• Title/Summary/Keyword: Gene Expression

Search Result 4,665, Processing Time 0.163 seconds

Analysis and Subclass Classification of Microarray Gene Expression Data Using Computational Biology (전산생물학을 이용한 마이크로어레이의 유전자 발현 데이터 분석 및 유형 분류 기법)

  • Yoo, Chang-Kyoo;Lee, Min-Young;Kim, Young-Hwang;Lee, In-Beum
    • Journal of Institute of Control, Robotics and Systems
    • /
    • v.11 no.10
    • /
    • pp.830-836
    • /
    • 2005
  • Application of microarray technologies which monitor simultaneously the expression pattern of thousands of individual genes in different biological systems results in a tremendous increase of the amount of available gene expression data and have provided new insights into gene expression during drug development, within disease processes, and across species. There is a great need of data mining methods allowing straightforward interpretation, visualization and analysis of the relevant information contained in gene expression profiles. Specially, classifying biological samples into known classes or phenotypes is an important practical application for microarray gene expression profiles. Gene expression profiles obtained from tissue samples of patients thus allowcancer classification. In this research, molecular classification of microarray gene expression data is applied for multi-class cancer using computational biology such gene selection, principal component analysis and fuzzy clustering. The proposed method was applied to microarray data from leukemia patients; specifically, it was used to interpret the gene expression pattern and analyze the leukemia subtype whose expression profiles correlated with four cases of acute leukemia gene expression. A basic understanding of the microarray data analysis is also introduced.

Effect of Transposable Element Insertion on Gene Expression (Transposable Element 삽입의 유전자 발현에 미치는 영향)

  • 김화영
    • Proceedings of the Botanical Society of Korea Conference
    • /
    • /
    • pp.349-356
    • /
    • 1987
  • Insertions of transposable elements in or near a structural gene give rise to null phenotypes, reduced levels of gene expression, or alteration on the tissue-specific pattern of gene expression. Null phenotypes often result from insertions in exons. Reduced levels of gene expression results from insertions in various regions such as promoter region, 5' non-translated region, exon and intron. The maize allele of Adh1-3F1124 is an example of alteration in the tissue-specific patetern of gene expression. Adh1-3F1124 contains a Mu element inserted 31 bp 5' to the transcriptional start site of the wild-type Adh1 activity in seeds and anaerobically-treated seedlings but normal levels in the pollen. Upon the insertion of a transposable element a certain number of host DNA sequences at the insertion site is duplcated. When transposable elements excise, all element sequences are deleted. However, the duplicated host sequences may be left intact or deleted to various extents. This results in null phenotypes, restoration of original levels of gene expression, or altered levels of gene expression. On the basis of effects of transposable-element insertions or excisions on gene expression, the usefulness of transposable ellements for studies on gene expression is discussed.

  • PDF

Use of a Combined Gene Expression Profile in Implementing a Drug Sensitivity Predictive Model for Breast Cancer

  • Zhang, Xianglan;Cha, In-Ho;Kim, Ki-Yeol
    • Cancer Research and Treatment
    • /
    • v.49 no.1
    • /
    • pp.116-128
    • /
    • 2017
  • Purpose Chemotherapy targets all rapidly growing cells, not only cancer cells, and thus is often associated with unpleasant side effects. Therefore, examination of the chemosensitivity based on genotypes is needed in order to reduce the side effects. Materials and Methods Various computational approaches have been proposed for predicting chemosensitivity based on gene expression profiles. A linear regression model can be used to predict the response of cancer cells to chemotherapeutic drugs, based on genomic features of the cells, and appropriate sample size for this method depends on the number of predictors. We used principal component analysis and identified a combined gene expression profile to reduce the number of predictors Results The coefficients of determinanation ($R^2$) of prediction models with combined gene expression and several independent gene expressions were similar. Corresponding F values, which represent model significances were improved by use of a combined gene expression profile, indicating that the use of a combined gene expression profile is helpful in predicting drug sensitivity. Even better, a prediction model can be used even with small samples because of the reduced number of predictors. Conclusion Combined gene expression analysis is expected to contribute to more personalized management of breast cancer cases by enabling more effective targeting of existing therapies. This procedure for identifying a cell-type-specific gene expression profile can be extended to other chemotherapeutic treatments and many other heterogeneous cancer types.

Nutritional and Tissue Specificity of IGF-I and IGFBP-2 Gene Expression in Growing Chickens - A Review -

