Molecules and Cells

Cited by CrossRef (36)

  1. Priyanka Mallick, Sebabrata Maity, Oishee Chakrabarti, Saikat Chakrabarti. Role of systems biology and multi-omics analyses in delineating spatial interconnectivity and temporal dynamicity of ER stress mediated cellular responses. Biochimica et Biophysica Acta (BBA) - Molecular Cell Research 2022;1869:119210
    https://doi.org/10.1016/j.bbamcr.2022.119210
  2. Faheem Ahmed, Anupama Samantasinghar, Afaque Manzoor Soomro, Sejong Kim, Kyung Hyun Choi. A systematic review of computational approaches to understand cancer biology for informed drug repurposing. Journal of Biomedical Informatics 2023;142:104373
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  3. Zhenzhong Deng, Yongkun Ji, Bing Han, Zhongming Tan, Yuqi Ren, Jinghan Gao, Nan Chen, Cong Ma, Yichi Zhang, Yunhai Yao, Hong Lu, Heqing Huang, Midie Xu, Lei Chen, Leizhen Zheng, Jianchun Gu, Deyi Xiong, Jianxin Zhao, Jinyang Gu, Zutao Chen, Ke Wang. Early detection of hepatocellular carcinoma via no end-repair enzymatic methylation sequencing of cell-free DNA and pre-trained neural network. Genome Med 2023;15
    https://doi.org/10.1186/s13073-023-01238-8
  4. Bjoern Titz, Oksana Lavrynenko, Nikolai V. Ivanov. Mass Spectrometry for Lipidomics. 2023.
    https://doi.org/10.1002/9783527836512.ch23
  5. Roberto Gasparri, Alessandra Guaglio, Lorenzo Spaggiari. Early Diagnosis of Lung Cancer: The Urgent Need of a Clinical Test. JCM 2022;11:4398
    https://doi.org/10.3390/jcm11154398
  6. Alex C. Soupir, Yijun Tian, Paul A. Stewart, Yury O. Nunez-Lopez, Brandon J. Manley, Bruna Pellini, Amanda M. Bloomer, Jingsong Zhang, Qianxing Mo, Douglas C. Marchion, Min Liu, John M. Koomen, Erin M. Siegel, Liang Wang. Detectable Lipidomes and Metabolomes by Different Plasma Exosome Isolation Methods in Healthy Controls and Patients with Advanced Prostate and Lung Cancer. IJMS 2023;24:1830
    https://doi.org/10.3390/ijms24031830
  7. Luka Peric, Sonja Vukadin, Ana Petrovic, Lucija Kuna, Nora Puseljic, Renata Sikora, Karla Rozac, Aleksandar Vcev, Martina Smolic. Glycosylation Alterations in Cancer Cells, Prognostic Value of Glycan Biomarkers and Their Potential as Novel Therapeutic Targets in Breast Cancer. Biomedicines 2022;10:3265
    https://doi.org/10.3390/biomedicines10123265
  8. Matyas Bukva, Gabriella Dobra, Edina Gyukity-Sebestyen, Timea Boroczky, Marietta Margareta Korsos, David G. Meckes, Peter Horvath, Krisztina Buzas, Maria Harmati. Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumor-specificity and predictive potential of extracellular vesicles for cell invasion and proliferation – A meta-analysis. Cell Commun Signal 2023;21
    https://doi.org/10.1186/s12964-023-01344-5
  9. Ivan Salido-Guadarrama, Sandra L. Romero-Cordoba, Bertha Rueda-Zarazua. Multi-Omics Mining of lncRNAs with Biological and Clinical Relevance in Cancer. IJMS 2023;24:16600
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  11. Sima Ranjbari, Suzan Arslanturk. Integration of incomplete multi-omics data using Knowledge Distillation and Supervised Variational Autoencoders for disease progression prediction. Journal of Biomedical Informatics 2023;147:104512
    https://doi.org/10.1016/j.jbi.2023.104512
