ACSNI: An unsupervised machine-learning tool for prediction of tissue-specific pathway components using gene expression profiles

Journal article


Anene, C., Khan F., Bewicke-Copley, F., Maniati, E. and Wang, J. (2021). ACSNI: An unsupervised machine-learning tool for prediction of tissue-specific pathway components using gene expression profiles. Patterns. 2 (6), p. 100270. https://doi.org/10.1016/j.patter.2021.100270
AuthorsAnene, C., Khan F., Bewicke-Copley, F., Maniati, E. and Wang, J.
Abstract

Determining the tissue- and disease-specific circuit of biological pathways remains a fundamental goal of molecular biology. Many components of these biological pathways still remain unknown, hindering the full and accurate characterization of biological processes of interest. Here we describe ACSNI, an algorithm that combines prior knowledge of biological processes with a deep neural network to effectively decompose gene expression profiles (GEPs) into multi-variable pathway activities and identify unknown pathway components. Experiments on public GEP data show that ACSNI predicts cogent components of mTOR, ATF2, and HOTAIRM1 signaling that recapitulate regulatory information from genetic perturbation and transcription factor binding datasets. Our framework provides a fast and easy-to-use method to identify components of signaling pathways as a tool for molecular mechanism discovery and to prioritize genes for designing future targeted experiments (https://github.com/caanene1/ACSNI).

Keywordsautoencoder; cell signaling; dimension reduction; gene expression; gene-regulatory networks; machine learning; neural network; pathways; systems biology
Year2021
JournalPatterns
Journal citation2 (6), p. 100270
PublisherElsevier
ISSN2666-3899
Digital Object Identifier (DOI)https://doi.org/10.1016/j.patter.2021.100270
Web address (URL)https://www.cell.com/patterns/fulltext/S2666-3899(21)00096-9?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS2666389921000969%3Fshowall%3Dtrue
Publication dates
Print11 Jun 2021
Publication process dates
Accepted28 Apr 2021
Deposited16 Jun 2022
Accepted author manuscript
License
File Access Level
Open
Permalink -

https://openresearch.lsbu.ac.uk/item/8zyq1

Download files


Accepted author manuscript
  • 2
    total views
  • 1
    total downloads
  • 0
    views this month
  • 0
    downloads this month

Export as

Related outputs

Decosus: An R Framework for Universal Integration of Cell Proportion Estimation Methods.
Anene, C.A., Taggart, E., Harwood, C., Pennington, D.J. and Wang, J. (2022). Decosus: An R Framework for Universal Integration of Cell Proportion Estimation Methods. Frontiers in genetics. 13, p. 802838. https://doi.org/10.3389/fgene.2022.802838
Dysregulation of the miR-30c/DLL4 axis by circHIPK3 is essential for KSHV lytic replication
Harper, K.L., Mottram, T.J., Anene, C., Foster, B., Patterson, M.R., McDonnell, E., Macdonald, A., Westhead, D. and Whitehouse, A. (2022). Dysregulation of the miR-30c/DLL4 axis by circHIPK3 is essential for KSHV lytic replication. EMBO Reports. (e54117). https://doi.org/10.15252/embr.202154117
The Genomic Landscape of Actinic Keratosis
Thomson, J., Bewicke-Copley, F., Anene, C., Gulati, A., Nagano, A., Purdie, K., Inman, G.J., Proby, C.M., Leigh, I.M., Harwood, C.A. and Wang, J. (2021). The Genomic Landscape of Actinic Keratosis. The Journal of Investigative Dermatology. https://doi.org/10.1016/j.jid.2020.12.024
SFPQ promotes an oncogenic transcriptomic state in melanoma
Bi, O., Anene, C., Nsengimana, J., Roberts, W., Newton-Bishop, J. and Boyne, J.R. (2021). SFPQ promotes an oncogenic transcriptomic state in melanoma. Oncogene. 40, pp. 5192-5203. https://doi.org/10.1038/s41388-021-01912-4
Systematic Evaluation of Somatic Cis-Regulatory Mutations in Follicular Lymphoma
Firat U., Bewicke-Copley, F., Anene, C., Schlesner, M., Icgc MMML-Seq Project3, Siebert, R., Okosun, J., Fitzgibbon, J. and Wang, J (2020). Systematic Evaluation of Somatic Cis-Regulatory Mutations in Follicular Lymphoma. American Society of Hematology. https://doi.org/10.1182/blood-2020-142623
The role of CAF derived exosomal microRNAs in the tumour microenvironment of melanoma
Shelton, M., Anene, C., Nsengimana, J., Roberts, W., Newton-Bishop, J. and Boyne, J.R. (2020). The role of CAF derived exosomal microRNAs in the tumour microenvironment of melanoma. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer. 1875 (1), p. 188456. https://doi.org/10.1016/j.bbcan.2020.188456
Merkel cell polyomavirus small tumour antigen activates the p38 MAPK pathway to enhance cellular motility
Dobson, S.J., Anene, C., Boyne, J.R., Mankouri, J., Macdonald, A. and Whitehouse, A (2020). Merkel cell polyomavirus small tumour antigen activates the p38 MAPK pathway to enhance cellular motility. Biochemical Journal. 477 (14), pp. 2721-2733. https://doi.org/10.1042/BCJ20200399
Platelet microparticle delivered microRNA-Let-7a promotes the angiogenic switch
Anene, C., Graham, A.M., Boyne, J. and Roberts, W. (2018). Platelet microparticle delivered microRNA-Let-7a promotes the angiogenic switch. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease. https://doi.org/10.1016/j.bbadis.2018.04.013
Platelet induced hepatocellular carcinoma HEPG2 cell proliferation and angiogenic potential is integrin IIb3 dependent.
Rashed, Al-Hammad, Anene, C., Graham, A.M. and Roberts, W. (2015). Platelet induced hepatocellular carcinoma HEPG2 cell proliferation and angiogenic potential is integrin IIb3 dependent. Taylor & Francis. https://doi.org/10.3109/09537104.2015.1115703