Cancer Diagnosis Through IsomiR Expression with Machine Learning Method

Liao, Zhijun; Li, Dapeng; Wang, Xinrui; Liu, Lisheng; Zou, Quan

doi:10.2174/1574893611666160609081155

Cited by 121 publications

(69 citation statements)

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“…Liao et al [22] used a random forest classification algorithm to classify six cancers by extracting five features of the isomiRs and achieved an accuracy of greater than 0.84. The classification accuracy in Liao's study for BRCA and STAD is lower than that in our study, while the accuracy for LUAD is higher than that of our method.…”

Section: Comparison With the State Of The Artmentioning

confidence: 99%

“…Methods Result AUC ACC Pre BRCA Liao [22] N/A 0.87 N/A Guo [20] N/A N/A 0.88 Telonis [41] N/A 0.91 N/A Li [42] 0.89 N/A N/A Sherafatian [43] N/A 0.89 0.90 MLW-gcForest 0.98 0.91 0.92 LUAD Liao [22] N/A 0.91 N/A Guo [20] N/A N/A 0.88 Telonis [41] N/A 0.86 N/A Podolsky [44] 0.92 N/A N/A Cai [19] N/A 0.85 0.86 MLW-gcForest 0.92 0.87 0.86 LIHC Guo [20] N/A N/A 0.82 Telonis [41] N/A 0.90 N/A Tan [45] 0.77 0.83 N/A Friemel [46] N/A 0.87 N/A MLW-gcForest 0.91 0.87 0.85 GBM Guo [20] N/A N/A 0.78 Lu [21] 0.92 0.88 N/A Ryu [47] 0.83 0.80 N/A MLW-gcForest 0.87 0.89 0.86 STAD Liao [22] N/A 0.84 N/A Telonis [41] N/A 0.85 N/A MLW-gcForest 0.88 0.87 0.87…”

Section: Cancermentioning

confidence: 99%

“…Lu et al [21] developed a three-level machine-learning model to identify glioma subtypes. Liao et al [22] used a random forest method based on isomiR data to classify six cancers. Xiao et al [23] developed a deep learning-based multi-model ensemble method based on Ribonucleic Acid (RNA)-seq data to identify three kinds of cancers.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MLW-gcForest: A Multi-Weighted gcForest Model for Cancer Subtype Classification by Methylation Data

et al. 2019

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Effective cancer treatment requires a clear subtype. Due to the small sample size, high dimensionality, and class imbalances of cancer gene data, classifying cancer subtypes by traditional machine learning methods remains challenging. The gcForest algorithm is a combination of machine learning methods and a deep neural network and has been indicated to achieve better classification of small samples of data. However, the gcForest algorithm still faces many challenges when this method is applied to the classification of cancer subtypes. In this paper, we propose an improved gcForest algorithm (MLW-gcForest) to study the applicability of this method to the small sample sizes, high dimensionality, and class imbalances of genetic data. The main contributions of this algorithm are as follows: (1) Different weights are assigned to different random forests according to the classification ability of the forests. (2) We propose a sorting optimization algorithm that assigns different weights to the feature vectors generated under different sliding windows. The MLW-gcForest model is trained on the methylation data of five data sets from the cancer genome atlas (TCGA). The experimental results show that the MLW-gcForest algorithm achieves high accuracy and area under curve (AUC) values for the classification of cancer subtypes compared with those of traditional machine learning methods and state of the art methods. The results also show that methylation data can be effectively used to diagnose cancer.

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Section: Comparison With the State Of The Artmentioning

confidence: 99%

Section: Cancermentioning

confidence: 99%

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MLW-gcForest: A Multi-Weighted gcForest Model for Cancer Subtype Classification by Methylation Data

et al. 2019

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“…Figure 1 provides the framework of our proposed method, STAM. Here, we took drug trichostatin A (TSA) [31] and three diseases it treated, leukemia, breast cancer and prostate cancer [32][33][34][35][36], as a case for studying. Finally, we identified two drug-target modules of TSA (M17, M18) as the potential treatment patterns of TSA.…”

Section: Introductionmentioning

confidence: 99%

Studying the drug treatment pattern based on the action of drug and multi-layer network model

