REFERENCES:
1. Agrawal, P., Kaur, G., Gupta, V.,
Agarwal, K., Pinjarkar, L., and Patil, S. (2025). AI Applications in Analyzing
Gene Expression for Cancer Diagnosis: A Comprehensive Review. Genomics at the
Nexus of AI, Computer Vision, and Machine Learning, 285-307.
2. Ahmed,
Z. (2022). Precision medicine with multi-omics strategies, deep phenotyping,
and predictive analysis. Progress in molecular biology and translational
science, 190(1), 101-125.
3. Al-Janabi,
A. (2022). Has DeepMind’s AlphaFold Solved the Protein Folding Problem?
BioTechniques, 72(3), 73–76. https://doi.org/10.2144/btn-2022-0007
4. Al-Shahrour,
F., Díaz-Uriarte, R., and Dopazo, J. (2005). Discovering molecular functions
significantly related to phenotypes by combining gene expression data and
biological information. Bioinformatics, 21(13), 2988-2993.
5. Álvarez-Machancoses,
Ó., DeAndres Galiana, E. J., Cernea, A., Fernández de la Viña, J., and
Fernández-Martínez, J. L. (2020). On the role of artificial intelligence in
genomics to enhance precision medicine. Pharmacogenomics and personalized
medicine, 105-119.
6. Antonov,
A. V., Schmidt, E. E., Dietmann, S., Krestyaninova, M., and Hermjakob, H.
(2010). R spider: a network-based analysis of gene lists by combining signaling
and metabolic pathways from Reactome and KEGG databases. Nucleic acids
research, 38(suppl_2), W78-W83.
7. Antonov,
A. V., Schmidt, E. E., Dietmann, S., Krestyaninova, M., and Hermjakob, H.
(2010). R spider: a network-based analysis of gene lists by combining signaling
and metabolic pathways from Reactome and KEGG databases. Nucleic Acids Res. 38,
W78–W83.
8. Apweiler,
R., Bairoch, A., Wu, C. H., Barker, W. C., Boeckmann, B., Ferro, S., ... and
Yeh, L. S. L. (2004). UniProt: the universal protein knowledgebase. Nucleic
acids research, 32(suppl_1), D115-D119.
9. Azevedo,
R., Jacquemin, C., Villain, N., Fenaille, F., Lamari, F., and Becher, F.
(2022). Mass spectrometry for neurobiomarker discovery: The relevance of
post-translational modifications. Cells, 11(8), 1279.
https://doi.org/10.3390/cells11081279
10. Bai,
M., Deng, J., Dai, C., Pfeuffer, J., Sachsenberg, T., and Perez-Riverol, Y.
(2023). LFQ-based peptide and protein intensity differential expression
analysis. Journal of Proteome Research, 22(6), 1289-1301.
https://doi.org/10.1021/acs.jproteome.3c00158
11. Brazma,
A., Hingamp, P., Quackenbush, J., Sherlock, G., Spellman, P., Stoeckert, C.,
... and Vingron, M. (2001). Minimum information about a microarray experiment
(MIAME)—toward standards for microarray data. Nature genetics, 29(4), 365-371.
12. Brazma,
A., Hingamp, P., Quackenbush, J., Sherlock, G., Spellman, P., Stoeckert, C.,
Aach, J., Ansorge, W., Ball, C. A., Causton, H. C., Gaasterland, T., Glenisson,
P., Holstege, F. C., Kim, I. F., Markowitz, V., Matese, J. C., Parkinson, H.,
Robinson, A., Sarkans, U., Schulze-Kremer, S., Stewart, J., Taylor, R., Vilo,
J., and Vingron, M. (2001). Minimum information about a microarray experiment
(MIAME)-toward standards for microarray data. Nat. Genet. 29, 365–371.
13. Brenner,
S., Noble, D., Sejnowski, T., Fields, R. D., Laughlin, S., Berridge, M., ... and
Dolmetsch, R. E. (2001). Understanding complex systems: top-down, bottom-up or
middle-out. In Novartis Foundation Symposium: Complexity in biological
information processing (Vol. 239, pp. 150-159). Chichester, UK: Wiley.
14. Cavener,
D. R., and Ray, S. C. (1991). Eukaryotic start and stop translation sites.
Nucleic acids research, 19(12), 3185-3192.
15. Cavener,
D. R., and Ray, S. C. (1991). Eukaryotic start and stop translation sites.
Nucleic Acids Res. 19, 3185–3192.
16. Chen,
C., Wang, J., Pan, D., Wang, X., Xu, Y., Yan, J., Wang, L., Yang, X., Yang, M.,
and Liu, G. P. (2023). Applications of multi-omics analysis in human diseases.
