Quinn, T. P., Crowley, T. M. & Richardson, M. F. Benchmarking differential expression analysis tools for RNA-Seq: normalization-based vs. log-ratio transformation-based methods. BMC Bioinforma. 19, 1–15 (2018).Article
Google Scholar
Smid, M. et al. Gene length corrected trimmed mean of M-values (GeTMM) processing of RNA-seq data performs similarly in intersample analyses while improving intrasample comparisons. BMC Bioinforma. 19, 1–13 (2018).Article
Google Scholar
Zyprych-Walczak, J. et al. The impact of normalization methods on RNA-Seq data analysis. Biomed Res. Int. 2015, 621690 (2015).Anders, S. & Huber, W. Differential expression analysis for sequence count data. Nat. Preced. 1, 1 (2010).Anders, S. et al. Count-based differential expression analysis of RNA sequencing data using R and Bioconductor. Nat. Protoc. 8, 1765–1786 (2013).Article
PubMed
Google Scholar
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).Article
PubMed
PubMed Central
Google Scholar
Li, P., Piao, Y., Shon, H. S. & Ryu, K. H. Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data. BMC Bioinforma. 16, 1–9 (2015).Article
Google Scholar
Zhao, Y. et al. TPM, FPKM, or normalized counts? A comparative study of quantification measures for the analysis of RNA-seq data from the NCI patient-derived models repository. J. Transl. Med. 19, 1–15 (2021).Article
CAS
Google Scholar
Stupnikov, A. et al. Robustness of differential gene expression analysis of RNA-seq. Comput. Struct. Biotechnol. J. 19, 3470–3481 (2021).Article
CAS
PubMed
PubMed Central
Google Scholar
Evans, C., Hardin, J. & Stoebel, D. M. Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions. Brief. Bioinform. 19, 776–792 (2018).Article
CAS
PubMed
Google Scholar
Mo, M. L., Jamshidi, N., Palsson & B, Ø. A genome-scale, constraint-based approach to systems biology of human metabolism. Mol. Biosyst. 3, 598–603 (2007).Article
CAS
PubMed
Google Scholar
Bordbar, A. & Palsson, B. O. Using the reconstructed genome-scale human metabolic network to study physiology and pathology. J. Intern. Med. 271, 131–141 (2012).Article
CAS
PubMed
PubMed Central
Google Scholar
Sen, P. & Orešič, M. Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine. Metabolites 13, 855 (2023).Article
CAS
PubMed
PubMed Central
Google Scholar
Cho, J. S., Gu, C., Han, T. H., Ryu, J. Y. & Lee, S. Y. Reconstruction of context-specific genome-scale metabolic models using multiomics data to study metabolic rewiring. Curr. Opin. Syst. Biol. 15, 1–11 (2019).Article
Google Scholar
Machado, D. & Herrgård, M. Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. PLoS Comput Biol. 10, e1003580 (2014).Article
PubMed
PubMed Central
Google Scholar
Jamialahmadi, O., Hashemi-Najafabadi, S., Motamedian, E., Romeo, S. & Bagheri, F. A benchmark-driven approach to reconstruct metabolic networks for studying cancer metabolism. PLoS Comput. Biol. 15, e1006936 (2019).Article
PubMed
PubMed Central
Google Scholar
Pacheco, M. P., Pfau, T. & Sauter, T. Benchmarking procedures for high-throughput context specific reconstruction algorithms. Front. Physiol. 6, 410 (2016).Article
PubMed
PubMed Central
Google Scholar
Vieira, V., Ferreira, J. & Rocha, M. A pipeline for the reconstruction and evaluation of context-specific human metabolic models at a large-scale. PLoS Comput. Biol. 18, e1009294 (2022).Article
CAS
PubMed
PubMed Central
Google Scholar
Opdam, S. et al. A systematic evaluation of methods for tailoring genome-scale metabolic models. Cell Syst. 4, 318–329 (2017).Article
CAS
PubMed
PubMed Central
Google Scholar
Zur, H., Ruppin, E. & Shlomi, T. iMAT: an integrative metabolic analysis tool. Bioinformatics 26, 3140–3142 (2010).Article
CAS
PubMed
Google Scholar
Stempler, S., Yizhak, K. & Ruppin, E. Integrating transcriptomics with metabolic modeling predicts biomarkers and drug targets for Alzheimer’s disease. PLoS One 9, e105383 (2014).Article
PubMed
PubMed Central
Google Scholar
Varma, V. R. et al. Abnormal brain cholesterol homeostasis in Alzheimer’s disease—a targeted metabolomic and transcriptomic study. NPJ aging Mech. Dis. 7, 1–14 (2021).Article
Google Scholar
Cheng, K. et al. Genome-scale metabolic modeling reveals SARS-CoV-2-induced metabolic changes and antiviral targets. Mol. Syst. Biol. 17, e10260 (2021).Article
