• Title/Summary/Keyword: concept-sequence

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Annotation of Genes Having Candidate Somatic Mutations in Acute Myeloid Leukemia with Whole-Exome Sequencing Using Concept Lattice Analysis

  • Lee, Kye Hwa;Lim, Jae Hyeun;Kim, Ju Han
    • Genomics & Informatics
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    • v.11 no.1
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    • pp.38-45
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    • 2013
  • In cancer genome studies, the annotation of newly detected oncogene/tumor suppressor gene candidates is a challenging process. We propose using concept lattice analysis for the annotation and interpretation of genes having candidate somatic mutations in whole-exome sequencing in acute myeloid leukemia (AML). We selected 45 highly mutated genes with whole-exome sequencing in 10 normal matched samples of the AML-M2 subtype. To evaluate these genes, we performed concept lattice analysis and annotated these genes with existing knowledge databases.

Feature Selection with Ensemble Learning for Prostate Cancer Prediction from Gene Expression

  • Abass, Yusuf Aleshinloye;Adeshina, Steve A.
    • International Journal of Computer Science & Network Security
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    • v.21 no.12spc
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    • pp.526-538
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    • 2021
  • Machine and deep learning-based models are emerging techniques that are being used to address prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that has attracted a great deal of attention in the biomedical domain. Machine and deep learning-based models have been shown to provide more accurate results when compared to conventional regression-based models. The prediction of the gene sequence that leads to cancerous diseases, such as prostate cancer, is crucial. Identifying the most important features in a gene sequence is a challenging task. Extracting the components of the gene sequence that can provide an insight into the types of mutation in the gene is of great importance as it will lead to effective drug design and the promotion of the new concept of personalised medicine. In this work, we extracted the exons in the prostate gene sequences that were used in the experiment. We built a Deep Neural Network (DNN) and Bi-directional Long-Short Term Memory (Bi-LSTM) model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The models were evaluated using different classification metrics. Our experimental results show that DNN model prediction offers a training accuracy of 99 percent and validation accuracy of 96 percent. The bi-LSTM model also has a training accuracy of 95 percent and validation accuracy of 91 percent.

A Study on Teaching and Learning of the Limit Concept in High School (고등학교에서의 극한개념 교수.학습에 관한 연구)

  • 박임숙;김흥기
    • Journal of Educational Research in Mathematics
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    • v.12 no.4
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    • pp.557-579
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    • 2002
  • The purpose of this study is to find out the problems which are caused when the limit concept of sequences is learned through an intuitive definition and to suggest a way of solving those problems. Students in Korea study the limit concept of sequences through an intuitive definition. They fail to apply the intuitive definition properly to the problems and they are apt to have misconception even though the Intuitive definition is applied properly. To solve these problems, this study examined the develop- mental process of the limit concept of sequences from the Intuitive definition to the formal definition, and looked into the way of students' internalization of the process through a field study. In this study, the levels of the limit concept of sequences possessed by the students at ZPD are as follows; level 0 : Students understand the limit concept of sequences through the intuitive definition. level 1 : Students understand the limit concept of sequences as 'The difference between $\alpha$$_{n}$ and $\alpha$ approaches 0' rather than 'The sequence approaches $\alpha$ infinitely.' level 2 : Students understand the limit concept of sequences through the formal definition. The levels of students' limit concept development were analysed by those criteria. Almost of the students who studied the limit concept of sequences through the intuitive defition stayed at level 0, whereas almost of the students who studied through the formal definition stayed at level 1. Through the study, I found that it was difficult for the students to develop the higher level of understanding for themselves but the teachers and peers could help the students to progress to the higher level. Students' learning ability was one of major factors that make the students progress to the higher level of understanding as the concept was developed hierarchically from Level 0 to Level 2. If you want to see your students get to the higher level of understanding in the limit concept, you need to facilitate them to fully develop understanding in lower levels through enough experiences so that they can be promoted to the highest level.

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M2M Transformation Rules for Automatic Test Case Generation from Sequence Diagram (시퀀스 다이어그램으로부터 테스트 케이스 자동 생성을 위한 M2M(Model-to-Model) 변환 규칙)

  • Kim, Jin-a;Kim, Su Ji;Seo, Yongjin;Cheon, Eunyoung;Kim, Hyeon Soo
    • KIISE Transactions on Computing Practices
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    • v.22 no.1
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    • pp.32-37
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    • 2016
  • In model-based testing using sequence diagrams, test cases are automatically derived from the sequence diagrams. For the generation of test cases, scenarios need to be found for representing as a sequence diagram, and to extract test paths satisfying the test coverage. However, it is hard to automatically extract test paths from the sequence diagram because a sequence diagram represents loop, opt, and alt information using CombinedFragments. To resolve this problem, we propose a transformation process that transforms a sequence diagram into an activity diagram which represents scenarios as a type of control flows. In addition, we generate test cases from the activity diagram by applying a test coverage concept. Finally, we present a case study for test cases generation from a sequence diagram.