MedComm, 4(4), e315. https://doi.org/10.1002/mco2.315
17. Chen,
L., Li, Q., Nasif, K. F. A., Xie, Y., Deng, B., Niu, S., Pouriyeh, S., Dai, Z.,
Chen, J., and Xie, C. Y. (2024). AI-driven deep learning techniques in protein
structure prediction. International Journal of Molecular Sciences, 25(15),
8426. https://doi.org/10.3390/ijms25158426
18. Cheng,
Y., Xu, S.-M., Santucci, K., Lindner, G., and Janitz, M. (2024). Machine
learning and related approaches in transcriptomics. Biochemical and Biophysical
Research Communications, 724, 150225.
https://doi.org/10.1016/j.bbrc.2024.150225
19. Dai,
X., and Shen, L. (2022). Advances and trends in omics technology development.
Frontiers in Medicine, 9, 911861.
20. Dhudum,
R., Ganeshpurkar, A., and Pawar, A. (2024). Revolutionizing drug discovery: A
comprehensive review of AI applications. Drugs and Drug Candidates, 3(1),
148-171. https://doi.org/10.3390/ddc3010009
21. Dunphy,
K., Dowling, P., Bazou, D., and O’Gorman, P. (2021). Current methods of
post-translational modification analysis and their applications in blood
cancers. Cancers, 13(8), 1930. https://doi.org/10.3390/cancers13081930
22. Enoma,
D. O., Bishung, J., Abiodun, T., Ogunlana, O., and Osamor, V. C. (2022).
Machine learning approaches to genome-wide association studies. Journal of King
Saud University-Science, 34(4), 101847.
23. Graveley
B. R. (2001). Alternative splicing: increasing diversity in the proteomic
world. Trends in genetics: TIG, 17(2), 100–107.
https://doi.org/10.1016/s0168-9525(00)02176-4
24. Guillermo,
S., Elizabeth, G., and Victor, S. (2020). DeepMSPeptide: Peptide detectability
prediction using deep learning. Bioinformatics, 36(4), 1279-1280.
https://doi.org/10.1093/bioinformatics/btz708
25. Guo,
K., Wu, M., Soo, Z., Yang, Y., Zhang, Y., Zhang, Q., ... and Lu, J. (2023).
Artificial intelligence-driven biomedical genomics. Knowledge-Based Systems,
110937.
26. Hartwell,
L. H., Hopfield, J. J., Leibler, S., and Murray, A. W. (1999). From molecular
to modular cell biology. Nature, 402(6761 Suppl), C47–C52.
https://doi.org/10.1038/35011540
27. Hubbard,
T., Barker, D., Birney, E., Cameron, G., Chen, Y., Clark, L., ... and Clamp, M.
(2002). The Ensembl genome database project. Nucleic acids research, 30(1),
38-41.
28. Hubbard,
T., Barker, D., Birney, E., Cameron, G., Chen, Y., Clark, L., Cox, T., Cuff,
J., Curwen, V., Down, T., Durbin, R., Eyras, E., Gilbert, J., Hammond, M.,
Huminiecki, L., Kasprzyk, A., Lehvaslaiho, H., Lijnzaad, P., Melsopp, C.,
Mongin, E., Pettett, R., Pocock, M., Potter, S., Rust, A., Schmidt, E., Searle,
S., Slater, G., Smith, J., Spooner, W., Stabenau, A., Stalker, J., Stupka, E.,
Ureta-Vidal, A., Vastrik, I., and Clamp, M. (2002). The Ensembl genome database
project. Nucleic Acids Res. 30, 38–41.
29. Ideker,
T., Galitski, T., and Hood, L. (2001). A new approach to decoding life: systems
biology. Annual review of genomics and human genetics, 2(1), 343-372.
30. Ivanova,
A. A., Rees, J. C., Parks, B. A., Andrews, M., Gardner, M., Grigorutsa, E.,
Kuklenyik, Z., Pirkle, J. L., and Barr, J. R. (2022). Integrated quantitative
targeted lipidomics and proteomics reveal unique fingerprints of multiple
metabolic conditions. Biomolecules, 12(10), 1439.
https://doi.org/10.3390/biom12101439
31. Johannsen,
C., Koehler, C. J., and Thiede, B. (2021). Comparison of LFQ and IPTL for
protein identification and relative quantification. Molecules, 26(17), 5330.
https://doi.org/10.3390/molecules26175330
32. Joshi-Tope,
G., Gillespie, M., Vastrik, I., D’eustachio, P., Schmidt, E., De Bono, B.,
Jassal, B., Gopinath, G. R., Wu, G. R., Matthews, L., Lewis, S., Birney, E.,
and Stein, L. (2005). Reactome: a knowledgebase of biological pathways. Nucleic
Acids Res. 33, D428–D432.
