Network-Based Method for Inferring Cancer Progression at the Pathway Level from Cross-Sectional Mutation Data

Wu, Hao; Gao, Lin; Kasabov, Nikola

doi:10.1109/tcbb.2016.2520934

Cited by 9 publications

(2 citation statements)

References 43 publications

(96 reference statements)

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“…We note that although such definitions have been employed in previous work, the module sizes have not been taken into consideration (Wu et al, 2015(Wu et al, , 2016.…”

Section: Problem Definitionmentioning

confidence: 99%

MEXCOwalk: mutual exclusion and coverage based random walk to identify cancer modules

et al. 2019

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Motivation Genomic analyses from large cancer cohorts have revealed the mutational heterogeneity problem which hinders the identification of driver genes based only on mutation profiles. One way to tackle this problem is to incorporate the fact that genes act together in functional modules. The connectivity knowledge present in existing protein–protein interaction (PPI) networks together with mutation frequencies of genes and the mutual exclusivity of cancer mutations can be utilized to increase the accuracy of identifying cancer driver modules. Results We present a novel edge-weighted random walk-based approach that incorporates connectivity information in the form of protein–protein interactions (PPIs), mutual exclusivity and coverage to identify cancer driver modules. MEXCOwalk outperforms several state-of-the-art computational methods on TCGA pan-cancer data in terms of recovering known cancer genes, providing modules that are capable of classifying normal and tumor samples and that are enriched for mutations in specific cancer types. Furthermore, the risk scores determined with output modules can stratify patients into low-risk and high-risk groups in multiple cancer types. MEXCOwalk identifies modules containing both well-known cancer genes and putative cancer genes that are rarely mutated in the pan-cancer data. The data, the source code and useful scripts are available at: https://github.com/abu-compbio/MEXCOwalk. Supplementary information Supplementary data are available at Bioinformatics online.

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“…We note that although such definitions have been employed in previous work, the module sizes have not been taken into consideration (Wu et al, 2015(Wu et al, , 2016.…”

Section: Problem Definitionmentioning

confidence: 99%

MEXCOwalk: mutual exclusion and coverage based random walk to identify cancer modules

et al. 2019

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“…It is also widely understood that complex, polygenic diseases often do not have clear signals characterised by only a few genes 1 . Over the past decade, there has been numerous attempts in combining machine learning methods with high-throughput gene expression data for diagnosis with promising results such as in the detection of cancer 2 , mental illnesses 3 , and genetic disorders 4 , 5 . A further objective would be predictive medicine where attempts are made to predict disease progression and response to intervention 6 .…”

Section: Introductionmentioning

confidence: 99%

Constrained neuro fuzzy inference methodology for explainable personalised modelling with applications on gene expression data

Singh

Doborjeh

et al. 2023

Sci Rep

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Interpretable machine learning models for gene expression datasets are important for understanding the decision-making process of a classifier and gaining insights on the underlying molecular processes of genetic conditions. Interpretable models can potentially support early diagnosis before full disease manifestation. This is particularly important yet, challenging for mental health. We hypothesise this is due to extreme heterogeneity issues which may be overcome and explained by personalised modelling techniques. Thus far, most machine learning methods applied to gene expression datasets, including deep neural networks, lack personalised interpretability. This paper proposes a new methodology named personalised constrained neuro fuzzy inference (PCNFI) for learning personalised rules from high dimensional datasets which are structurally and semantically interpretable. Case studies on two mental health related datasets (schizophrenia and bipolar disorders) have shown that the relatively short and simple personalised fuzzy rules provided enhanced interpretability as well as better classification performance compared to other commonly used machine learning methods. Performance test on a cancer dataset also showed that PCNFI matches previous benchmarks. Insights from our approach also indicated the importance of two genes (ATRX and TSPAN2) as possible biomarkers for early differentiation of ultra-high risk, bipolar and healthy individuals. These genes are linked to cognitive ability and impulsive behaviour. Our findings suggest a significant starting point for further research into the biological role of cognitive and impulsivity-related differences. With potential applications across bio-medical research, the proposed PCNFI method is promising for diagnosis, prognosis, and the design of personalised treatment plans for better outcomes in the future.

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Gene mutation detection for breast cancer disease: A review

Wisesty

Mengko

Purwarianti

2020

IOP Conf. Ser.: Mater. Sci. Eng.

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Breast cancer is one of the most common diseases suffered, especially by women, in the world, and about two billion new cases of patients with breast cancer in 2018. Therefore, it is very important to detect cancer early. Early detection of cancer can be done through the analysis of DNA abnormalities from blood cell samples, where the sampling does not require surgery, non-invasive and painless, and can reduce the sampling cost. DNA abnormalities can occur due to heredity or gene mutation. This paper presents a systematic review that includes an explanation of DNA sequences, gene mutations that occur in breast cancer, and bioinformatics techniques for detecting breast cancer. From several studies that have been conducted in the medical field there are mutations in the BRCA1, BRCA2, and PALB2 genes, where mutations in these genes can cause an increased risk of breast cancer. Other gene mutations associated with cancer risk are ATM, BARD1, CDH1, CHEK2, MRE11A, NBN, TP53, PTEN, RAD50, RECQL, RINT1. In bioinformatics, breast cancer detection based on DNA sequence data is carried out in three phases namely data mapping, feature extraction, and prediction / classification. The methods that can be used are Voss mapping and its variations for data mapping, statistical feature representation approach and Wavelet analysis for feature extraction, and regression approaches, probability models, Support Vector Machines, Neural Networks and Deep Learning for classification.

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Network-Based Method for Inferring Cancer Progression at the Pathway Level from Cross-Sectional Mutation Data

Cited by 9 publications

References 43 publications

MEXCOwalk: mutual exclusion and coverage based random walk to identify cancer modules

MEXCOwalk: mutual exclusion and coverage based random walk to identify cancer modules

Constrained neuro fuzzy inference methodology for explainable personalised modelling with applications on gene expression data

Gene mutation detection for breast cancer disease: A review

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