A data imputation method for multivariate time series based on generative adversarial network

Missing value imputation affects the performance of machine learning: A review and analysis of the literature (2010–2021)

Sign language is the most important means of communication for deaf people. Given the lack of familiarity of non-deaf people with the language of deaf people, designing a translator system which facilitates the communication of deaf people with the surrounding environment seems to be necessary. The system of translating the sign language into spoken languages should be able to identify the gestures in sign language videos. Consequently, this study provides a system based on machine vision to recognize the signs in continuous Persian sign language video. This system generally consists of two main phases of sign words extraction and their classification. Several stages, including tracking and separating the sign words, are conducted in the sign word extraction phase. The most challenging part of this process is separation of sign words from video sequences. To do this, a new algorithm is presented which is capable of detecting accurate boundaries of words in the Persian sign language video. This algorithm decomposes sign language video into the sign words using motion and hand shape features, leading to more favorable results compared to the other methods presented in the literature. In the classification phase, separated words are classified and recognized using hidden Markov model and hybrid KNN-DTW algorithm, respectively. Due to the lack of proper database on Persian sign language, the authors prepared a database including several sentences and words performed by three signers. Simulation of proposed words boundary detection and classification algorithms on the above database led to the promising results. The results indicated an average rate of 93.73 % for accurate words boundary detection algorithm and the average rate of 92.4 and 92.3 % for words recognition using hands motion and shape features, respectively.

An algorithm on sign words extraction and recognition of continuous Persian sign language based on motion and shape features of hands

Microarray technology provides an opportunity for scientists to analyze thousands of gene expression profiles simultaneously. However, microarray gene expression data often contain multiple missing expression values due to many reasons. Effective methods for missing value imputation in gene expression data are needed since many algorithms for gene analysis require a complete matrix of gene array values. Several algorithms are proposed to handle this problem, but they have various limitations. In this paper, we develop a novel method to impute missing values in microarray time-series data combining k-nearest neighbor (KNN) and dynamic time warping (DTW). We also analyze and implement several variants of DTW to further improve the efficiency and accuracy of our method. Experimental results show that our method is more accurate compared with existing missing value imputation methods on real microarray time series datasets.

/pdf/knn-dtw-based-missing-value-imputation-for-microarray-time-391ug3lp27.pdf

KNN-DTW Based Missing Value Imputation for Microarray Time Series Data

For the real-world time series analysis, data missing is a ubiquitously existing problem due to anomalies during data collecting and storage. If not treated properly, this problem will seriously hinder the classification, regression, or related tasks. Existing methods for time series imputation either impose too strong assumptions on the distribution of missing data or cannot fully exploit, even simply ignore, the informative temporal dependencies and feature correlations across different time steps. In this article, inspired by the idea of conditional generative adversarial networks, we propose a generative adversarial learning framework for time series imputation under the condition of observed data (as well as the labels, if possible). In our model, we employ a modified bidirectional RNN structure as the generator G, which is aimed at generating the missing values by taking advantage of the temporal and nontemporal information extracted from the observed time series. The discriminator D is designed to distinguish whether each value in a time series is generated or not so that it can help the generator to make an adjustment toward a more authentic imputation result. For an empirical verification of our model, we conduct imputation and classification experiments on several real-world time series data sets. The experimental results show an eminent improvement compared with state-of-the-art baseline models. 

/pdf/adversarial-recurrent-time-series-imputation-1ttkea6y.pdf

Adversarial Recurrent Time Series Imputation

Microarray technology provides an opportunity for scientists to analyze thousands of gene expression profiles simultaneously. Time-series microarray data are gene expression values generated from microarray experiments within certain time intervals. Scientists can infer gene regulations in a biological system by judging whether two genes present similar gene expression values in microarray time-series data. Recently, a great many methods are widely applied on microarray time-series data to find out the similarity and the correlation degree among genes. Existing approaches including traditional Pearson coefficient correlation, Bayesian networks, clustering analysis, classification methods, and correlation analysis have individual disadvantages such as high computational complexity or they may be unsuitable for some microarray data. Traditional Pearson correlation coefficient is a numeric measuring method which gives novel effectiveness on two sets of numeric data. However, it is not suitable to be applied on microarray time-series data because of the existence of outliers among gene expression values. This paper presents a novel method of applying Pearson correlation coefficient along with an outlier filtering procedure on the widely-used microarray time-series datasets. Results show that the proposed method produces a better outcome compared with traditional Pearson correlation coefficient on the same dataset. Results show that the proposed method not only can find out certain more known regulatory gene pairs, but also keeps rational computational time.

Outlier Filtering for Identification of Gene Regulations in Microarray Time-Series Data

Selecting informative genes is the most important task for data analysis on microarray gene expression data. In this work, we aim at identifying regulatory gene pairs from microarray gene expression data. However, microarray data often contain multiple missing expression values. Missing value imputation is thus needed before further processing for regulatory gene pairs becomes possible. We develop a novel approach to first impute missing values in microarray time series data by combining k-Nearest Neighbour (KNN), Dynamic Time Warping (DTW) and Gene Ontology (GO). After missing values are imputed, we then perform gene regulation prediction based on our proposed DTW-GO distance measurement of gene pairs. Experimental results show that our approach is more accurate when compared with existing missing value imputation methods on real microarray data sets. Furthermore, our approach can also discover more regulatory gene pairs that are known in the literature than other methods.

Prediction of regulatory gene pairs using dynamic time warping and gene ontology

Microarray technology is a big step in bioinformatics. Hidden information within the large amounts of data provides scientists with molecular functions or essential biological meanings to study and analyze. However, these data often contain a certain portion of entities that are missing. Several methods to estimate these missing values are developed, but most of them are with disadvantages. In this paper, we propose a novel approach to deal with these missing values based on a practical similarity measurement between gene pairs. Our approach takes gene expression values and gene ontology (GO) information for genes into consideration. We implement our approach on a real microarray dataset and compare its imputation accuracy with other methods. Experimental results show that our approach can estimate missing values in microarray data effectively.

Imputing missing values in microarray data with ontology information

Selecting informative genes from microarray gene expression data is the most important task while performing data analysis on the large amount of data. Mining genes having regulatory relations within thousands of genes is essential. To fit this need, a number of methods were proposed from various points of view. However, most existing methods solely focus on gene expression values themselves without using any external information of genes. Gene Ontology (GO) provides biological information of genes or proteins involved. It utilizes a hierarchical structure to give additional biological information of genes as the aid for data analysis. In this paper, we first give a brief description about the GO structure and give a review of existing literatures that take GO into account. Subsequently, we propose a novel method to identify regulatory gene pairs in a real microarray dataset based on dynamic time warping (DTW) algorithm and GO. Finally, we summarize this paper with a discussion on how GO can be used to facilitate the analysis of microarray gene expression data.

Applying gene ontology to microarray gene expression data analysis

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Andy C. Yang

Author Tools

Chat about Author