Digital Library[ Search Result ]
A Method for Cancer Prognosis Prediction Using Gene Embedding
http://doi.org/10.5626/JOK.2021.48.7.842
Identifying prognostic genes and using them to predict the prognosis of cancer patients can help provide them with more effective treatments. Many methods have been proposed to identify prognostic genes and predict cancer prognosis, and recent studies have focused on machine learning methods including deep learning. However, applying gene expression data to machine learning methods has the limitations of a small number of samples and a large number of genes. In this study, we additionally use a gene network to generate many random gene paths, which we used for training the model, thereby compensating for the small sample problem. We identified the prognostic genes and predicted the prognosis of patients using the gene expression data and gene networks for five cancer types and confirmed that the proposed method showed better predictive accuracy compared to other existing methods, and good performance on small sample data.
AttDRP: Attention Mechanism-based Model for Anti-Cancer Drug Response Prediction
Jonghwan Choi, Sangmin Seo, Sanghyun Park
http://doi.org/10.5626/JOK.2021.48.6.713
Resistance to anti-cancer drugs makes chemotherapy ineffective for cancer patients. Drug resistance is caused by genetic alterations in cancer cells. Many studies have investigated drug responses of diverse cancer cell lines to various anti-cancer drugs to understand drug response mechanisms. Existing studies have proposed machine learning models for drug response prediction to find effective anti-cancer drugs. However, currently there are no models to learn the relationship between anticancer drugs and genes to improve the prediction accuracy. In this paper, we proposed a predictive model AttDRP that could identify important genes associated with anti-cancer drugs and predict drug responses based on identified genes. AttDRP exhibited better predictive accuracy than existing models and we found that the attention scores of AttDRP could be effective tools to analyze molecular structures of anticancer drugs. We hope that our proposed method would contribute to the development of precision medicine for effective chemotherapy. Resistance to anti-cancer drugs makes chemotherapy ineffective for cancer patients. Drug resistance is caused by genetic alterations in cancer cells. Many studies have investigated drug responses of diverse cancer cell lines to various anti-cancer drugs to understand drug response mechanisms. Existing studies have proposed machine learning models for drug response prediction to find effective anti-cancer drugs. However, currently there are no models to learn the relationship between anticancer drugs and genes to improve the prediction accuracy. In this paper, we proposed a predictive model AttDRP that could identify important genes associated with anti-cancer drugs and predict drug responses based on identified genes. AttDRP exhibited better predictive accuracy than existing models and we found that the attention scores of AttDRP could be effective tools to analyze molecular structures of anticancer drugs.
Breast Cancer Subtype Classification Using Multi-omics Data Integration Based on Neural Network
Joungmin Choi, Jiyoung Lee, Jieun Kim, Jihyun Kim, Heejoon Chae
http://doi.org/10.5626/JOK.2020.47.9.835
Breast cancer is one of the highly heterogeneous diseases comprising multiple biological factors, causing multiple subtypes. Early diagnosis and accurate subtype prediction of breast cancer play a critical role in the prognosis of cancer and are crucial to providing appropriate treatment for each patient with different subtypes. To identify significant patterns from enormous volumes of genetic and epigenetic data, machine learning-based methods have been adopted to the breast cancer subtype classification. Recently, multi-omics data integration has attracted much attention as a promising approach in recognizing complex molecular mechanisms and providing a comprehensive view of patients. However, because of the characteristics of high dimensionality, multi-omics based approaches are limited in prediction accuracy. In this paper, we propose a neural network-based breast cancer subtype classification model using multi-omics data integration. The gene expression, DNA methylation, and miRNA omics dataset were integrated after preprocessing and the classification model was trained based on the neural network using the dataset. Our performance evaluation results showed that the proposed model outperforms all other methods, providing the highest classification accuracy of 90.45%. We expect this model to be useful in predicting the subtypes of breast cancer and improving patients’ prognosis.
Privacy-preserving Association Rule Mining Algorithm Based on FP-Growth in Cloud Computing Environment
JaeHwan Shin, Hyeong-Jin Kim, JaeWoo Chang, Young-Ho Song
http://doi.org/10.5626/JOK.2020.47.8.707
Recently, with the advancement of cloud computing technology, database owners can outsource their databases to the cloud for professional management of data at low cost. However, outsourcing the original database to the cloud server exposes sensitive information in the database, such as banking and medical treatment. In this paper, we propose a privacy-preserving association rule mining algorithm based on the FP-Growth in the cloud computing environment. To protect the sensitive information, the proposed algorithm encrypts the original data and the user"s queries with homomorphic encryption schemes that support specific operations on cipher-texts. To provide efficient query processing on cipher-texts, we propose a comparison operation protocol that compares ciphertexts without exposing the original data. Through the performance evaluation, the proposed algorithm shows approximately 68～123% performance improvement, compared to the existing algorithm.