33. Kanehisa,
M., and Goto, S. (2000). KEGG: Kyoto encyclopedia of genes and genomes. Nucleic
Acids Res. 28, 27–30.
34. Kaur
P, Singh A, Chana I. Computational techniques and tools for omics data
analysis: state-of-the-art, challenges, and future directions[J]. Archives of
Computational Methods in Engineering, 2021, 28: 4595-4631.
35. Kelley,
L. A., Mezulis, S., Yates, C. M., Wass, M. N., and Sternberg, M. J. (2015). The
Phyre2 web portal for protein modeling, prediction and analysis. Nature
protocols, 10(6), 845–858. https://doi.org/10.1038/nprot.2015.053
36. Lee,
M. (2023). Recent advances in deep learning for protein-protein interaction
analysis: A comprehensive review. Molecules, 28(13), 5169.
https://doi.org/10.3390/molecules28135169
37. Mann,
M., and Jensen, O. N. (2003). Proteomic analysis of post-translational
modifications. Nature biotechnology, 21(3), 255-261.
38. Mann,
M., and Jensen, O. N. (2003). Proteomic analysis of post-translational
modifications. Nat. Biotechnol. 21, 255–261.
39. Manzoni,
C., Kia, D. A., Vandrovcova, J., Hardy, J., Wood, N. W., Lewis, P. A., and
Ferrari, R. (2018). Genome, transcriptome and proteome: the rise of omics data
and their integration in biomedical sciences. Briefings in bioinformatics,
19(2), 286–302. https://doi.org/10.1093/bib/bbw114
40. Miettinen,
T., Nieminen, A. I., Mäntyselkä, P., Kalso, E., and Lötsch, J. (2022). Machine
learning and pathway analysis-based discovery of metabolomic markers relating
to chronic pain phenotypes. International Journal of Molecular Sciences, 23(9),
5085.
41. Nguyen,
T. M., Kim, N., Kim, D. H., Le, H. L., Piran, M. J., Um, S. -J., and Kim, J. H.
(2021). Deep learning for human disease detection, subtype classification, and
treatment response prediction using epigenomic data. Biomedicines, 9(11), 1733.
https://doi.org/10.3390/biomedicines9111733
42. Orasch,
O., Weber, N., Müller, M., Amanzadi, A., Gasbarri, C., and Trummer, C. (2022).
Protein–protein interaction prediction for targeted protein degradation.
International Journal of Molecular Sciences, 23(13), 7033.
https://doi.org/10.3390/ijms23137033
43. Orchard,
S., and Hermjakob, H. (2008). The HUPO proteomics standards initiative—easing
communication and minimizing data loss in a changing world. Briefings in
bioinformatics, 9(2), 166-173.
44. Orchard,
S., Hermjakob, H., and Apweiler, R. (2003). The proteomics standards
initiative. Proteomics 3, 1374–1376.
45. Pakhrin,
S. C., Shrestha, B., Adhikari, B., and KC, D. B. (2021). Deep learning-based
advances in protein structure prediction. International Journal of Molecular
Sciences, 22(11), 5553. https://doi.org/10.3390/ijms22115553
46. Parmigiani,
G., Garrett, E. S., Anbazhagan, R., and Gabrielson, E. (2002). A statistical
framework for expression-based molecular classification in cancer. Journal of
the Royal Statistical Society Series B: Statistical Methodology, 64(4),
717-736.
47. Parmigiani,
G., Garrett, E. S., Anbazhaghan, R., and Gabrielson, E. (2002). A statistical
framework for expression-based molecular classification in cancer. J. R. Stat.
Soc. B Stat. Methodol. 64, 717–736.
48. Pedrioli,
P. G., Eng, J. K., Hubley, R., Vogelzang, M., Deutsch, E. W., Raught, B., ... and
Aebersold, R. (2004). A common open representation of mass spectrometry data
and its application to proteomics research. Nature biotechnology, 22(11),
1459-1466.
49. Pedrioli,
P. G., Eng, J. K., Hubley, R., Vogelzang, M., Deutsch, E. W., Raught, B.,
Pratt, B., Nilsson, E., Angeletti, R. H., Apweiler, R., Cheung, K., Costello,
C. E., Hermjakob, H., Huang, S., Julian, R. K., Kapp, E., Mccomb, M. E.,
Oliver, S. G., Omenn, G., Paton, N. W., Simpson, R., Smith, R., Taylor, C. F.,
Zhu, W., and Aebersold, R. (2004). A common open representation of mass
spectrometry data and its application to proteomics research. Nat. Biotechnol.
22, 1459–1466.