CAS
PubMed
PubMed Central
Google Scholar
Katzir, R. et al. The landscape of tiered regulation of breast cancer cell metabolism. Sci. Rep. 9, 17760 (2019).Article
PubMed
PubMed Central
Google Scholar
Blazier, A. S. & Papin, J. A. Integration of expression data in genome-scale metabolic network reconstructions. Front. Physiol. 3, 299 (2012).Article
PubMed
PubMed Central
Google Scholar
Agren, R. et al. Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLoS Comput. Biol. 8, e1002518 (2012).Article
CAS
PubMed
PubMed Central
Google Scholar
Kishk, A. et al. Review of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegenerative Diseases. Cells 11, 2486 (2022).Article
CAS
PubMed
PubMed Central
Google Scholar
Wang, Y., Eddy, J. A. & Price, N. D. Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE. BMC Syst. Biol. 6, 1–16 (2012).Article
CAS
Google Scholar
Wang, H. et al. Genome-scale metabolic network reconstruction of model animals as a platform for translational research. Proc. Natl Acad. Sci. 118, e2102344118 (2021).Article
CAS
PubMed
PubMed Central
Google Scholar
Mucke, L. Alzheimer’s disease. Nature 461, 895–897 (2009).Article
CAS
PubMed
Google Scholar
Venuta, F. et al. Lung cancer in elderly patients. J. Thorac. Dis. 8, S908 (2016).Article
PubMed
PubMed Central
Google Scholar
Cancer Genome Atlas, R. N. Comprehensive molecular profiling of lung adenocarcinoma. Nature 511, 543 (2014).Podcasy, J. L. & Epperson, C. N. Considering sex and gender in Alzheimer disease and other dementias. Dialogues Clin. Neurosci. 18, 437–446 (2016).Article
PubMed
PubMed Central
Google Scholar
Tammemagi, C. M., Neslund-Dudas, C., Simoff, M. & Kvale, P. In lung cancer patients, age, race-ethnicity, gender and smoking predict adverse comorbidity, which in turn predicts treatment and survival. J. Clin. Epidemiol. 57, 597–609 (2004).Article
PubMed
Google Scholar
Stapelfeld, C., Dammann, C. & Maser, E. Sex-specificity in lung cancer risk. Int. J. Cancer 146, 2376–2382 (2020).Article
CAS
PubMed
Google Scholar
Merchant, J. P. et al. Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer’s disease. Commun. Biol. 6, 503 (2023).Article
PubMed
PubMed Central
Google Scholar
Lynch, M. T. et al. Evaluating genomic signatures of aging in brain tissue as it relates to Alzheimer’s disease. Sci. Rep. 13, 14747 (2023).Article
CAS
PubMed
PubMed Central
Google Scholar
Posma, J. M. et al. Optimized Phenotypic Biomarker Discovery and Confounder Elimination via Covariate-Adjusted Projection to Latent Structures from Metabolic Spectroscopy Data. J. Proteome Res. 17, 1586–1595 (2018).Article
CAS
PubMed
PubMed Central
Google Scholar
Radkiewicz, C. et al. Sex and survival in non-small cell lung cancer: A nationwide cohort study. PLoS One 14, e0219206 (2019).Article
CAS
PubMed
PubMed Central
Google Scholar
Raškevičius, V. et al. Genome scale metabolic models as tools for drug design and personalized medicine. PLoS One 13, e0190636 (2018).Article
PubMed
PubMed Central
Google Scholar
Barata, T., Vieira, V., Rodrigues, R., das Neves, R. P. & Rocha, M. Reconstruction of tissue-specific genome-scale metabolic models for human cancer stem cells. Comput. Biol. Med. 142, 105177 (2022).Article
CAS
PubMed
Google Scholar
Baloni, P. et al. Metabolic network analysis reveals altered bile acid synthesis and metabolism in Alzheimer’s disease. Cell Reports Med. 1, 8 (2020).Vlassis, N., Pacheco, M. P. & Sauter, T. Fast reconstruction of compact context-specific metabolic network models. PLoS Comput. Biol. 10, e1003424 (2014).Article
PubMed
PubMed Central
Google Scholar
Ramon, C. & Stelling, J. Functional comparison of metabolic networks across species. Nat. Commun. 14, 1699 (2023).Article
CAS
PubMed
PubMed Central
Google Scholar
Choi, S. H. et al. Evaluation of logistic regression models and effect of covariates for case–control study in rna-seq analysis. BMC Bioinforma. 18, 1–13 (2017).Article
Google Scholar
Düz, E. & Çakir, T. Effect of RNA-Seq data normalization on protein interactome mapping for Alzheimer’s disease. Comput. Biol. Chem. 109, 108028 (2024).Article
PubMed
Google Scholar
Corchete, L. A. et al. Systematic comparison and assessment of RNA-seq procedures for gene expression quantitative analysis. Sci. Rep. 10, 19737 (2020).Article
