Identification of candidate drugs using tensor-decomposition-based unsupervised feature extraction in integrated analysis of gene expression between diseases and DrugMatrix datasets

Taguchi, Y-h.

doi:10.1038/s41598-017-13003-0

Cited by 44 publications

(34 citation statements)

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“…In this paper, we predict other potential miRNA biomarkers in human sera and identify functions enriched in mRNAs targeted by these predicted miRNA biomarkers. We apply the recently proposed principal component analysis (PCA)-based unsupervised feature extraction (FE) [ 8 , 9 , 10 , 11 ] to human serum miRNAs and successfully predict miRNA biomarkers that can discriminate healthy controls from ALS patients.…”

Section: Introductionmentioning

confidence: 99%

Exploring microRNA Biomarker for Amyotrophic Lateral Sclerosis

Taguchi

Wang

2018

IJMS

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Amyotrophic lateral sclerosis (ALS) is among the severe neuro degenerative diseases that lack widely available effective treatments. As the disease progresses, patients lose the control of voluntary muscles. Although the neuronal degeneration is the cause of this disease, the failure mechanism is still unknown. In order to seek genetic mechanisms that initiate and progress ALS, the association of microRNA (miRNA) expression with this disease was considered. Serum miRNAs from healthy controls, sporadic ALS (sALS), familial ALS (fALS) and ALS mutation carriers were investigated. Principal component analysis (PCA)-based unsupervised feature extraction (FE) was applied to these serum miRNA profiles. As a result, we predict miRNAs that can discriminate patients from healthy controls with high accuracy. Thus, these miRNAs can be potential prognosis miRNA biomarkers for ALS.

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Section: Introductionmentioning

confidence: 99%

Exploring microRNA Biomarker for Amyotrophic Lateral Sclerosis

Taguchi

Wang

2018

IJMS

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“…The tensor is actually a multidimensional array [24], and we use the Euler script letters ( ) to represent the tensor. We refer to the tensor dimension and number of tensor dimension as modes and order, respectively [22].…”

Section: Definitions and Notationsmentioning

confidence: 99%

Learning Users’ Intention of Legal Consultation through Pattern-Oriented Tensor Decomposition with Bi-LSTM

Guo

Zhang

2019

Wireless Communications and Mobile Computing

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Online legal consultation plays an increasingly important role in the modern rule-of-law society. This study aims to understand the intention of legal consultation of users with different language expressions and legal knowledge background. A critical issue is a method through which users’ legal consultation data are classified and the feature of each category is extracted. Traditional classification methods rely considerably on lexical and syntactic features and frequently require strict sentence formatting, which eliminates substantial energy and may not be universally applicable. We aim to extract the patterns of users’ consultation on different categories, which minimally depend on lexical, syntax, and sentence formatting. However, research in this area has rarely been conducted in previous legal advisory service studies. In this study, a classification approach for multiclass users’ intention based on pattern-oriented tensor decomposition and Bi-LSTM is proposed, and each user’s legal consulting statement is expressed as a tensor. Moreover, we propose a pattern-oriented tensor decomposition method that can obtain a core tensor that approximates the patterns of users’ consultation. These patterns can improve the accuracy of classifying users’ intention of legal consultation. We use Bi-LSTM to automatically learn and optimize these patterns. Evidently, Bi-LSTM with a pattern-oriented tensor decomposition layer performs better than a recurrent neural network only. Results show that our method is more accurate than the previous work, and the factor matrix and core tensor calculated by the pattern-oriented tensor decomposition are interpretable.

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“…Various virtual screening techniques can be used to reduce the cost and time required to identify a potential drug [2]. As a computational method in drug discovery and virtual screening, clustering of chemical compounds by the similarity of their molecular fingerprints can be used to identify similar structures in a large set of similar data [38,41]. Their virtual screening performance is comparable to other,more complex, methods.…”

Section: Introductionmentioning

confidence: 99%

The rcdk and cluster R packages applied to drug candidate selection

et al. 2020

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The aim of this article is to show how thevpower of statistics and cheminformatics can be combined, in R, using two packages: rcdk and cluster.We describe the role of clustering methods for identifying similar structures in a group of 23 molecules according to their fingerprints. The most commonly used method is to group the molecules using a "score" obtained by measuring the average distance between them. This score reflects the similarity/non-similarity between compounds and helps us identify active or potentially toxic substances through predictive studies.Clustering is the process by which the common characteristics of a particular class of compounds are identified. For clustering applications, we are generally measure the molecular fingerprint similarity with the Tanimoto coefficient. Based on the molecular fingerprints, we calculated the molecular distances between the methotrexate molecule and the other 23 molecules in the group, and organized them into a matrix. According to the molecular distances and Ward 's method, the molecules were grouped into 3 clusters. We can presume structural similarity between the compounds and their locations in the cluster map. Because only 5 molecules were included in the methotrexate cluster, we considered that they might have similar properties and might be further tested as potential drug candidates.

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Identification of candidate drugs using tensor-decomposition-based unsupervised feature extraction in integrated analysis of gene expression between diseases and DrugMatrix datasets

Cited by 44 publications

References 50 publications

Exploring microRNA Biomarker for Amyotrophic Lateral Sclerosis

Exploring microRNA Biomarker for Amyotrophic Lateral Sclerosis

Learning Users’ Intention of Legal Consultation through Pattern-Oriented Tensor Decomposition with Bi-LSTM

The rcdk and cluster R packages applied to drug candidate selection

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