50. Quan,
C., Luo, Z., and Wang, S. (2020). A hybrid deep learning model for
protein–protein interactions extraction from biomedical literature. Applied
Sciences, 10(8), 2690. https://doi.org/10.3390/app10082690
51. Rukhsar,
L., Bangyal, W. H., Ali Khan, M. S., Ag Ibrahim, A. A., Nisar, K., and Rawat,
D. B. (2022). Analyzing RNA-seq gene expression data using deep learning
approaches for cancer classification. Applied Sciences, 12(4), 1850.
https://doi.org/10.3390/app12041850
52. Sanches,
P. H. G., de Melo, N. C., Porcari, A. M., and de Carvalho, L. M. (2024).
Integrating molecular perspectives: Strategies for comprehensive multi-omics
integrative data analysis and machine learning applications in transcriptomics,
proteomics, and metabolomics. Biology, 13(11), 848. https://doi.org/10.3390/biology13110848
53. Sekaran,
K., and Zayed, H. (2024). Identification of novel hypertension biomarkers using
explainable AI and metabolomics. Metabolomics, 20(6), 124.
54. Serrano,
D. R., Luciano, F. C., Anaya, B. J., Ongoren, B., Kara, A., Molina, G.,
Ramirez, B. I., Sánchez-Guirales, S. A., Simon, J. A., Tomietto, G., et al.
(2024). Artificial intelligence (AI) applications in drug discovery and drug
delivery: Revolutionizing personalized medicine. Pharmaceutics, 16(10), 1328.
https://doi.org/10.3390/pharmaceutics16101328
55. Sethi,
M. K., and Fanayan, S. (2015). Mass spectrometry-based N-glycomics of
colorectal cancer. International Journal of Molecular Sciences, 16(12),
29278-29304. https://doi.org/10.3390/ijms161226165
56. Sharpf,
R. B., Tjelmeland, H., Parmigiani, G., and Nobel, A. B. (2009). A Bayesian
model for cross-study differential gene expression. J. Am. Stat. Assoc. 104,
1295–1310.
57. Soleymani,
F., Paquet, E., Viktor, H., Michalowski, W., and Spinello, D. (2022).
Protein-protein interaction prediction with deep learning: A comprehensive
review. Computational and structural biotechnology journal, 20, 5316–5341.
https://doi.org/10.1016/j.csbj.2022.08.070
58. Spellman,
P. T., Miller, M., Stewart, J., Troup, C., Sarkans, U., Chervitz, S., ... and
Brazma, A. (2002). Design and implementation of microarray gene expression
markup language (MAGE-ML). Genome biology, 3, 1-9.
59. Spellman,
P. T., Miller, M., Stewart, J., Troup, C., Sarkans, U., Chervitz, S., Bernhart,
D., Sherlock, G., Ball, C., Lepage, M., Swiatek, M., Marks, W. L., Goncalves,
J., Markel, S., Iordan, D., Shojatalab, M., Pizarro, A., White, J., Hubley, R.,
Deutsch, E., Senger, M., Aronow, B. J., Robinson, A., Bassett, D., Stoeckert,
C. J. Jr., and Brazma, A. (2002). Design and implementation of microarray gene
expression markup language (MAGE-ML). Genome Biol. 3, RESEARCH0046.
60. Theodorakis,
N., Feretzakis, G., Tzelves, L., Paxinou, E., Hitas, C., Vamvakou, G.,
Verykios, V. S., and Nikolaou, M. (2024). Integrating machine learning with
multi-omics technologies in geroscience: Towards personalized medicine. Journal
of Personalized Medicine, 14(9), 931. https://doi.org/10.3390/jpm14090931
61. Vadapalli,
S., Abdelhalim, H., Zeeshan, S., and Ahmed, Z. (2022). Artificial intelligence
and machine learning approaches using gene expression and variant data for
personalized medicine. Briefings in bioinformatics, 23(5), bbac191.
62. Visan,
A. I., and Negut, I. (2024). Integrating Artificial Intelligence for Drug
Discovery in the Context of Revolutionizing Drug Delivery. Life (Basel,
Switzerland), 14(2), 233. https://doi.org/10.3390/life14020233
63. Xiao,
J. F., Zhou, B., and Ressom, H. W. (2012). Metabolite identification and
quantitation in LC-MS/MS-based metabolomics. TrAC Trends in Analytical
Chemistry, 32, 1-14.
64. Yin,
J., Lei, J., Yu, J., Cui, W., Satz, A. L., Zhou, Y., Feng, H., Deng, J., Su,
W., and Kuai, L. (2022). Assessment of AI-based protein structure prediction
for the NLRP3 target. Molecules, 27(18), 5797.
https://doi.org/10.3390/molecules27185797
65. Zainab,
N., Seong Beom, A., Mark S., B., Shoba, R., and Abidali, M. (2021). Mass
spectrometry–based protein identification in proteomics—a review. Briefings in
Bioinformatics, 22(2), 1620–1638. https://doi.org/10.1093/bib/bbz163