Next-Generation Computational Approaches for Biological Network Analysis

Hamza Ali Mari; Maham Taqi; Abrar Ahmed Rattar; Ahsan Jamal Memon; Muhammad Talha Nasir; Arleen Yousuf

doi:10.59786/bmtj.313

Authors

Hamza Ali Mari Medical Research Centre, Liaquat University of Medical and Health Sciences (LUMHS), Jamshoro 76080, Pakistan https://orcid.org/0000-0001-9993-9277
Maham Taqi Department of Molecular Biology, Liaquat University of Medical and Health Sciences (LUMHS), Jamshoro, 76080, Pakistan https://orcid.org/0009-0009-2547-9066
Abrar Ahmed Rattar Department of Medicine, Liaquat University of Medical and Health Sciences (LUMHS), Jamshoro, 76080, Pakistan https://orcid.org/0000-0001-8975-3591
Ahsan Jamal Memon Department of Medicine, Liaquat University of Medical and Health Sciences (LUMHS), Jamshoro, 76080, Pakistan https://orcid.org/0000-0001-7989-311X
Muhammad Talha Nasir Medical Research Centre, Liaquat University of Medical and Health Sciences (LUMHS), Jamshoro, 76080, Pakistan https://orcid.org/0009-0001-7988-1191
Arleen Yousuf Institute of Biotechnology and Genetic Engineering, University of Sindh, Jamshoro 76080, Pakistan https://orcid.org/0009-0001-1374-8072

DOI:

https://doi.org/10.59786/bmtj.313

Keywords:

Protein-protein interaction networks, Cellular functions, Biological processes, Computational techniques, Modeling and analysis, Drug discovery

Abstract

Protein-protein interaction (PPI) networks are critical to understanding cellular processes and disease mechanisms. Computational advances have transformed PPI analysis, allowing for the prediction, analysis, and visualization of intricate interaction networks. This article discusses the basics of PPI networks, experimental and computational methods for their detection and analysis, and novel predictive models. We cover sequence-based approaches, such as homology, domain, and motif-based methods, as well as structure-based methods like structural alignment, comparison, and interface-based prediction. Functional annotation-based methods, such as Gene Ontology (GO) annotations, pathway-based methods, and co-expression data, are also discussed. Machine learning methods, such as supervised and unsupervised models, neural networks, and deep learning, increasingly contribute to improving PPI predictions. In addition, network inference methods, including Bayesian networks, graph-based approaches, and integrative multi-omics strategies, extend our understanding of biological systems. Key applications of PPI networks are the prioritization of disease genes, annotating uncharacterized proteins' functions, analyzing pathways, and discovering biomarkers. Yet, incompleteness and noisiness of data, false positives and negatives, and scalability limitations of computational methods continue to pose problems. Emerging directions highlight upcoming technologies, advances in machine learning, and multi-omics integration with the potential for steering personalized medicine and precision health.

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References

R. Freilich, T. Arhar, J. L. Abrams, and J. E. Gestwicki, “Protein–Protein Interactions in the Molecular Chaperone Network,” Acc Chem Res, vol. 51, no. 4, pp. 940–949, Apr. 2018, doi: 10.1021/acs.accounts.8b00036.

O. Keskin, A. Gursoy, B. Ma, and R. Nussinov, “Principles of Protein−Protein Interactions: What are the Preferred Ways For Proteins To Interact?,” Chem Rev, vol. 108, no. 4, pp. 1225–1244, Apr. 2008, doi: 10.1021/cr040409x.

R. Freilich, T. Arhar, J. L. Abrams, and J. E. Gestwicki, “Protein–Protein Interactions in the Molecular Chaperone Network,” Acc Chem Res, vol. 51, no. 4, pp. 940–949, Apr. 2018, doi: 10.1021/acs.accounts.8b00036.

B. Ma and R. Nussinov, “Druggable Orthosteric and Allosteric Hot Spots to Target Protein-protein Interactions,” Curr Pharm Des, vol. 20, no. 8, pp. 1293–1301, Mar. 2014, doi: 10.2174/13816128113199990073.