  • Kita, K.;Nagao, K.;Okumura, J.
    • Asian-Australasian Journal of Animal Sciences
    • /
    • v.18 no.5
    • /
    • pp.747-754
    • /
    • 2005
  • Nutritional regulation of gene expression associated with growth and feeding behavior in avian species can become an important technique to improve poultry production according to the supply of nutrients in the diet. Insulin-like growth factor-I (IGF-I) found in chickens has been characterized to be a 70 amino acid polypeptide and plays an important role in growth and metabolism. Although it is been well known that IGF-I is highly associated with embryonic development and post-hatching growth, changes in the distribution of IGF-I gene expression throughout early- to late-embryogenesis have not been studied so far. We revealed that the developmental pattern of IGF-I gene expression during embryogenesis differed among various tissues. No bands of IGF-I mRNA were detected in embryonic liver at 7 days of incubation, and thereafter the amount of hepatic IGF-I mRNA was increased from 14 to 20 days of incubation. In eyes, a peak in IGF-I mRNA levels occurred at mid-embryogenesis, but by contrast, IGF-I mRNA was barely detectable in the heart throughout all incubation periods. In the muscle, no significant difference in IGF-I gene expression was observed during different stages of embryogenesis. After hatching, hepatic IGF-I gene expression as well as plasma IGF-I concentration increases rapidly with age, reaches a peak before sexual maturity, and then declines. The IGF-I gene expression is very sensitive to changes in nutritional conditions. Food-restriction and fasting decreased hepatic IGF-I gene expression and refeeding restored IGF-I gene expression to the level of fed chickens. Dietary protein is also a very strong factor in changing hepatic IGF-I gene expression. Refeeding with dietary protein alone successfully restored hepatic IGF-I gene expression of fasted chickens to the level of fed controls. In most circumstances, IGF-I makes a complex with specific high-affinity IGF-binding proteins (IGFBPs). So far, four different IGFBPs have been identified in avian species and the major IGFBP in chicken plasma has been reported to be IGFBP-2. We studied the relationship between nutritional status and IGFBP-2 gene expression in various tissues of young chickens. In the liver of fed chickens, almost no IGFBP-2 mRNA was detected. However, fasting markedly increased hepatic IGFBP-2 gene expression, and the level was reduced after refeeding. In the gizzard of well-fed young chickens, IGFBP-2 gene expression was detected and fasting significantly elevated gizzard IGFBP-2 mRNA levels to about double that of fed controls. After refeeding, gizzard IGFBP-2 gene expression decreased similar to hepatic IGFBP-2 gene expression. In the brain, IGFBP-2 mRNA was observed in fed chickens and had significantly decreased by fasting. In the kidney, IGFBP-2 gene expression was observed but not influenced by fasting and refeeding. Recently, we have demonstrated in vivo that gizzard and hepatic IGFBP-2 gene expression in fasted chickens was rapidly reduced by intravenous administration of insulin, as indicated that in young chickens the reduction in gizzard and hepatic IGFBP-2 gene expression in vivo stimulated by malnutrition may be, in part, regulated by means of the increase in plasma insulin concentration via an insulin-response element. The influence of dietary protein source (isolated soybean protein vs. casein) and the supplementation of essential amino acids on gizzard IGFBP-2 gene expression was examined. In both soybean protein and casein diet groups, the deficiency of essential amino acids stimulated chickens to increase gizzard IGFBP-2 gene expression. Although amino acid supplementation of a soybean protein diet significantly decreased gizzard IGFBP-2 mRNA levels, a similar reduction was not observed in chickens fed a casein diet supplemented with amino acids. This overview of nutritional regulation of IGF-I and IGFBP-2 gene expression in young chickens would serve for the establishment of the supply of nutrients to diets to improve poultry production.

Enhancing Gene Expression Classification of Support Vector Machines with Generative Adversarial Networks

  • Huynh, Phuoc-Hai;Nguyen, Van Hoa;Do, Thanh-Nghi
    • Journal of information and communication convergence engineering
    • /
    • v.17 no.1
    • /
    • pp.14-20
    • /
    • 2019
  • Currently, microarray gene expression data take advantage of the sufficient classification of cancers, which addresses the problems relating to cancer causes and treatment regimens. However, the sample size of gene expression data is often restricted, because the price of microarray technology on studies in humans is high. We propose enhancing the gene expression classification of support vector machines with generative adversarial networks (GAN-SVMs). A GAN that generates new data from original training datasets was implemented. The GAN was used in conjunction with nonlinear SVMs that efficiently classify gene expression data. Numerical test results on 20 low-sample-size and very high-dimensional microarray gene expression datasets from the Kent Ridge Biomedical and Array Expression repositories indicate that the model is more accurate than state-of-the-art classifying models.

Reverting Gene Expression Pattern of Cancer into Normal-Like Using Cycle-Consistent Adversarial Network

  • Lee, Chan-hee;Ahn, TaeJin
    • International Journal of Advanced Culture Technology
    • /
    • v.6 no.4
    • /
    • pp.275-283
    • /
    • 2018
  • Cancer show distinct pattern of gene expression when it is compared to normal. This difference results malignant characteristic of cancer. Many cancer drugs are targeting this difference so that it can selectively kill cancer cells. One of the recent demand for personalized treating cancer is retrieving normal tissue from a patient so that the gene expression difference between cancer and normal be assessed. However, in most clinical situation it is hard to retrieve normal tissue from a patient. This is because biopsy of normal tissues may cause damage to the organ function or a risk of infection or side effect what a patient to take. Thus, there is a challenge to estimate normal cell's gene expression where cancers are originated from without taking additional biopsy. In this paper, we propose in-silico based prediction of normal cell's gene expression from gene expression data of a tumor sample. We call this challenge as reverting the cancer into normal. We divided this challenge into two parts. The first part is making a generator that is able to fool a pretrained discriminator. Pretrained discriminator is from the training of public data (9,601 cancers, 7,240 normals) which shows 0.997 of accuracy to discriminate if a given gene expression pattern is cancer or normal. Deceiving this pretrained discriminator means our method is capable of generating very normal-like gene expression data. The second part of the challenge is to address whether generated normal is similar to true reverse form of the input cancer data. We used, cycle-consistent adversarial networks to approach our challenges, since this network is capable of translating one domain to the other while maintaining original domain's feature and at the same time adding the new domain's feature. We evaluated that, if we put cancer data into a cycle-consistent adversarial network, it could retain most of the information from the input (cancer) and at the same time change the data into normal. We also evaluated if this generated gene expression of normal tissue would be the biological reverse form of the gene expression of cancer used as an input.