Shi

Zou³

et al. 2019

Preprint

Self Cite

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Objectives:A drug can treat multiple diseases, indicating that the treatment of the drug has certain patterns. In this paper, we studied the treatment pattern of drugs from a new perspective based on the action of drug and multi-layer network model (STAM).Diseases affect the gene expression in related tissues and each disease corresponds to a tissue-specific protein-protein interaction (TSPPI) network. Therefore, a drug is associated with a multi-layer TSPPI network associated with diseases it treats. Single tissue-specific PPI network cannot consider all disease-related information, leading to find the potential treatment pattern of drugs difficultly. Research on multi-layer networks can effectively solve this disadvantage. Furthermore, proteins usually interact with other proteins in PPI to achieve specific functions, such as causing disease. Hence, studying the drug treatment patterns is equivalent to study common module structures in the multi-layer TSPPI network corresponding to drug-related diseases. Knowing the treatment patterns of the drug can help to understand the action mechanisms of the drug and to identify new indications of the drug. Methods:In this paper, we were based on the action of drug and multi-layer network model to study the treatment patterns of drugs. We named our method as STAM. As a case of our proposed method STAM, we focused on a study to trichostatin A (TSA) and three diseases it treats: leukemia, breast cancer, and prostate cancer. Based on the therapeutic effects of TSA on various diseases, we constructed a tissue-specific protein-protein interaction (TSPPI) network and applied a multi-layer network module mining algorithm to obtain candidate drug-target modules. Then, using the genes affected by TSA and related to the three diseases, we employed Gene Ontology (GO), the modules' significance, co-expression network and literatures to filter and analyze the identified drug-target modules. Finally, two modules (named as M17 and M18) were preserved as the potential treatment patterns of TSA. Results:The processed results based on the above framework STAM demonstrated that M17 and M18 had strong potential to be the treatment patterns of TSA. Through the analysis of the significance, composition and functions of the selected drug-target modules, we validated the feasibility and rationality of our proposed method STAM for identifying the drug treatment pattern.Conclusion: This paper studied the drug treatment pattern from a new perspective.The new method STAM used a multi-layer network model, which overcame the shortcomings of the single-layer network, and combined the action of drug. Research on drug treatment model provides new research ideas for disease treatment.

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“…Although reliable, experimental based methods are generally time-consuming and labor-intensive[8]. With the increasing amount of available biological data, a great number of computational models have been developed by taking advantage of multiple data sources to effectively and efficiently predict associations between miRNAs and diseases[9–11]. Under the assumption that miRNAs with similar functions tend to be associated to phenotypically similar diseases[12,13].…”

Section: Introductionmentioning

confidence: 99%

GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods

Liang

Xiao

et al. 2018

RNA Biology

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Recently, increasing studies have shown that miRNAs are involved in the development and progression of various complex diseases. Consequently, predicting potential miRNA-disease associations makes an important contribution to understanding the pathogenesis of diseases, developing new drugs as well as designing individualized diagnostic and therapeutic approaches for different human diseases. Nonetheless, the inherent noise and incompleteness in the existing biological datasets have limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA-disease association prediction based on global linear neighborhoods (GLNMDA). Specifically, our method obtains a new miRNA/disease similarity matrix by linearly reconstructing each miRNA/disease according to the known experimentally verified miRNA-disease associations. We then adopt label propagation to infer the potential associations between miRNAs and diseases. As a result, GLNMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.867 and 0.929, respectively) and 5-fold cross validation (average AUC of 0.926). Case studies on five common human diseases further confirmed the utility of our method in discovering latent miRNA-disease pairs. Taken together, GLNMDA could serve as a reliable computational tool for miRNA-disease association prediction.

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Cancer Diagnosis Through IsomiR Expression with Machine Learning Method

Cited by 121 publications

References 0 publications

MLW-gcForest: A Multi-Weighted gcForest Model for Cancer Subtype Classification by Methylation Data

MLW-gcForest: A Multi-Weighted gcForest Model for Cancer Subtype Classification by Methylation Data

Studying the drug treatment pattern based on the action of drug and multi-layer network model

GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods

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