E. Valkov, T. Sharpe, M. Marsh, S. Greive, and M. Hyvönen, “Targeting Protein–Protein Interactions and Fragment-Based Drug Discovery,” 2011, pp. 145–179. doi: 10.1007/128_2011_265.

L. Xian and Y. Wang, “Advances in Computational Methods for Protein–Protein Interaction Prediction,” Electronics (Basel), vol. 13, no. 6, p. 1059, Mar. 2024, doi: 10.3390/electronics13061059.

J. Snider, M. Kotlyar, P. Saraon, Z. Yao, I. Jurisica, and I. Stagljar, “Fundamentals of protein interaction network mapping,” Mol Syst Biol, vol. 11, no. 12, Dec. 2015, doi: 10.15252/msb.20156351.

J. De Las Rivas and C. Fontanillo, “Protein-protein interaction networks: unraveling the wiring of molecular machines within the cell,” Brief Funct Genomics, vol. 11, no. 6, pp. 489–496, Nov. 2012, doi: 10.1093/bfgp/els036.

J. Snider, M. Kotlyar, P. Saraon, Z. Yao, I. Jurisica, and I. Stagljar, “Fundamentals of protein interaction network mapping.,” Mol Syst Biol, vol. 11, no. 12, p. 848, Dec. 2015, doi: 10.15252/msb.20156351.

M. A. Ghadie and Y. Xia, “Are transient protein-protein interactions more dispensable?,” PLoS Comput Biol, vol. 18, no. 4, Apr. 2022, doi: 10.1371/JOURNAL.PCBI.1010013.

K. K. A, S. M. S. Karim, M. Kumar, and R. R. Singh, “Prediction of transient and permanent protein interactions using AI methods,” Bioinformation, vol. 19, no. 6, p. 749, Jun. 2023, doi: 10.6026/97320630019749.

M. Maleki and L. Rueda, “Domain-domain interactions in obligate and non-obligate protein-protein interactions,” 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), pp. 907–908, 2011, doi: 10.1109/BIBMW.2011.6112498.

M. Maleki, M. Hall, and L. Rueda, “Using structural domains to predict obligate and non-obligate protein-protein interactions,” IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 9–15, 2012, doi: 10.1109/CIBCB.2012.6217204.

I. Bera and S. Ray, “A study of interface roughness of heteromeric obligate and non-obligate protein-protein complexes,” Bioinformation, vol. 4, no. 5, p. 210, Oct. 2009, doi: 10.6026/97320630004210.

M. Aziz, “Prediction of obligate and non-obligate protein-protein interactions,” 2011.

M. Maleki, M. M. Aziz, and L. Rueda, “Analysis of relevant physicochemical properties in obligate and non-obligate protein-protein interactions,” 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), pp. 345–351, 2011, doi: 10.1109/BIBMW.2011.6112397.

C. L. Meyerkord and H. Fu, “Protein-Protein Interactions Methods and Applications Second Edition Methods in Molecular Biology 1278,” Protein-Protein Interactions: Methods and Applications: Second Edition, pp. 1–613, Apr. 2015, doi: 10.1007/978-1-4939-2425-7.

T. Pawson, “Specificity in Signal Transduction: From Phosphotyrosine-SH2 Domain Interactions to Complex Cellular Systems,” Cell, vol. 116, no. 2, pp. 191–203, Jan. 2004, doi: 10.1016/S0092-8674(03)01077-8.

M. H. Glickman and A. Ciechanover, “The ubiquitin-proteasome proteolytic pathway: destruction for the sake of construction,” Physiol Rev, vol. 82, no. 2, pp. 373–428, 2002, doi: 10.1152/PHYSREV.00027.2001.

D. M. Lonard and B. W. O’Malley, “Nuclear Receptor Coregulators: Judges, Juries, and Executioners of Cellular Regulation,” Mol Cell, vol. 27, no. 5, pp. 691–700, Sep. 2007, doi: 10.1016/J.MOLCEL.2007.08.012.

