A Selective Review of Multi-Level Omics Data Integration Using Variable Selection

Wu, Cen; Zhou, Fei; Ren, Jie; Li, Xiaoxi; Jiang, Yu; Ma, Shuangge

doi:10.3390/ht8010004

Cited by 151 publications

(132 citation statements)

References 129 publications

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“…Moreover, unlike other integrated modeling which combined features from different platforms either horizontally or hierarchically [7], one unique advantage of HetEnc is that it does not require multi-platform data for the test samples. HetEnc is designed to use multi-platform data to train two heteroencoding networks in the feature representation step, which is totally unsupervised; therefore, no labeling information (i.e., the endpoint) is needed for utilizing the multi-platform data.…”

Section: Discussionmentioning

confidence: 99%

“…In general, gene features were pre-filtered by their p-value (<0.05) and log2 fold-change (>1.5). Parameter K in KNN ranged in (1,3,5,7,9); kernel used in SVM is 'rbf'; and the other parameters are set as default. In training process, each model was trained based on randomly selected 70% of training data and its performance was evaluated on the remaining 30% of training data.…”

Section: Other Machine Learning Algorithmsmentioning

confidence: 99%

“…The difficulties mostly come from the inability to utilize the complicated, close linkages among features from different platforms efficiently. Several reviews have been conducted in integrative models [7,8]. Popular integrated analysis includes horizontal (or simultaneous) and vertical (or sequential) data integration [9], However, both of which assumes every sample, including the testing data has the data accessibility of all platforms.…”

Section: Introductionmentioning

confidence: 99%

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HetEnc: A Deep Learning Predictive Model for Multi-type Biological Dataset

Liu

2019

Preprint

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Background: Researchers today are generating unprecedented amounts of biological data. One trend in current biological research is integrated analysis with multi-platform data. Effective integration of multi-platform data into the solution of a single or multi-task classification problem; however, is critical and challenging. In this study, we proposed HetEnc, a novel deep learning-based approach, for information domain separation.Results: HetEnc includes both an unsupervised feature representation module and a supervised neural network module to handle multi-platform gene expression datasets. It first constructs three different encoding networks to represent the original gene expression data using high-level abstracted features. A six-layer fully-connected feed-forward neural network is then trained using these abstracted features for each targeted endpoint. We applied HetEnc to the SEQC neuroblastoma dataset to demonstrate that it outperforms other machine learning approaches. Although we used multi-platform data in feature abstraction and model training, HetEnc does not need multi-platform data for prediction, enabling a broader application of the trained model by reducing the cost of gene expression profiling for new samples to a single platform. Thus, HetEnc provides a new solution to integrated gene expression analysis, accelerating modern biological research.

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Section: Discussionmentioning

confidence: 99%

Section: Other Machine Learning Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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HetEnc: A Deep Learning Predictive Model for Multi-type Biological Dataset

Liu

2019

Preprint

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“…The lack of common methodologies and terminologies can transform this synergy into a further level of complexity in the process of data integration (51). As observed in (52,53), specific technological limits, noise levels and variability ranges affect the different omics, and thus confounding the underlying biological signals, yielding that really integrative analysis is still very rare, while different methods often discover different kinds of patterns, as evidenced by the lack of consistency in the published results, although efforts in this direction have started appearing (54,55).…”

Section: Background and Related Workmentioning

confidence: 99%

“…In the early-integration approach, also known as juxtaposition-based, the multi-omics datasets are first concatenated into one matrix. To deal with the high-dimensionality of the joint dataset, these methods generally adopt matrix factorization (68,53,55,52), statistical (46,69,70,59,57,44,71,72,73,55), and machine learning tools (74,73,55). Although the dimensionality reduction procedure is necessary and may improve the predictive performance, it can also cause the loss of key information (66).…”

Section: Background and Related Workmentioning

confidence: 99%

Integrative Network Fusion: a multi-omics approach in molecular profiling

Chierici

Bussola

Marcolini

et al. 2020

Preprint

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Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold crossvalidation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein abundances and copy number variants are used to predict estrogen receptor status (BRCA-ER, N=381) and breast invasive carcinoma subtypes (BRCA-subtypes, N=305), while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival (AML-OS, N=157; KIRC-OS, N=181). In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER (MCC: 0.83 vs 0.80; FS: 56 vs 1801) and BRCA-subtypes 1 Chierici et al. INF(0.84 vs 0.80; 302 vs 1801), improving KIRC-OS performance (0.38 vs 0.31; 111 vs 2319). INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizes 1 .

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Interpretability of bi‐level variable selection methods

Buch,

Schulz,

Schmidtmann

et al. 2024

Biometrical J

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Variable selection is usually performed to increase interpretability, as sparser models are easier to understand than full models. However, a focus on sparsity is not always suitable, for example, when features are related due to contextual similarities or high correlations. Here, it may be more appropriate to identify groups and their predictive members, a task that can be accomplished with bi‐level selection procedures. To investigate whether such techniques lead to increased interpretability, group exponential LASSO (GEL), sparse group LASSO (SGL), composite minimax concave penalty (cMCP), and least absolute shrinkage, and selection operator (LASSO) as reference methods were used to select predictors in time‐to‐event, regression, and classification tasks in bootstrap samples from a cohort of 1001 patients. Different groupings based on prior knowledge, correlation structure, and random assignment were compared in terms of selection relevance, group consistency, and collinearity tolerance. The results show that bi‐level selection methods are superior to LASSO in all criteria. The cMCP demonstrated superiority in selection relevance, while SGL was convincing in group consistency. An all‐round capacity was achieved by GEL: the approach jointly selected correlated and content‐related predictors while maintaining high selection relevance. This method seems recommendable when variables are grouped, and interpretation is of primary interest.

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A Selective Review of Multi-Level Omics Data Integration Using Variable Selection

Cited by 151 publications

References 129 publications

HetEnc: A Deep Learning Predictive Model for Multi-type Biological Dataset

HetEnc: A Deep Learning Predictive Model for Multi-type Biological Dataset

Integrative Network Fusion: a multi-omics approach in molecular profiling

Interpretability of bi‐level variable selection methods

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