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Biological Feature Selection and Disease Gene Identification using New Stepwise Random Forests

Hwang, Wook-Yeon
- Industrial Engineering and Management Systems
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- v.16 no.1
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- pp.64-79
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- 2017
Identifying disease genes from human genome is a critical task in biomedical research. Important biological features to distinguish the disease genes from the non-disease genes have been mainly selected based on traditional feature selection approaches. However, the traditional feature selection approaches unnecessarily consider many unimportant biological features. As a result, although some of the existing classification techniques have been applied to disease gene identification, the prediction performance was not satisfactory. A small set of the most important biological features can enhance the accuracy of disease gene identification, as well as provide potentially useful knowledge for biologists or clinicians, who can further investigate the selected biological features as well as the potential disease genes. In this paper, we propose a new stepwise random forests (SRF) approach for biological feature selection and disease gene identification. The SRF approach consists of two stages. In the first stage, only important biological features are iteratively selected in a forward selection manner based on one-dimensional random forest regression, where the updated residual vector is considered as the current response vector. We can then determine a small set of important biological features. In the second stage, random forests classification with regard to the selected biological features is applied to identify disease genes. Our extensive experiments show that the proposed SRF approach outperforms the existing feature selection and classification techniques in terms of biological feature selection and disease gene identification.
https://doi.org/10.7232/iems.2017.16.1.064 인용 PDF

Diagnosis of Parkinson's disease based on audio voice using wav2vec (Wav2vec을 이용한 오디오 음성 기반의 파킨슨병 진단)

Yoon, Hee-Jin
- Journal of Digital Convergence
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- v.19 no.12
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- pp.353-358
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- 2021
Parkinson's disease is the second most common degenerative brain disease after Alzheimer's in old age. Symptoms of Parkinson's disease are factors that reduce the quality of life in daily life, such as shaking hands, slowing behavior and cognitive function. Parkinson's disease that can slow the progression of the disease through early diagnosis. To diagnoze Parkinson's disease early, an algorithm was implemented to extract features using wav2vec and to diagnose the presence or absence of Parkinson's disease with deep learning(ANN). As a results of the experiment, the accuracy was 97.47%. It was better than the results of diagnosing Parkinson's disease using the existing neural network. The audio voice file could simply reduce the experiment process and obtain improved results.
https://doi.org/10.14400/JDC.2021.19.12.353 인용 PDF KSCI

Epigenetic Age Prediction of Alzheimer's Disease Patients Using the Aging Clock (노화 시계를 이용한 알츠하이머병 환자의 후성유전학적 연령 예측)

Jinyoung Kim;Gwang-Won Cho
- Journal of Integrative Natural Science
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- v.16 no.2
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- pp.61-67
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- 2023
Human body ages differently due to environmental, genetic and pathological factors. DNA methylation patterns also differs depending on various factors such as aging and several other diseases. The aging clock model, which uses these differences to predict age, analyzes DNA methylation patterns, recognizes age-specific patterns, predicts age, and grasps the speed and degree of aging. Aging occurs in everyone and causes various problems such as deterioration of physical ability and complications. Alzheimer's disease is a disease associated with aging and the most common brain degenerative disease. This disease causes various cognitive functions disabilities such as dementia and impaired judgment to motor functions, making daily life impossible. It has been reported that the incidence and progression of this disease increase with aging, and that increased phosphorylation of Aβ and tau proteins, which are overexpressed in this disease and accelerates epigenetic aging. It has also been reported that DNA methylation is significantly increased in the hippocampus and entorhinal cortex of Alzheimer's disease patients. Therefore, we calculated the biological age using the Epi clock, a pan-tissue aging clock model, and confirmed that the epigenetic age of patients suffering from Alzheimer's disease is lower than their actual age. Also, it was confirmed to slow down aging.
https://doi.org/10.13160/ricns.2023.16.2.61 인용 PDF