F. U. Hartl, A. Bracher, and M. Hayer-Hartl, “Molecular chaperones in protein folding and proteostasis,” Nature, vol. 475, no. 7356, pp. 324–332, Jul. 2011, doi: 10.1038/NATURE10317.

M. L. Dustin and D. Depoil, “New insights into the T cell synapse from single molecule techniques,” Nat Rev Immunol, vol. 11, no. 10, pp. 672–684, Oct. 2011, doi: 10.1038/NRI3066.

T. Kirchhausen, D. Owen, and S. C. Harrison, “Molecular Structure, Function, and Dynamics of Clathrin-Mediated Membrane Traffic,” Cold Spring Harb Perspect Biol, vol. 6, no. 5, pp. a016725–a016725, May 2014, doi: 10.1101/cshperspect.a016725.

Y. Zhang and A. R. Fernie, “Metabolons, enzyme–enzyme assemblies that mediate substrate channeling, and their roles in plant metabolism,” Plant Commun, vol. 2, no. 1, p. 100081, Jan. 2021, doi: 10.1016/J.XPLC.2020.100081.

A. Monti, L. Vitagliano, A. Caporale, M. Ruvo, and N. Doti, “Targeting Protein–Protein Interfaces with Peptides: The Contribution of Chemical Combinatorial Peptide Library Approaches,” Int J Mol Sci, vol. 24, no. 9, May 2023, doi: 10.3390/IJMS24097842.

M. M. R. Arkin and J. A. Wells, “Small-molecule inhibitors of protein-protein interactions: progressing towards the dream,” Nat Rev Drug Discov, vol. 3, no. 4, pp. 301–317, 2004, doi: 10.1038/NRD1343.

S. Fields and O. K. Song, “A novel genetic system to detect protein–protein interactions,” Nature 1989 340:6230, vol. 340, no. 6230, pp. 245–246, 1989, doi: 10.1038/340245a0.

S. J. Park, B. H. Lee, and D. J. Kim, “Identification of Proteins That Interact with Podocin Using the Yeast 2-Hybrid System,” Yonsei Med J, vol. 50, no. 2, p. 273, 2009, doi: 10.3349/ymj.2009.50.2.273.

S. E. Kohalmi, L. J. V. Reader, A. Samach, J. Nowak, G. W. Haughn, and W. L. Crosby, “Identification and characterization of protein interactions using the yeast 2-hybrid system,” Plant Molecular Biology Manual, pp. 95–124, 1998, doi: 10.1007/978-94-011-5242-6_6.

P. Wang et al., “Using the yeast three-hybrid system for the identification of small molecule-protein interactions with the example of ethinylestradiol,” Biol Chem, vol. 403, no. 4, pp. 421–431, Mar. 2022, doi: 10.1515/HSZ-2021-0355.

“[PDF] A pipeline for systematic yeast 2-hybrid matricial screening in liquid culture. | Semantic Scholar.” Accessed: Jul. 23, 2024. [Online]. Available: https://www.semanticscholar.org/paper/A-pipeline-for-systematic-yeast-2-hybrid-matricial-Dario-Damien/737f5d8762ca332cf5899ddb3cf9f628ee0be9c9

A. Conrad and A. J. Piefer, “Using a yeast‐2‐hybrid system to map chemotactic protein‐protein interactions in Epulopiscium sp. type b (539.5),” The FASEB Journal, vol. 28, no. S1, Apr. 2014, doi: 10.1096/FASEBJ.28.1_SUPPLEMENT.539.5.

Z. Jia-ke, “Construction of cDNA library from mouse oocyte for yeast-2-hybrid system by homologous recombination,” 2007.

T. A. Peterson and R. C. Piper, “Deconvolution of Multiple Rab Binding Domains Using the Batch Yeast 2-Hybrid Method DEEPN,” Methods Mol Biol, vol. 2293, pp. 117–141, 2021, doi: 10.1007/978-1-0716-1346-7_9.

P. A. J. Huber, A. L. Medhurst, H. Youssoufian, and C. G. Mathew, “Investigation of fanconi anemia protein interactions by yeast two-hybrid analysis,” Biochem Biophys Res Commun, vol. 268, no. 1, pp. 73–77, Feb. 2000, doi: 10.1006/bbrc.1999.2055.

