022-14127-8
  57. https://doi.org/10.1016/j.bspc.2022.103909
  58. https://doi.org/10.21203/rs.3.rs-1743456/v1
  59. https://doi.org/10.3390/ijms23158314
  60. https://doi.org/10.1016/j.chemolab.2022.104622
  61. https://www.researchgate.net/publication/362070879
  62. https://doi.org/10.1016/j.ins.2022.05.060
  63. https://doi.org/10.1186/s12859-022-04821-9
  64. https://doi.org/10.1016/j.compbiolchem.2022.107732
  65. https://doi.org/10.3390/e24070978
  66. https://doi.org/10.3390/molecules27092626
  67. https://doi.org/10.3390/ijms23158221
  68. https://doi.org/10.1371/journal.ppat.1010345
  69. https://doi.org/10.1128/mbio.00076-22
  70. https://doi.org/10.31083/j.fbl2705152
  71. https://doi.org/10.1002/open.202100248
  72. https://doi.org/10.37394/23208.2022.19.11
  73. https://doi.org/10.3390/genes13040677

(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
  49. https://doi.org/10.1128/mSphere.00216-21
  50. https://doi.org/10.3390/ijms22147361

(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
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  25. https://doi.org/10.1016/j.ygeno.2020.09.054
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(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.
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