  12. Nima Zafari, Parsa Bathaei, Mahla Velayati, Fatemeh Khojasteh-Leylakoohi, Majid Khazaei, Hamid Fiuji, Mohammadreza Nassiri, Seyed Mahdi Hassanian, Gordon A. Ferns, Elham Nazari, Amir Avan. Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer. Computers in Biology and Medicine 2023;155:106639
    https://doi.org/10.1016/j.compbiomed.2023.106639
  13. Shipan Fan, Ansgar Poetsch. Proteomic Research of Extracellular Vesicles in Clinical Biofluid. Proteomes 2023;11:18
    https://doi.org/10.3390/proteomes11020018
  14. Qing-Qing Cao, Jian-Ping Zhao, Chun-Hou Zheng. Multi-channel Partial Graph Integration Learning of Partial Multi-omics Data for Cancer Subtyping. CBIO 2023;18:680
    https://doi.org/10.2174/1574893618666230519145545
  15. Farhad Vahid, Kimia Hajizadeghan, Adeleh Khodabakhshi. Nutritional Metabolomics in Diet–Breast Cancer Relations: Current Research, Challenges, and Future Directions—A Review. Biomedicines 2023;11:1845
    https://doi.org/10.3390/biomedicines11071845
  16. Jorge Espinosa-Espinosa, Anchel González-Barriga, Arturo López-Castel, Rubén Artero. Deciphering the Complex Molecular Pathogenesis of Myotonic Dystrophy Type 1 through Omics Studies. IJMS 2022;23:1441
    https://doi.org/10.3390/ijms23031441
  17. Abhishek Mohanty, Daniel Catchpoole. Omics approaches in cancer management: Focussing on biobanks as emerging platforms for biomarker-based precision oncology. J Precis Oncol 2022;2:67
    https://doi.org/10.4103/jpo.jpo_18_22
  18. Sayantan Bhattacharyya, Shafqat F. Ehsan, Loukia G. Karacosta. Phenotypic maps for precision medicine: a promising systems biology tool for assessing therapy response and resistance at a personalized level. Front. Netw. Physiol. 2023;3
    https://doi.org/10.3389/fnetp.2023.1256104
  19. Dai Li, Feng Ju, Han Wang, Chunfu Fan, Jule C. Jacob, Sheraz Gul, Andrea Zaliani, Thomas Wartmann, Maria Cristina Polidori, Christiane J. Bruns, Yue Zhao. Combination of the biomarkers for aging and cancer? - Challenges and current status. Translational Oncology 2023;38:101783
    https://doi.org/10.1016/j.tranon.2023.101783
  20. Valeria Capaci, Lorenzo Monasta, Michelangelo Aloisio, Eduardo Sommella, Emanuela Salviati, Pietro Campiglia, Manuela Giovanna Basilicata, Feras Kharrat, Danilo Licastro, Giovanni Di Lorenzo, Federico Romano, Giuseppe Ricci, Blendi Ura. A Multi-Omics Approach Revealed Common Dysregulated Pathways in Type One and Type Two Endometrial Cancers. IJMS 2023;24:16057
    https://doi.org/10.3390/ijms242216057
  21. Xiao Li, Jie Ma, Ling Leng, Mingfei Han, Mansheng Li, Fuchu He, Yunping Zhu. MoGCN: A Multi-Omics Integration Method Based on Graph Convolutional Network for Cancer Subtype Analysis. Front. Genet. 2022;13
    https://doi.org/10.3389/fgene.2022.806842
  22. Ping Yue, Bingjie Han, Yi Zhao. Focus on the molecular mechanisms of cisplatin resistance based on multi-omics approaches. Mol. Omics 2023;19:297
    https://doi.org/10.1039/D2MO00220E
  23. Dongjin Leng, Linyi Zheng, Yuqi Wen, Yunhao Zhang, Lianlian Wu, Jing Wang, Meihong Wang, Zhongnan Zhang, Song He, Xiaochen Bo. A benchmark study of deep learning-based multi-omics data fusion methods for cancer. Genome Biol 2022;23