P. Liu et al., “Research advances in preparation and application of chitosan nanofluorescent probes,” Int J Biol Macromol, vol. 163, pp. 1884–1896, Nov. 2020, doi: 10.1016/j.ijbiomac.2020.09.190.

G. Bunt and F. S. Wouters, “FRET from single to multiplexed signaling events,” Biophys Rev, vol. 9, no. 2, pp. 119–129, Apr. 2017, doi: 10.1007/s12551-017-0252-z.

L. He, D. P. Olson, X. Wu, T. S. Karpova, J. G. McNally, and P. E. Lipsky, “A flow cytometric method to detect protein‐protein interaction in living cells by directly visualizing donor fluorophore quenching during CFP→YFP fluorescence resonance energy transfer (FRET),” Cytometry Part A, vol. 55A, no. 2, pp. 71–85, Oct. 2003, doi: 10.1002/cyto.a.10073.

K. Okamoto and Y. Sako, “Recent advances in FRET for the study of protein interactions and dynamics,” Curr Opin Struct Biol, vol. 46, pp. 16–23, Oct. 2017, doi: 10.1016/j.sbi.2017.03.010.

W. R. Algar, N. Hildebrandt, S. S. Vogel, and I. L. Medintz, “FRET as a biomolecular research tool — understanding its potential while avoiding pitfalls,” Nat Methods, vol. 16, no. 9, pp. 815–829, Sep. 2019, doi: 10.1038/s41592-019-0530-8.

E. S. Butz et al., “Quantifying macromolecular interactions in living cells using FRET two-hybrid assays,” Nat Protoc, vol. 11, no. 12, pp. 2470–2498, Dec. 2016, doi: 10.1038/nprot.2016.128.

J. H. Wong et al., “A yeast two-hybrid system for the screening and characterization of small-molecule inhibitors of protein–protein interactions identifies a novel putative Mdm2-binding site in p53,” BMC Biol, vol. 15, no. 1, p. 108, Dec. 2017, doi: 10.1186/s12915-017-0446-7.

J. H. Morris et al., “Affinity purification–mass spectrometry and network analysis to understand protein-protein interactions,” Nat Protoc, vol. 9, no. 11, pp. 2539–2554, Nov. 2014, doi: 10.1038/nprot.2014.164.

J.-S. Lin and E.-M. Lai, “Protein–Protein Interactions: Co-Immunoprecipitation,” 2017, pp. 211–219. doi: 10.1007/978-1-4939-7033-9_17.

S. W. Michnick, P. H. Ear, C. Landry, M. K. Malleshaiah, and V. Messier, “Protein-Fragment Complementation Assays for Large-Scale Analysis, Functional Dissection and Dynamic Studies of Protein–Protein Interactions in Living Cells,” 2011, pp. 395–425. doi: 10.1007/978-1-61779-160-4_25.

A. Dragulescu-Andrasi, C. T. Chan, A. De, T. F. Massoud, and S. S. Gambhir, “Bioluminescence resonance energy transfer (BRET) imaging of protein–protein interactions within deep tissues of living subjects,” Proceedings of the National Academy of Sciences, vol. 108, no. 29, pp. 12060–12065, Jul. 2011, doi: 10.1073/pnas.1100923108.

J. George, S. Mittal, I. P. Kadamberi, S. Pradeep, and P. Chaluvally-Raghavan, “Optimized proximity ligation assay (PLA) for detection of RNA-protein complex interactions in cell lines,” STAR Protoc, vol. 3, no. 2, p. 101340, Jun. 2022, doi: 10.1016/j.xpro.2022.101340.

A. A. Kermani, S. Aggarwal, and A. Ghanbarpour, “Advances in X-ray crystallography methods to study structural dynamics of macromolecules,” in Advanced Spectroscopic Methods to Study Biomolecular Structure and Dynamics, Elsevier, 2023, pp. 309–355. doi: 10.1016/B978-0-323-99127-8.00020-9.

