Integrative Analysis of Cancer Omics Data for Prognosis Modeling

Wang, Shuaichao; Wu, Mengyun; Ma, Shuangge

doi:10.3390/genes10080604

Cited by 6 publications

(2 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Independent component analysis (ICA), principal component analysis (PCA), and non-negative matrix factorization (NMF) are only some of the standard methods applied to the analysis of high-dimensional biological data [5]. The integration of multi-omics and/or multi-cancer layers is another crucial task in the interpretation of cancer data, especially regarding the characterization of drug sensitivity and the prognosis prediction of oncological patients [6]. Machine learning (ML) has been extensively used to address these challenges in cancer research [7,8].…”

Section: Introductionmentioning

confidence: 99%

Knowledge Generation with Rule Induction in Cancer Omics

Scala

Federico

Fortino

et al. 2019

IJMS

View full text Add to dashboard Cite

The explosion of omics data availability in cancer research has boosted the knowledge of the molecular basis of cancer, although the strategies for its definitive resolution are still not well established. The complexity of cancer biology, given by the high heterogeneity of cancer cells, leads to the development of pharmacoresistance for many patients, hampering the efficacy of therapeutic approaches. Machine learning techniques have been implemented to extract knowledge from cancer omics data in order to address fundamental issues in cancer research, as well as the classification of clinically relevant sub-groups of patients and for the identification of biomarkers for disease risk and prognosis. Rule induction algorithms are a group of pattern discovery approaches that represents discovered relationships in the form of human readable associative rules. The application of such techniques to the modern plethora of collected cancer omics data can effectively boost our understanding of cancer-related mechanisms. In fact, the capability of these methods to extract a huge amount of human readable knowledge will eventually help to uncover unknown relationships between molecular attributes and the malignant phenotype. In this review, we describe applications and strategies for the usage of rule induction approaches in cancer omics data analysis. In particular, we explore the canonical applications and the future challenges and opportunities posed by multi-omics integration problems.

show abstract

Section: Introductionmentioning

confidence: 99%

Knowledge Generation with Rule Induction in Cancer Omics

Scala

Federico

Fortino

et al. 2019

IJMS

View full text Add to dashboard Cite

show abstract

“…Integrated analysis can help improve statistical power by borrowing information across multiple datasets. In [3], Wang et al, introduce a novel penalized regression-based approach for the integrated analysis of gene expression data with survival outcomes. Novel shrinkage penalty functions are proposed to promote similarity among estimated coefficients from each cancer, and the coordinate descent (CD) algorithm is used for model fitting.…”

mentioning

confidence: 99%

Statistics in the Genomic Era

Jiang

2020

Genes

View full text Add to dashboard Cite

show abstract

A penalized integrative deep neural network for variable selection among multiple omics datasets

Li,

Ren,

et al. 2024

Quant. Biol.

View full text Add to dashboard Cite

Deep learning has been increasingly popular in omics data analysis. Recent works incorporating variable selection into deep learning have greatly enhanced the model’s interpretability. However, because deep learning desires a large sample size, the existing methods may result in uncertain findings when the dataset has a small sample size, commonly seen in omics data analysis. With the explosion and availability of omics data from multiple populations/studies, the existing methods naively pool them into one dataset to enhance the sample size while ignoring that variable structures can differ across datasets, which might lead to inaccurate variable selection results. We propose a penalized integrative deep neural network (PIN) to simultaneously select important variables from multiple datasets. PIN directly aggregates multiple datasets as input and considers both homogeneity and heterogeneity situations among multiple datasets in an integrative analysis framework. Results from extensive simulation studies and applications of PIN to gene expression datasets from elders with different cognitive statuses or ovarian cancer patients at different stages demonstrate that PIN outperforms existing methods with considerably improved performance among multiple datasets. The source code is freely available on Github (rucliyang/PINFunc). We speculate that the proposed PIN method will promote the identification of disease‐related important variables based on multiple studies/datasets from diverse origins.

show abstract

Integrative Analysis of Cancer Omics Data for Prognosis Modeling

Cited by 6 publications

References 43 publications

Knowledge Generation with Rule Induction in Cancer Omics

Knowledge Generation with Rule Induction in Cancer Omics

Statistics in the Genomic Era

A penalized integrative deep neural network for variable selection among multiple omics datasets

Contact Info

Product

Resources

About