Biomedical articles

(2024)

1. https://doi.org/10.2174/0115748936287244240117065325
2. https://doi.org/10.1186/s12859-024-05738-1
3. https://doi.org/10.3390/biom14040423
4. https://doi.org/10.1038/s41598-024-57457-5
5. https://doi.org/10.1016/j.ymeth.2024.05.004
6. https://doi.org/10.1016/j.compbiomed.2024.108466
7. https://doi.org/10.3390/pr12061129
8. https://doi.org/10.1371/journal.pone.0305406
9. https://doi.org/10.3390/biom14070767
10. https://doi.org/10.1016/j.jmgm.2024.108835
11. https://doi.org/10.3390/pharmaceutics16080997
12. https://doi.org/10.1016/j.jmb.2024.168717
13. https://doi.org/10.1016/j.ab.2024.115495
14. https://doi.org/10.1093/bib/bbae169
15. https://doi.org/10.1101/2023.08.03.551768
16. https://doi.org/10.1016/j.compbiomed.2023.107848
17. https://doi.org/10.1016/j.omtn.2024.102192
18. https://doi.org/10.3390/ijms25094911
19. https://doi.org/10.1101/2023.12.14.571762
20. https://doi.org/10.1016/j.ymeth.2024.06.012
21. https://link.springer.com/article/10.1007/s10930-024-10219-8
22. https://doi.org/10.1109/ICEENG58856.2024.10566462
23. https://doi.org/10.1007/s00500-023-09577-9
24. https://doi.org/10.1186/s12864-024-10077-9
25. https://doi.org/10.1080/15476286.2024.2315384
26. https://doi.org/10.1016/j.compbiomed.2024.108166
27. https://doi.org/10.3934/mbe.2024169
28. https://doi.org/10.1093/bioinformatics/btae004
29. https://doi.org/10.18483/ijSci.2743
30. https://doi.org/10.2174/0115748936287244240117065325
31. https://doi.org/10.3390/biophysica4010005
32. https://doi.org/10.2174/0115748936287244240117065325
33. 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
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
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22. https://doi.org/10.1016/j.omtn.2020.06.004
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24. https://doi.org/10.1093/bib/bbaa304
25. https://doi.org/10.1016/j.ygeno.2020.09.054
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27. https://doi.org/10.1101/794131
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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.
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   Genomics. 2019 May;111(3):284-296. doi: 10.1016/j.ygeno.2018.10.008.
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   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
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   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
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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.
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23. Evolution of H5-Type Avian Influenza A Virus Towards Mammalian Tropism in Egypt, 2014 to 2015.
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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.
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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.
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26. A machine learning approach for reliable prediction of amino acid interactions and its applicationin the directed evolution of enantioselective enzymes.
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27. A survey of recently emerged genome-wide computational enhancer predictor tools.
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28. Application of fourier transform and proteochemometrics principles to protein engineering.
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