D. Bhella, “Cryo-electron microscopy: an introduction to the technique, and considerations when working to establish a national facility,” Biophys Rev, vol. 11, no. 4, pp. 515–519, Aug. 2019, doi: 10.1007/s12551-019-00571-w.

D. Szklarczyk et al., “STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets,” Nucleic Acids Res, vol. 47, no. D1, pp. D607–D613, Jan. 2019, doi: 10.1093/nar/gky1131.

“STRING - Database Commons.” Accessed: Jul. 25, 2024. [Online]. Available: https://ngdc.cncb.ac.cn/databasecommons/database/id/62

“STRING - Database Commons.” Accessed: Jul. 27, 2024. [Online]. Available: https://ngdc.cncb.ac.cn/databasecommons/database/id/62

“Database of Protein, Chemical, and Genetic Interactions | BioGRID.” Accessed: Jul. 27, 2024. [Online]. Available: https://thebiogrid.org/news/315

“BioGRID | Database of Protein, Chemical, and Genetic Interactions.” Accessed: Jul. 27, 2024. [Online]. Available: https://thebiogrid.org/

“IntAct reaches 1.5 million binary interaction evidences | EMBL-EBI.” Accessed: Jul. 27, 2024. [Online]. Available: https://www.ebi.ac.uk/about/news/updates-from-data-resources/intact-247-release/

“Latest release of IntAct and Complex Portal | EMBL-EBI.” Accessed: Jul. 27, 2024. [Online]. Available: https://www.ebi.ac.uk/about/news/updates-from-data-resources/intact-244-complex-portal-244/

S. Orchard et al., “The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases,” Nucleic Acids Res, vol. 42, no. D1, pp. D358–D363, Jan. 2014, doi: 10.1093/nar/gkt1115.

N. del Toro et al., “The IntAct database: efficient access to fine-grained molecular interaction data,” Nucleic Acids Res, vol. 50, no. D1, pp. D648–D653, Jan. 2022, doi: 10.1093/nar/gkab1006.

J. Dapkūnas, A. Timinskas, K. Olechnovič, M. Margelevičius, R. Dičiūnas, and Č. Venclovas, “The PPI3D web server for searching, analyzing and modeling protein–protein interactions in the context of 3D structures,” Bioinformatics, vol. 33, no. 6, pp. 935–937, Mar. 2017, doi: 10.1093/bioinformatics/btw756.

D. Szklarczyk et al., “STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets,” Nucleic Acids Res, vol. 47, no. D1, pp. D607–D613, Jan. 2019, doi: 10.1093/nar/gky1131.

A. Chatr-aryamontri et al., “The BioGRID interaction database: 2015 update,” Nucleic Acids Res, vol. 43, no. D1, pp. D470–D478, Jan. 2015, doi: 10.1093/nar/gku1204.

S. Orchard et al., “The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases,” Nucleic Acids Res, vol. 42, no. D1, pp. D358–D363, Jan. 2014, doi: 10.1093/nar/gkt1115.

N. del Toro et al., “The IntAct database: efficient access to fine-grained molecular interaction data,” Nucleic Acids Res, vol. 50, no. D1, pp. D648–D653, Jan. 2022, doi: 10.1093/nar/gkab1006.

X.-W. Wang et al., “Assessment of community efforts to advance network-based prediction of protein–protein interactions,” Nat Commun, vol. 14, no. 1, p. 1582, Mar. 2023, doi: 10.1038/s41467-023-37079-7.

T. Hamamsy et al., “Protein remote homology detection and structural alignment using deep learning,” Nat Biotechnol, vol. 42, no. 6, pp. 975–985, Jun. 2024, doi: 10.1038/s41587-023-01917-2.

S. Barik, “Special Issue: Structure, Function and Evolution of Protein Domains,” Int J Mol Sci, vol. 23, no. 11, p. 6201, May 2022, doi: 10.3390/ijms23116201.

M. Blum et al., “The InterPro protein families and domains database: 20 years on,” Nucleic Acids Res, vol. 49, no. D1, pp. D344–D354, Jan. 2021, doi: 10.1093/nar/gkaa977.