CAS
PubMed
PubMed Central
Google Scholar
Maza, E., Frasse, P., Senin, P., Bouzayen, M. & Zouine, M. Comparison of normalization methods for differential gene expression analysis in RNA-Seq experiments: a matter of relative size of studied transcriptomes. Commun. \ Integr. Biol. 6, e25849 (2013).Article
PubMed
PubMed Central
Google Scholar
De Jager, P. L. et al. A multi-omic atlas of the human frontal cortex for aging and Alzheimer’s disease research. Sci. data 5, 1–13 (2018).Article
Google Scholar
Bioinformatics, B. FastQC: a quality control tool for high throughput sequence data. (Cambridge, UK Babraham Inst., 2011).Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article
CAS
PubMed
PubMed Central
Google Scholar
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).Article
CAS
PubMed
Google Scholar
Frankish, A. et al. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res 47, D766–D773 (2019).Article
CAS
PubMed
Google Scholar
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).Article
CAS
PubMed
Google Scholar
Weinstein, J. N. et al. The cancer genome atlas pan-cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).Article
PubMed
PubMed Central
Google Scholar
Colaprico, A. et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res 44, e71–(2016).Article
PubMed
Google Scholar
Abrams, Z. B., Johnson, T. S., Huang, K., Payne, P. R. O. & Coombes, K. A protocol to evaluate RNA sequencing normalization methods. BMC Bioinforma. 20, 1–7 (2019).Article
CAS
Google Scholar
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).Article
CAS
PubMed
Google Scholar
Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).Article
PubMed
PubMed Central
Google Scholar
Robinson, J. L. et al. An atlas of human metabolism. Sci. Signal. 13, eaaz1482 (2020).Lüleci, H. B., Uzuner, D., Çakır, T. & Thambisetty, M. Computational Approaches to Assess Abnormal Metabolism in Alzheimer’s Disease Using Transcriptomics. Methods Mol. Biol. 2561, 173–189 (Springer, 2023).Shlomi, T., Cabili, M. N., Herrgård, M. J., Palsson, B. Ø. & Ruppin, E. Network-based prediction of human tissue-specific metabolism. Nat. Biotechnol. 26, 1003–1010 (2008).Article
CAS
PubMed
Google Scholar
Heirendt, L. et al. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v. 3.0. Nat. Protoc. 14, 639–702 (2019).Article
CAS
PubMed
PubMed Central
Google Scholar
Wang, H. et al. RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor. PLoS Comput. Biol. 14, e1006541 (2018).Article
PubMed
PubMed Central
Google Scholar
Fisher, R. A. The genetical theory of natural selection. (Рипол Классик, 1958).Xie, Z. et al. Gene Set Knowledge Discovery with Enrichr. Curr. Protoc. 1, 1–51 (2021).Article
Google Scholar
Piñero, J. et al. DisGeNET: A comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res 45, D833–D839 (2017).Article
PubMed
Google Scholar
Ceylan, B., Düz, E. & Çakir, T. Personalized Protein ‑ Protein Interaction Networks Towards Unraveling the Molecular Mechanisms of Alzheimer ’ s Disease. Mol. Neurobiol. https://doi.org/10.1007/s12035-023-03690-4 (2023).Li, C., Long, Q., Zhang, D., Li, J. & Zhang, X. Identification of a four-gene panel predicting overall survival for lung adenocarcinoma. BMC Cancer 20, 1–16 (2020).Article
Google Scholar
He, L., Chen, J., Xu, F. & Li, J. Prognostic Implication of a Metabolism-Associated Gene Signature in Lung Adenocarcinoma. Mol. Ther. – Oncolytics 19, 265–277 (2020).Article
CAS
PubMed
PubMed Central
Google Scholar
Morikawa, K. et al. A Prospective Validation Study of Lung Cancer Gene Panel Testing Using Cytological Specimens. Cancers. 14, 3784 (2022).Liu, Y., Zhao, M. & Qu, H. A Database of Lung Cancer-Related Genes for the Identification of Subtype-Specific Prognostic Biomarkers. Biology. 12, 357 (2023).Herbst, R. S., Morgensztern, D. & Boshoff, C. The biology and management of non-small cell lung cancer. Nature 553, 446–454 (2018).Article
CAS
PubMed
Google Scholar
Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Oxf. Univ. Press 28, 27–30 (2000).CAS
Google Scholar
Niwattanakul, S., Singthongchai, J., Naenudorn, E. & Wanapu, S. Using of Jaccard coefficient for keywords similarity. In Proceedings of the international multiconference of engineers and computer scientists 1, 380–384 (2013).