Optimisation Models for Pathway Activity Inference in Cancer.
Simple Summary: Subtype classification and prognostic prediction are key research targets in complex diseases such as cancer. In this work, an optimisation model was designed to infer the activity of biological pathways from gene expression values. The optimisation model enables the pathway activity...
| Published in: | Cancers Vol. 15; no. 6; pp. 1787 - 1806 |
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| Main Authors: | , , , , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
MDPI
Mar2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=162751186&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162751186 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Mar2023 vid: 15 iid: 6 pid: 97109 pub: MDPI artinfo: ui: 162751186 162751186 162751186 10.3390/cancers15061787 162751186 ppf: 1787 ppct: 19 formats: tig: atl: Optimisation Models for Pathway Activity Inference in Cancer. aug: au: Chen, Yongnan Liu, Songsong Papageorgiou, Lazaros G. Theofilatos, Konstantinos Tsoka, Sophia affil: Department of Informatics, Faculty of Natural, Mathematical and Engineering Sciences, King's College London, Bush House, London WC2B 4BG, UK sug: subj: Neoplasms Classification Models, Biological Gene Expression Profiling Signal Transduction Human Models, Theoretical Phenotype Sequence Analysis RNA Genes Breast Neoplasms Colorectal Neoplasms Survival Analysis Funding Source ab: Simple Summary: Subtype classification and prognostic prediction are key research targets in complex diseases such as cancer. In this work, an optimisation model was designed to infer the activity of biological pathways from gene expression values. The optimisation model enables the pathway activity values to separate the sample subtypes to the greatest extent, thereby improving sample classification accuracy. The proposed model was evaluated on cancer molecular subtype classification, robustness to noisy data and survival prediction, and allowed the identification of disease-important genes and pathways. Background: With advances in high-throughput technologies, there has been an enormous increase in data related to profiling the activity of molecules in disease. While such data provide more comprehensive information on cellular actions, their large volume and complexity pose difficulty in accurate classification of disease phenotypes. Therefore, novel modelling methods that can improve accuracy while offering interpretable means of analysis are required. Biological pathways can be used to incorporate a priori knowledge of biological interactions to decrease data dimensionality and increase the biological interpretability of machine learning models. Methodology: A mathematical optimisation model is proposed for pathway activity inference towards precise disease phenotype prediction and is applied to RNA-Seq datasets. The model is based on mixed-integer linear programming (MILP) mathematical optimisation principles and infers pathway activity as the linear combination of pathway member gene expression, multiplying expression values with model-determined gene weights that are optimised to maximise discrimination of phenotype classes and minimise incorrect sample allocation. Results: The model is evaluated on the transcriptome of breast and colorectal cancer, and exhibits solution results of good optimality as well as good prediction performance on related cancer subtypes. Two baseline pathway activity inference methods and three advanced methods are used for comparison. Sample prediction accuracy, robustness against noise expression data, and survival analysis suggest competitive prediction performance of our model while providing interpretability and insight on key pathways and genes. Overall, our work demonstrates that the flexible nature of mathematical programming lends itself well to developing efficient computational strategies for pathway activity inference and disease subtype prediction. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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