C. Yang, E. A. Chen, and Y. Zhang, “Protein–Ligand Docking in the Machine-Learning Era,” Molecules, vol. 27, no. 14, p. 4568, Jul. 2022, doi: 10.3390/molecules27144568.

Y. Yan, H. Tao, J. He, and S.-Y. Huang, “The HDOCK server for integrated protein–protein docking,” Nat Protoc, vol. 15, no. 5, pp. 1829–1852, May 2020, doi: 10.1038/s41596-020-0312-x.

C. Zhang, M. Shine, A. M. Pyle, and Y. Zhang, “US-align: universal structure alignments of proteins, nucleic acids, and macromolecular complexes,” Nat Methods, vol. 19, no. 9, pp. 1109–1115, Sep. 2022, doi: 10.1038/s41592-022-01585-1.

T. Hamamsy et al., “Protein remote homology detection and structural alignment using deep learning,” Nat Biotechnol, vol. 42, no. 6, pp. 975–985, Jun. 2024, doi: 10.1038/s41587-023-01917-2.

S. Das and S. Chakrabarti, “Classification and prediction of protein–protein interaction interface using machine learning algorithm,” Sci Rep, vol. 11, no. 1, p. 1761, Jan. 2021, doi: 10.1038/s41598-020-80900-2.

G. Csaba, F. Birzele, and R. Zimmer, “Systematic comparison of SCOP and CATH: a new gold standard for protein structure analysis,” BMC Struct Biol, vol. 9, no. 1, p. 23, Dec. 2009, doi: 10.1186/1472-6807-9-23.

J. Zhu, Q. Zhao, E. Katsevich, and C. Sabatti, “Exploratory Gene Ontology Analysis with Interactive Visualization,” Sci Rep, vol. 9, no. 1, p. 7793, May 2019, doi: 10.1038/s41598-019-42178-x.

B. Habermann, J. Villaveces, and P. Koti, “Tools for visualization and analysis of molecular networks, pathways, and -omics data,” Advances and Applications in Bioinformatics and Chemistry, p. 11, Jun. 2015, doi: 10.2147/AABC.S63534.

L. E. Cardozo et al., “webCEMiTool: Co-expression Modular Analysis Made Easy,” Front Genet, vol. 10, Mar. 2019, doi: 10.3389/fgene.2019.00146.

V. Sheth, U. Tripathi, and A. Sharma, “A Comparative Analysis of Machine Learning Algorithms for Classification Purpose,” Procedia Comput Sci, vol. 215, pp. 422–431, 2022, doi: 10.1016/j.procs.2022.12.044.

N. Altman and M. Krzywinski, “Clustering,” Nat Methods, vol. 14, no. 6, pp. 545–546, Jun. 2017, doi: 10.1038/nmeth.4299.

L. Alzubaidi et al., “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” J Big Data, vol. 8, no. 1, p. 53, Mar. 2021, doi: 10.1186/s40537-021-00444-8.

J. L. Puga, M. Krzywinski, and N. Altman, “Bayesian networks,” Nat Methods, vol. 12, no. 9, pp. 799–800, Sep. 2015, doi: 10.1038/nmeth.3550.

Y. Ni, P. Müller, L. Wei, and Y. Ji, “Bayesian graphical models for computational network biology,” BMC Bioinformatics, vol. 19, no. S3, p. 63, Mar. 2018, doi: 10.1186/s12859-018-2063-z.

F. Yang, K. Fan, D. Song, and H. Lin, “Graph-based prediction of Protein-protein interactions with attributed signed graph embedding,” BMC Bioinformatics, vol. 21, no. 1, p. 323, Dec. 2020, doi: 10.1186/s12859-020-03646-8.

I. Subramanian, S. Verma, S. Kumar, A. Jere, and K. Anamika, “Multi-omics Data Integration, Interpretation, and Its Application,” Bioinform Biol Insights, vol. 14, p. 117793221989905, Jan. 2020, doi: 10.1177/1177932219899051.

