• KSBB Journal
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• v.26 no.2
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• pp.131-138
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• 2011

Some computational approaches are needed for clarifying RNAi sequences, because it takes much time and endeavor that almost of RNAi sequences are verified by experimental data. Incorrectness of RNAi mechanism and other unaware factors in organism system are frequently faced with questions regarding potential use of RNAi as therapeutic applications. Our massive parallelized pair alignment scoring between dsRNA in Genebank and expressed sequence tags (ESTs) in Caenorhabditis elegans Genome Sequencing Projects revealed that this provides a useful tool for the prediction of RNAi induced cosuppression details for practical use. This pair alignment scoring method using high performance computing exhibited some possibility that numerous unwanted gene silencing and cosuppression exist even at high matching scores each other. The classifying the relative higher matching score of them based on GO (Gene Ontology) system could present mapping dsRNA of C. elegans and functional roles in an applied system. Our prediction also exhibited that more than 78% of the predicted co-suppressible genes are located in the ribosomal spot of C. elegans.

• Proceedings of the Zoological Society Korea Conference
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• 1995.10b
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• pp.285.1-285
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• 1995

No Abstract, See Full Text

• Interdisciplinary Bio Central
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• v.4 no.4
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• pp.11.1-11.8
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• 2012

Genes that are indispensable for survival are referred to as essential gene. Due to the momentous significance of these genes for cellular activity they can be selected potentially as drug targets. Here in this study, an essential gene for Streptococcus suis was predicted using coherent statistical analysis and powerful genome comparison computational method. At first the whole genome protein scatter plot was generated and subsequently, on the basis of statistical significance, a reference genome was chosen. The parameters set forth for selecting the reference genome was that the genome of the query (Streptococcus suis) and subject must fall in the same genus and yet they must vary to a good degree. Streptococcus pneumoniae was found to be suitable as the reference genome. A whole genome comparison was performed for the reference (Streptococcus pneumoniae) and the query genome (Streptococcus suis) and 14 conserved proteins from them were subjected to a screen for potential essential gene property. Among those 14 only one essential gene was found to be with impressive similarity score between reference and query. The essential gene encodes for a type of 'Clp protease'. Clp proteases play major roles in degrading misfolded proteins. Results found here should help formulating a drug against Strptococcus suis which is responsible for mild to severe clinical conditions in human. However, like many other computational studies, the study has to be validated furthermore through in vitro assays for concrete proof.

• Tunnel and Underground Space
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• v.28 no.6
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• pp.651-669
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• 2018

This study suggests the prediction model to estimate the specific energy of a pick cutter using a gene expression programming (GEP) and particle swarm optimization (PSO). Estimating the performance of mechanical excavators is of crucial importance in early design stage of tunnelling projects, and the specific energy (SE) based approach serves as a standard performance prediction procedure that is applicable to all excavation machines. The purpose of this research, is to investigate the relationship between UCS and BTS, penetration depth, cut spacing, and SE. A total of 46 full-scale linear cutting test results using pick cutters and different values of depth of cut and cut spacing on various rock types was collected from the previous study for the analysis. The Mean Squared Error (MSE) associated with the conventional Multiple Linear Regression (MLR) method is more than two times larger than the MSE generated by GEP-PSO algorithm. The $R^2$ value associated with the GEP-PSO algorithm, is about 0.13 higher than the $R^2$ associated with MLR.

• Geomechanics and Engineering
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• v.33 no.1
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• pp.1-9
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• 2023

The unconfined compression strength (UCS) of soils is commonly used either before or during the construction of geo-structures. In the pre-design stage, UCS as a mechanical property is obtained through a laboratory test that requires cumbersome procedures and high costs from in-situ sampling and sample preparation. As an alternative way, the empirical model established from limited testing cases is used to economically estimate the UCS. However, many parameters affecting the 1D soil compression response hinder employing the traditional statistical analysis. In this study, gene expression programming (GEP) is adopted to develop a prediction model of UCS with common affecting soil properties. A total of 79 undisturbed soil samples are collected, of which 54 samples are utilized for the generation of a predictive model and 25 samples are used to validate the proposed model. Experimental studies are conducted to measure the unconfined compression strength and basic soil index properties. A performance assessment of the prediction model is carried out using statistical checks including the correlation coefficient (R), the root mean square error (RMSE), the mean absolute error (MAE), the relatively squared error (RSE), and external criteria checks. The prediction model has achieved excellent accuracy with values of R, RMSE, MAE, and RSE of 0.98, 10.01, 7.94, and 0.03, respectively for the training data and 0.92, 19.82, 14.56, and 0.15, respectively for the testing data. From the sensitivity analysis and parametric study, the liquid limit and fine content are found to be the most sensitive parameters whereas the sand content is the least critical parameter.

