Nonuniformly Sampled Data Processing Using LSTM Networks

Sahin, Safa Onur; Kozat, Süleyman S.

doi:10.1109/tnnls.2018.2869822

Cited by 48 publications

(36 citation statements)

References 19 publications

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“…In our study, together with the 50 flanking bases on both sides, there were theoretically 6×4 100 possible classes, making it nearly impossible to analyze such classes with classical statistical methods. LSTM is a machine learning approach that is good at extracting the features of long sequences [28]. This approach provided us with a method for extracting the features of mutated sequences across a wider spatial scope.…”

Section: Discussionmentioning

confidence: 99%

Clinical Feature-Related Single-Base Substitution Sequence Signatures Identified with an Unsupervised Machine Learning Approach

Hongchen

Zhang

et al. 2021

Preprint

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Background: Mutation processes leave different signatures in genes. For single-base substitutions, previous studies have suggested that mutation signatures are not only reflected in mutation bases but also in neighboring bases. However, because of the lack of a method to identify features of long sequences next to mutation bases, the understanding of how flanking sequences influence mutation signatures is limited.Methods: We constructed a long short-term memory – self organizing map (LSTM-SOM) unsupervised neural network. By extracting mutated sequence features via LSTM and clustering similar features with the SOM, single-base substitutions in The Cancer Genome Atlas database were clustered according to both their mutation site and flanking sequences. The relationship between mutation sequence signatures and clinical features was then analyzed. Finally, we clustered patients into different classes according to the composition of the mutation sequence signatures by the K-means method and then studied the differences in clinical features and survival between classes.Results: Ten classes of mutant sequence signatures (mutation blots, MBs) were obtained from 2,141,527 single-base substitutions via LSTM-SOM machine learning approach. Different features in mutation bases and flanking sequences were revealed among MBs. MBs reflect both the site and pathological features of cancers. MBs were related to clinical features, including age, gender, and cancer stage. The class of an MB in a given gene was associated with survival. Finally, patients were clustered into 7 classes according to the MB composition. Significant differences in survival and clinical features were observed among different patient classes.Conclusions: We provided a method for analyzing the characteristics of mutant sequences. Result of this study showed that flanking sequences, together with mutation bases, shape the signatures of SBSs. MBs were shown related to clinical features and survival of cancer patients. Composition of MBs is a feasible predictive factor of clinical prognosis. Further study of the mechanism of MBs related to cancer characteristics is suggested.

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Section: Discussionmentioning

confidence: 99%

Clinical Feature-Related Single-Base Substitution Sequence Signatures Identified with an Unsupervised Machine Learning Approach

Hongchen

Zhang

et al. 2021

Preprint

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“…A variety of mutation signatures that may be related to the biology and etiology of cancer have been identified 2,8,[15][16][17][18][19][20][21][31][32][33][34] . LSTM is a machine learning approach that is good at extracting the features of long sequences 35 . This approach provided us with a method for extracting the features of mutated sequences across a wider spatial scope.…”

Section: Discussionmentioning

confidence: 99%

Characteristic of mutant sequences in human cancers

Hongchen¹,

Liu²,

Zhang³

et al. 2020

Preprint

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Mutation processes leave different signatures in genes. Previous studies suggested that both the mutated and flanking bases influence somatic mutation characteristics. However, the understanding of how flanking sequences influence somatic mutation characteristics is limited. Here, we constructed A long short-term memory – self organizing map (LSTM-SOM) unsupervised neural network. By extracting mutated sequence features via LSTM and clustering similar features with SOM, we obtained 10 classes of mutant sequences (named mutation blots, MBs) from 2,141,527 somatic mutations in The Cancer Genome Atlas (TCGA) database. Differences features were revealed among MBs. MBs were related to clinical features, including age, sex, and cancer stage. Different kinds of MBs for specific genes may affect patient survival. Finally, we clustered the patients into 7 classes by MB composition. Significant differences in survival and clinical features were observed among different patient classes. This study provides a novel method for understanding mutant sequences and revealing the extensive relationships among mutant sequences, clinical features, and cancer patient survival.

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“…LSTM provided us with a method for extracting the features of mutated sequences across a wider spatial scope 36 . A follow-up SOM method can then be used to discover internal relationships between the extracted features and ultimately obtain different categories of mutant sequences.…”

Section: Discussionmentioning

confidence: 99%

Insight of characteristic of mutation sequences in human cancers via an unsupervised neural network approach.

Hongchen

Zhang

et al. 2020

Preprint

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Background: Mutation processes leave different signatures in genes. Previous studies have suggested that both the mutated and flanking bases influence somatic mutation characteristics. However, the understanding of how flanking sequences influence somatic mutation characteristics is limited.Materials and methods: We constructed a long short-term memory – self organizing map (LSTM-SOM) unsupervised neural network. By extracting mutated sequence features via LSTM and clustering similar features with SOM, somatic mutations in The Cancer Genome Atlas database were clustered according to their mutation type and flanking sequences. The relationship between MB and cancer characteristics was then analyzed. At last, we clustered the patients into different classes according to the composition of MB by K-means method, and then studied the differences in clinical features and survival between classes.Results: Ten classes of mutant sequences (named mutation blots, MBs) were obtained from 2,141,527 somatic mutations. Different features in mutation bases and flanking sequences were revealed among MBs. MB reflect both the site and pathological features of cancers. MBs were related to clinical features, including age, sex, and cancer stage. Class of MB in a given gene is associated with survival. Finally, patients were clustered into 7 classes according to MB composition. Significant differences in survival and clinical features were observed among different patient classes.Conclusions: Our study provides a novel method for analyzing the information of mutant sequences and reveals the extensive relationships among mutant sequences, clinical features, and cancer patient survival.

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Nonuniformly Sampled Data Processing Using LSTM Networks

Cited by 48 publications

References 19 publications

Clinical Feature-Related Single-Base Substitution Sequence Signatures Identified with an Unsupervised Machine Learning Approach

Clinical Feature-Related Single-Base Substitution Sequence Signatures Identified with an Unsupervised Machine Learning Approach

Characteristic of mutant sequences in human cancers

Insight of characteristic of mutation sequences in human cancers via an unsupervised neural network approach.

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Nonuniformly Sampled Data Processing Using LSTM Networks

Cited by 48 publications

References 19 publications

Clinical Feature-Related Single-Base Substitution Sequence Signatures Identified with an Unsupervised Machine Learning Approach

Clinical Feature-Related Single-Base Substitution Sequence Signatures Identified with an Unsupervised Machine Learning Approach

Characteristic of mutant sequences&nbsp;in human cancers

Insight of characteristic of mutation sequences in human cancers via an unsupervised neural network approach.

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Characteristic of mutant sequences in human cancers