Q. ul A. Farooq, Z. Shaukat, S. Aiman, and C.-H. Li, “Protein-protein interactions: Methods, databases, and applications in virus-host study,” World J Virol, vol. 10, no. 6, pp. 288–300, Nov. 2021, doi: 10.5501/wjv.v10.i6.288.

S. Wang, K. Dong, D. Liang, Y. Zhang, X. Li, and T. Song, “MIPPIS: protein–protein interaction site prediction network with multi-information fusion,” BMC Bioinformatics, vol. 25, no. 1, p. 345, Nov. 2024, doi: 10.1186/s12859-024-05964-7.

E. J. Draizen, J. Readey, C. Mura, and P. E. Bourne, “Prop3D: A flexible, Python-based platform for machine learning with protein structural properties and biophysical data,” BMC Bioinformatics, vol. 25, no. 1, p. 11, Jan. 2024, doi: 10.1186/s12859-023-05586-5.

A. Dasgupta and R. K. De, “Artificial intelligence in systems biology,” 2023, pp. 153–201. doi: 10.1016/bs.host.2023.06.004.

D. Vella, S. Marini, F. Vitali, D. Di Silvestre, G. Mauri, and R. Bellazzi, “MTGO: PPI Network Analysis Via Topological and Functional Module Identification,” Sci Rep, vol. 8, no. 1, p. 5499, Apr. 2018, doi: 10.1038/s41598-018-23672-0.

C.-J. Tsai, B. Ma, and R. Nussinov, “Protein–protein interaction networks: how can a hub protein bind so many different partners?,” Trends Biochem Sci, vol. 34, no. 12, pp. 594–600, Dec. 2009, doi: 10.1016/j.tibs.2009.07.007.

S. Dilmaghani, M. R. Brust, C. H. C. Ribeiro, E. Kieffer, G. Danoy, and P. Bouvry, “From communities to protein complexes: A local community detection algorithm on PPI networks,” PLoS One, vol. 17, no. 1, p. e0260484, Jan. 2022, doi: 10.1371/journal.pone.0260484.

I. Bludau and R. Aebersold, “Proteomic and interactomic insights into the molecular basis of cell functional diversity,” Nat Rev Mol Cell Biol, vol. 21, no. 6, pp. 327–340, Jun. 2020, doi: 10.1038/s41580-020-0231-2.

X. Yan, P. Mischel, and H. Chang, “Extrachromosomal DNA in cancer,” Nat Rev Cancer, vol. 24, no. 4, pp. 261–273, Apr. 2024, doi: 10.1038/s41568-024-00669-8.

D. Silverbush and R. Sharan, “A systematic approach to orient the human protein–protein interaction network,” Nat Commun, vol. 10, no. 1, p. 3015, Jul. 2019, doi: 10.1038/s41467-019-10887-6.

H. Lu et al., “Recent advances in the development of protein–protein interactions modulators: mechanisms and clinical trials,” Signal Transduct Target Ther, vol. 5, no. 1, p. 213, Sep. 2020, doi: 10.1038/s41392-020-00315-3.

A. K. Wong, R. S. G. Sealfon, C. L. Theesfeld, and O. G. Troyanskaya, “Decoding disease: from genomes to networks to phenotypes,” Nat Rev Genet, vol. 22, no. 12, pp. 774–790, Dec. 2021, doi: 10.1038/s41576-021-00389-x.

G. Kustatscher et al., “Understudied proteins: opportunities and challenges for functional proteomics,” Nat Methods, vol. 19, no. 7, pp. 774–779, Jul. 2022, doi: 10.1038/s41592-022-01454-x.

A. Rodina et al., “Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics identify cancer-specific mechanisms of stress adaptation,” Nat Commun, vol. 14, no. 1, p. 3742, Jun. 2023, doi: 10.1038/s41467-023-39241-7.

M. W. Gonzalez and M. G. Kann, “Chapter 4: Protein Interactions and Disease,” PLoS Comput Biol, vol. 8, no. 12, p. e1002819, Dec. 2012, doi: 10.1371/journal.pcbi.1002819.