• Genomics & Informatics
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• v.20 no.4
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• pp.45.1-45.7
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• 2022

Food security will be affected by climate change worldwide, particularly in the developing world, where the most important food products originate from plants. Plants are often exposed to environmental stresses that may affect their growth, development, yield, and food quality. Auxin is a hormone that plays a critical role in improving plants' tolerance of environmental conditions. Auxin controls the expression of many stress-responsive genes in plants by interacting with specific cis-regulatory elements called auxin-responsive elements (AuxREs). In this work, we performed an in silico prediction of AuxREs in promoters of five auxin-responsive genes in Zea mays. We applied a data fusion approach based on the combined use of Dempster-Shafer evidence theory and fuzzy sets. Auxin has a direct impact on cell membrane proteins. The short-term auxin response may be represented by the regulation of transmembrane gene expression. The detection of an AuxRE in the promoter of prolyl oligopeptidase (POP) in Z. mays and the 3-fold overexpression of this gene under auxin treatment for 30 min indicated the role of POP in maize auxin response. POP is regulated by auxin to perform stress adaptation. In addition, the detection of two AuxRE TGTCTC motifs in the upstream sequence of the bx1 gene suggests that bx1 can be regulated by auxin. Auxin may also be involved in the regulation of dehydration-responsive element-binding and some members of the protein kinase superfamily.

• Journal of Information Processing Systems
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• v.12 no.1
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• pp.1-34
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• 2016

Gene identification is at the center of genomic studies. Although the first phase of the Encyclopedia of DNA Elements (ENCODE) project has been claimed to be complete, the annotation of the functional elements is far from being so. Computational methods in gene identification continue to play important roles in this area and other relevant issues. So far, a lot of work has been performed on this area, and a plethora of computational methods and avenues have been developed. Many review papers have summarized these methods and other related work. However, most of them focus on the methodologies from a particular aspect or perspective. Different from these existing bodies of research, this paper aims to comprehensively summarize the mainstream computational methods in gene identification and tries to provide a short but concise technical reference for future studies. Moreover, this review sheds light on the emerging trends and cutting-edge techniques that are believed to be capable of leading the research on this field in the future.

• Horticultural Science & Technology
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• v.29 no.5
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• pp.461-466
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• 2011

In genetic and molecular breeding studies of plants, researchers need to design various kinds of primers based on their research purposes. So far many kinds of web- or script-based non-commercial programs for primer design are available. Because most of them do not include user interface for multipurpose usage including gene structure prediction and direct target selection on sequences, it has been a laborious work to design primers targeting on the exon or intron regions of interesting genes. Here we report a primer designing graphic user interface program, Pickprimer, that includes gene structure prediction and primer design modules by combining source codes of the Spidey and Primer3 programs. This program provides simple graphic user interface to input sequences and design primers. Genomic sequence and mRNA or coding sequence of genes can be copy and pasted or input as fasta or text files. Based on alignment of the input sequences using the Spidey module, a putative gene structure is graphically visualized along with exon-intron sequences of color codes. Primer design can be easily performed by dragging mouse on the displayed sequences or input primer targeting position with desirable values of primers. The output of designed primers with detailed information is provided by the Primer3 module. PCR evaluation of 24 selected primer sets successfully amplified single amplicons from six Brassica rapa cultivars. The Pickprimer will be a convenient tool for genetic and molecular breeding studies of plants.

• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.275-283
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• 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.

• The KIPS Transactions:PartB
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• v.18B no.2
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• pp.67-72
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• 2011

Single nucleotide polymorphisms (SNPs) are a very important tool for the study of human genome structure. Cluster analysis of the large amount of gene expression data is useful for identifying biologically relevant groups of genes and for generating networks of gene-gene interactions. In this paper we compared the clusters of SNPs within asthma group and normal control group obtained by using hierarchical cluster analysis method with entropy distance. It appears that the 5-cluster collections of the two groups are significantly different. We searched the best set of SNPs that are useful for diagnosing the two types of asthma using representative SNPs of the clusters of the asthma group. Here support vector machines are used to evaluate the prediction accuracy of the selected combinations. The best combination model turns out to be the five-locus SNPs including one on the gene ALOX12 and their accuracy in predicting aspirin tolerant asthma disease risk among asthmatic patients is 66.41%.