    https://doi.org/10.1186/s13059-022-02739-2
  24. Amit Kumar, Swadesh K. Das, Luni Emdad, Paul B. Fisher. . 2022.
    https://doi.org/10.1016/bs.acr.2023.03.005
  25. Keisuke Okuno, Raju Kandimalla, Marta Mendiola, Francesc Balaguer, Luis Bujanda, Carlos Fernandez-Martos, Jorge Aparicio, Jaime Feliu, Masanori Tokunaga, Yusuke Kinugasa, Joan Maurel, Ajay Goel. A microRNA signature for risk-stratification and response prediction to FOLFOX-based adjuvant therapy in stage II and III colorectal cancer. Mol Cancer 2023;22
    https://doi.org/10.1186/s12943-022-01699-2
  26. Jennyfer M. García-Cárdenas, Isaac Armendáriz-Castillo, Nathali García-Cárdenas, David Pesantez-Coronel, Andrés López-Cortés, Alberto Indacochea, Santiago Guerrero. Data mining identifies novel RNA-binding proteins involved in colon and rectal carcinomas. Front. Cell Dev. Biol. 2023;11
    https://doi.org/10.3389/fcell.2023.1088057
  27. Mohamed Javad Wahadat, Sander J van Tilburg, Yvonne M Mueller, Harm de Wit, Cornelia G Van Helden-Meeuwsen, Anton W Langerak, Marike J Gruijters, Amani Mubarak, Marleen Verkaaik, Peter D Katsikis, Marjan A Versnel, Sylvia Kamphuis. Targeted multiomics in childhood-onset SLE reveal distinct biological phenotypes associated with disease activity: results from an explorative study. Lupus Sci Med 2023;10:e000799
    https://doi.org/10.1136/lupus-2022-000799
  28. Sina Marsilio, Valerie Freiche, Eric Johnson, Chiara Leo, Anton W. Langerak, Iain Peters, Mark R. Ackermann. ACVIM consensus statement guidelines on diagnosing and distinguishing low‐grade neoplastic from inflammatory lymphocytic chronic enteropathies in cats. Veterinary Internal Medicne 2023;37:794
    https://doi.org/10.1111/jvim.16690
  29. Lise Wei, Dipesh Niraula, Evan D. H. Gates, Jie Fu, Yi Luo, Matthew J. Nyflot, Stephen R. Bowen, Issam M El Naqa, Sunan Cui. Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration. BJR 2023;96
    https://doi.org/10.1259/bjr.20230211
  30. Souzana Logotheti, Eugenia Papadaki, Vasiliki Zolota, Christopher Logothetis, Aristidis G. Vrahatis, Rama Soundararajan, Vasiliki Tzelepi. Lineage Plasticity and Stemness Phenotypes in Prostate Cancer: Harnessing the Power of Integrated “Omics” Approaches to Explore Measurable Metrics. Cancers 2023;15:4357
    https://doi.org/10.3390/cancers15174357
  31. Rayan Nassani, Yahya Bokhari, Bahauddeen M. Alrfaei, Aniruddha Datta. Molecular signature to predict quality of life and survival with glioblastoma using Multiview omics model. PLoS ONE 2023;18:e0287448
    https://doi.org/10.1371/journal.pone.0287448
  32. Daechan Park. Fast Growing Furious Races for Targeting Fibroblast Growth Factor Receptors. Mol.Cells 2022;45:789
    https://doi.org/10.14348/molcells.2022.0146
  33. Yanan Li, Xue Chu, Xin Xie, Jinxiu Guo, Junjun Meng, Qingying Si, Pei Jiang. Integrating transcriptomics and metabolomics to analyze the mechanism of hypertension-induced hippocampal injury. Front. Mol. Neurosci. 2023;16
    https://doi.org/10.3389/fnmol.2023.1146525
  34. Elizaveta M. Kazakova, Elizaveta M. Solovyeva, Lev I. Levitsky, Julia A. Bubis, Daria D. Emekeeva, Anastasia A. Antonets, Alexey A. Nazarov, Mikhail V. Gorshkov, Irina A. Tarasova. Proteomics‐based scoring of cellular response to stimuli for improved characterization of signaling pathway activity. Proteomics 2023;23
    https://doi.org/10.1002/pmic.202200275
  35. Haodi Yue, Jialin Wang, Siyu Hou, Mengjun Zhang. As a potential predictor of pan-cancer, UBE2S is related to tumor-associated macrophage infiltration. Future Oncology 2023;19:1973
    https://doi.org/10.2217/fon-2023-0086
  36. Valentina Crippa, Federica Malighetti, Matteo Villa, Alex Graudenzi, Rocco Piazza, Luca Mologni, Daniele Ramazzotti. Characterization of cancer subtypes associated with clinical outcomes by multi-omics integrative clustering. Computers in Biology and Medicine 2023;162:107064
    https://doi.org/10.1016/j.compbiomed.2023.107064