D. Mandair, J. S. Reis-Filho, and A. Ashworth, “Biological insights and novel biomarker discovery through deep learning approaches in breast cancer histopathology,” NPJ Breast Cancer, vol. 9, no. 1, p. 21, Apr. 2023, doi: 10.1038/s41523-023-00518-1.

S. Akbarzadeh, Ö. Coşkun, and B. Günçer, “Studying protein–protein interactions: Latest and most popular approaches,” J Struct Biol, vol. 216, no. 4, p. 108118, Dec. 2024, doi: 10.1016/j.jsb.2024.108118.

H. Kang, “The prevention and handling of the missing data,” Korean J Anesthesiol, vol. 64, no. 5, p. 402, 2013, doi: 10.4097/kjae.2013.64.5.402.

J.-S. Lin and E.-M. Lai, “Protein–Protein Interactions: Co-Immunoprecipitation,” 2017, pp. 211–219. doi: 10.1007/978-1-4939-7033-9_17.

R. S. Baker and A. Hawn, “Algorithmic Bias in Education,” Int J Artif Intell Educ, vol. 32, no. 4, pp. 1052–1092, Dec. 2022, doi: 10.1007/s40593-021-00285-9.

S. E. Whang, Y. Roh, H. Song, and J.-G. Lee, “Data collection and quality challenges in deep learning: a data-centric AI perspective,” The VLDB Journal, vol. 32, no. 4, pp. 791–813, Jul. 2023, doi: 10.1007/s00778-022-00775-9.

A. Rodina et al., “Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics identify cancer-specific mechanisms of stress adaptation,” Nat Commun, vol. 14, no. 1, p. 3742, Jun. 2023, doi: 10.1038/s41467-023-39241-7.

V. S. Rao, K. Srinivas, G. N. Sujini, and G. N. S. Kumar, “Protein-Protein Interaction Detection: Methods and Analysis,” Int J Proteomics, vol. 2014, pp. 1–12, Feb. 2014, doi: 10.1155/2014/147648.

E. Nogales and J. Mahamid, “Bridging structural and cell biology with cryo-electron microscopy,” Nature, vol. 628, no. 8006, pp. 47–56, Apr. 2024, doi: 10.1038/s41586-024-07198-2.

H. Wang et al., “Scientific discovery in the age of artificial intelligence,” Nature, vol. 620, no. 7972, pp. 47–60, Aug. 2023, doi: 10.1038/s41586-023-06221-2.

N. Zhang et al., “Graph-Based Spatial Proximity of Super-Resolved Protein–Protein Interactions Predicts Cancer Drug Responses in Single Cells,” Cell Mol Bioeng, vol. 17, no. 5, pp. 467–490, Oct. 2024, doi: 10.1007/s12195-024-00822-1.

M. Lee, “Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review,” Molecules, vol. 28, no. 13, p. 5169, Jul. 2023, doi: 10.3390/molecules28135169.

M. Soori, B. Arezoo, and R. Dastres, “Artificial intelligence, machine learning and deep learning in advanced robotics, a review,” Cognitive Robotics, vol. 3, pp. 54–70, 2023, doi: 10.1016/j.cogr.2023.04.001.

T. Jendoubi, “Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer,” Metabolites, vol. 11, no. 3, p. 184, Mar. 2021, doi: 10.3390/metabo11030184.

M. Slobodyanyuk, A. T. Bahcheli, Z. P. Klein, M. Bayati, L. J. Strug, and J. Reimand, “Directional integration and pathway enrichment analysis for multi-omics data,” Nat Commun, vol. 15, no. 1, p. 5690, Jul. 2024, doi: 10.1038/s41467-024-49986-4.

K. B. Johnson et al., “Precision Medicine, AI, and the Future of Personalized Health Care,” Clin Transl Sci, vol. 14, no. 1, pp. 86–93, Jan. 2021, doi: 10.1111/cts.12884.

T. Wang et al., “MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification,” Nat Commun, vol. 12, no. 1, p. 3445, Jun. 2021, doi: 10.1038/s41467-021-23774-w.