A Robust Three-Factor User Authentication Scheme based on Elliptic Curve Cryptography and Fuzzy Extractor
Trung Thanh Ngo, Tae-Young Choe
http://doi.org/10.5626/JOK.2019.46.6.587
A three-factor user authentication is appropriate to ensure a high degree of authentication. Fan and Lin proposed a typical three-factor authentication scheme, which requires token, password, and fingerprint. The scheme does not allow authentication in the absence of any of the three factors. Unfortunately, Fan and Lin"s scheme is associated with security risks such as vulnerability to insider attacks, stolen-verifier attacks, and message modification attacks. Yeh et al. proposed a three-factor user authentication, which overcomes such pitfalls and improves security and performance using elliptic curve cryptography. We found that Yeh et al."s scheme is still vulnerable to user impersonation attacks and server masquerading attacks. We propose a robust three-factor authentication scheme entailing server smart cards, elliptic curve cryptography, and a fuzzy extractor that address the foregoing flaws and result in enhanced security. The proposed scheme is resistant to various attacks and improves system performance. BAN logic is used to prove that the scheme establishes a secure channel.
Research for Speed Improvement Method of Lightweight Block Cipher CHAM using NEON SIMD
Sujin Lee, Junyoung Kang, Dowon Hong, Changho Seo
http://doi.org/10.5626/JOK.2019.46.5.485
As embedded devices and IoT devices are being developed, lightweight block ciphers have been proposed to achieve confidentiality on low-end devices. Recently, a lightweight block cipher algorithm, called CHAM, with 4-branch Feistel structure was proposed in Korea. It is consists of CHAM-64/128, CHAM-128/128, and CHAM-128/256 depending on the size of plaintext and secret key. CHAM, which is based on ‘stateless on the fly’ key schedule and ARX operations, is efficient on embedded devices, especially on low-end devices. In this paper, we analyze the lightweight block cipher CHAM and study an optimization method on a high-end IoT device. We implemented a serial code by independently generating round keys and leveraging 4-branch Feistel structure, and optimized CHAM using NEON-SIMD on ARM Cortex-A53.
Practically Secure Key Exchange Scheme based on Neural Network
Sooyong Jeong, Dowon Hong, Changho Seo
http://doi.org/10.5626/JOK.2019.46.2.208
Key exchange is one of the major aspects in cryptography. Recently, compared to the existing key exchange schemes, more efficient key exchange schemes have been proposed based on neural network learning. After the first key exchange scheme based on neural network was proposed, various attack models have been suggested in security analysis. Hebbian learning rule is vulnerable to majority attack which is the most powerful attack. Anti Hebbian learning rule is secure against majority attack has a limitation in efficiency, so we can only use key exchange scheme based on random walk learning rule which is more secure and efficient than the others. However, if we use random walk learning rule, the efficiency which is advantage about neural cryptography is reduced than the other learning rules. In this paper we analyze random walk and neural cryptography, and we propose new learning rule which is more efficient than existing random walk learning rule. Also, we theoretically analyze about key exchange scheme which is uses new learning rule and verify the efficiency and security by implementing majority attack model.
Secure MQTT Protocol based on Attribute-Based Encryption Scheme
http://doi.org/10.5626/JOK.2018.45.3.195
Recently, with increasing scale of internet of Things (IoT), a large amount of data are generated and various services using such data are emerging. Therefore, a protocol suitable for IoT environment that can efficiently process / transmit big data is needed. MQTT is a lightweight messaging protocol for IoT environment. Although MQTT protocol can use TLS to provide security, it has a problem in that handshake and packet overhead will increase when TLS is used. Therefore, this paper proposed as Secure_MQTT protocol. It can provide stronger security by using lightweight encryption algorithm for MQTT protocol.
Identification of Heterogeneous Prognostic Genes and Prediction of Cancer Outcome using PageRank
http://doi.org/10.5626/JOK.2018.45.1.61
The identification of genes that contribute to the prediction of prognosis in patients with cancer is one of the challenges in providing appropriate therapies. To find the prognostic genes, several classification models using gene expression data have been proposed. However, the prediction accuracy of cancer prognosis is limited due to the heterogeneity of cancer. In this paper, we integrate microarray data with biological network data using a modified PageRank algorithm to identify prognostic genes. We also predict the prognosis of patients with 6 cancer types (including breast carcinoma) using the K-Nearest Neighbor algorithm. Before we apply the modified PageRank, we separate samples by K-Means clustering to address the heterogeneity of cancer. The proposed algorithm showed better performance than traditional algorithms for prognosis. We were also able to identify cluster-specific biological processes using GO enrichment analysis.
Efficient and Privacy-Preserving Near-Duplicate Detection in Cloud Computing
Changhee Hahn, Hyung June Shin, Junbeom Hur
http://doi.org/10.5626/JOK.2017.44.10.1112
As content providers further offload content-centric services to the cloud, data retrieval over the cloud typically results in many redundant items because there is a prevalent near-duplication of content on the Internet. Simply fetching all data from the cloud severely degrades efficiency in terms of resource utilization and bandwidth, and data can be encrypted by multiple content providers under different keys to preserve privacy. Thus, locating near-duplicate data in a privacy-preserving way is highly dependent on the ability to deduplicate redundant search results and returns best matches without decrypting data. To this end, we propose an efficient near-duplicate detection scheme for encrypted data in the cloud. Our scheme has the following benefits. First, a single query is enough to locate near-duplicate data even if they are encrypted under different keys of multiple content providers. Second, storage, computation and communication costs are alleviated compared to existing schemes, while achieving the same level of search accuracy. Third, scalability is significantly improved as a result of a novel and efficient two-round detection to locate near-duplicate candidates over large quantities of data in the cloud. An experimental analysis with real-world data demonstrates the applicability of the proposed scheme to a practical cloud system. Last, the proposed scheme is an average of 70.6% faster than an existing scheme.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr