Disease genes prediction by HMM based PU-learning using gene expression profiles

Nikdelfaz, Ozra; Jalili, Saeed

doi:10.1016/j.jbi.2018.03.006

Cited by 19 publications

(10 citation statements)

References 32 publications

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“…The proposed algorithm automatically determines agglomerative clusters and outperforms other benchmark techniques. Nikdelfaz and Jalili [36] proposed HMM driven semantic similarity identification technique between various genes using gene ontology and K-means. Samanta et al [43] proposed HMM based handwritten word segmentation script based on gaussian mixture model.…”

Section: Related Workmentioning

confidence: 99%

Investigating the Importance of Psychological and Environmental Factors for Improving Learner’s Performance Using Hidden Markov Model

et al. 2019

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In the proposed work, hidden Markov model (HMM) has been deployed to improve the learner's performance or grades on the basis of their psychological and environmental factors like connect/gather isolation, pleasure/comfort, depression, trust, anxiety, proper guidance, improper guidance, entertainment, and stress. The categorization of psychological and environmental factors has been done on the basis of two factors as positive and negative. The responsibility of the positive factor is to boost up learner's performance or grades, whereas negative factors reduce learning performance respectively. Finally, this paper addresses the application of HMM to determine the optimal sequence of states for different states as grades A, B, and C for different emission observations. The states identification leads to training the HMM model where optimal value of individual states computed using different observation sequences which determines the probability of state sequences. The probability of achieved optimal states is shown in different logical combinations where best state is searched among available different states using different search techniques. The computational results obtained after training are encouraging and useful. INDEX TERMS Hidden Markov model, psychological, environmental, negative, positive, validation.

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Section: Related Workmentioning

confidence: 99%

Investigating the Importance of Psychological and Environmental Factors for Improving Learner’s Performance Using Hidden Markov Model

et al. 2019

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“…Related work: Positive-and-unlabeled (PU) learning [6] is a promising novel machine learning problem that performs binary classification from positive labels and unlabeled samples. Many methods have been proposed for ensemble learning [8], Bayes classifier [9], and time series classification [10], and PU learning has been applied to several applications, particularly text analysis: opinion analysis [11], spam detection [12], and gene expression analysis [13]. These methods assume that positive samples are given with supervised labels, which is the original problem setup.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Body Hair Detection by Positive-Unlabeled Learning in Photoacoustic Image

Kikkawa¹,

Kajita²,

Imanishi³

et al. 2021

2021 43rd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Photoacoustic (PA) imaging is a new imaging technology that can non-invasively visualize blood vessels and body hair in 3D. It is useful in cosmetic surgery for detecting body hair and computing metrics such as the number and thicknesses of hairs. Previous supervised body hair detection methods often do not work if the imaging conditions change from training data. We propose an unsupervised hair detection method. Hair samples were automatically extracted from unlabeled samples using prior knowledge about spatial structure. If hair (positive) samples and unlabeled samples are obtained, Positive Unlabeled (PU) learning becomes possible. PU methods can learn a binary classifier from positive samples and unlabeled samples. The advantage of the proposed method is that it can estimate an appropriate decision boundary in accordance with the distribution of the test data. Experimental results using real PA data demonstrate that the proposed approach effectively detects body hairs.

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“…Conditional random field (CRF) and support vector machines (SVM) have been used to mine relationships [ 9 – 11 ]. In [ 12 ], a new semisupervised learning method based on hidden Markov models is proposed to extract the disease candidate genes from the human genome. This method predicts genes by positive-unlabeled learning (PU-Learning).…”

Section: Introductionmentioning

confidence: 99%

A semantic relationship mining method among disorders, genes, and drugs from different biomedical datasets

Zhang

et al. 2020

BMC Med Inform Decis Mak

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Background Semantic web technology has been applied widely in the biomedical informatics field. Large numbers of biomedical datasets are available online in the resource description framework (RDF) format. Semantic relationship mining among genes, disorders, and drugs is widely used in, for example, precision medicine and drug repositioning. However, most of the existing studies focused on a single dataset. It is not easy to find the most current relationships among disorder-gene-drug relationships since the relationships are distributed in heterogeneous datasets. How to mine their semantic relationships from different biomedical datasets is an important issue. Methods First, a variety of biomedical datasets were converted into RDF triple data; then, multisource biomedical datasets were integrated into a storage system using a data integration algorithm. Second, nine query patterns among genes, disorders, and drugs from different biomedical datasets were designed. Third, the gene-disorder-drug semantic relationship mining algorithm is presented. This algorithm can query the relationships among various entities from different datasets. Results and conclusions We focused on mining the putative and the most current disorder-gene-drug relationships about Parkinson’s disease (PD). The results demonstrate that our method has significant advantages in mining and integrating multisource heterogeneous biomedical datasets. Twenty-five new relationships among the genes, disorders, and drugs were mined from four different datasets. The query results showed that most of them came from different datasets. The precision of the method increased by 2.51% compared to that of the multisource linked open data fusion method presented in the 4th International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019). Moreover, the number of query results increased by 7.7%, and the number of correct queries increased by 9.5%.

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Disease genes prediction by HMM based PU-learning using gene expression profiles

Cited by 19 publications

References 32 publications

Investigating the Importance of Psychological and Environmental Factors for Improving Learner’s Performance Using Hidden Markov Model

Investigating the Importance of Psychological and Environmental Factors for Improving Learner’s Performance Using Hidden Markov Model

Unsupervised Body Hair Detection by Positive-Unlabeled Learning in Photoacoustic Image

A semantic relationship mining method among disorders, genes, and drugs from different biomedical datasets

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