A Dynamic Ensemble Method using Adaptive Weight Adjustment for Concept Drifting Streaming Data (컨셉 변동 스트리밍 데이터를 위한 적응적 가중치 조정을 이용한 동적 앙상블 방법)

  • Kim, Young-Deok;Park, Cheong Hee
    • Journal of KIISE
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    • v.44 no.8
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    • pp.842-853
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    • 2017
  • Streaming data is a sequence of data samples that are consistently generated over time. The data distribution or concept can change over time, and this change becomes a factor to reduce the performance of a classification model. Adaptive incremental learning can maintain the classification performance by updating the current classification model with the weight adjusted according to the degree of concept drift. However, selecting the proper weight value depending on the degree of concept drift is difficult. In this paper, we propose a dynamic ensemble method based on adaptive weight adjustment according to the degree of concept drift. Experimental results demonstrate that the proposed method shows higher performance than the other compared methods.

ON THE κ-REGULAR SEQUENCES AND THE GENERALIZATION OF F-MODULES

  • Ahmadi-Amoli, Khadijeh;Sanaei, Navid
    • Journal of the Korean Mathematical Society
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    • v.49 no.5
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    • pp.1083-1096
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    • 2012
  • For a given ideal I of a Noetherian ring R and an arbitrary integer ${\kappa}{\geq}-1$, we apply the concept of ${\kappa}$-regular sequences and the notion of ${\kappa}$-depth to give some results on modules called ${\kappa}$-Cohen Macaulay modules, which in local case, is exactly the ${\kappa}$-modules (as a generalization of f-modules). Meanwhile, we give an expression of local cohomology with respect to any ${\kappa}$-regular sequence in I, in a particular case. We prove that the dimension of homology modules of the Koszul complex with respect to any ${\kappa}$-regular sequence is at most ${\kappa}$. Therefore homology modules of the Koszul complex with respect to any filter regular sequence has finite length.

On the Multi-Stage Group Scheduling with Dependent Setup Time (종속적 준비시간을 갖는 다단계 그룹가공 생산시스템에서의 그룹스케듈링에 관한 연구)

  • 황문영
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.17 no.31
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    • pp.115-123
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    • 1994
  • Group scheduling, which is a kind of operations scheduling based on the GT concept is analyzed in a multi-stage manufacturing system. The purpose of this research is to develop and evaluate a heuristic algorithm for determining gro up sequence and job sequence within each group to minimize a complex cost function, i.e. the sum of the total pe-nalty cost for tardiness and the total holding cost for flow time, in a multi-stage manufacturing system with group setup time dependent upon group sequence. A heuristic algorithm for group sc heduling is developed, and a numerical example is illustrated. For the evaluation of the pro-posed heuristic algorithm, the heuristic solution of each of 63 problems is compared with that of random scheduling. The result shows that the proposed heuristic algorithm provides better solution in light of the proposed cost function.

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Basic Concept of Gene Microarray (Gene Microarray의 기본개념)

  • Hwang, Seung Yong
    • Korean Journal of Biological Psychiatry
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    • v.8 no.2
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    • pp.203-207
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    • 2001
  • The genome sequencing project has generated and will continue to generate enormous amounts of sequence data including 5 eukaryotic and about 60 prokaryotic genomes. Given this ever-increasing amounts of sequence information, new strategies are necessary to efficiently pursue the next phase of the genome project-the elucidation of gene expression patterns and gene product function on a whole genome scale. In order to assign functional information to the genome sequence, DNA chip(or gene microarray) technology was developed to efficiently identify the differential expression pattern of independent biological samples. DNA chip provides a new tool for genome expression analysis that may revolutionize many aspects of biotechnology including new drug discovery and disease diagnostics.

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Global Sequence Homology Detection Using Word Conservation Probability

  • Yang, Jae-Seong;Kim, Dae-Kyum;Kim, Jin-Ho;Kim, Sang-Uk
    • Interdisciplinary Bio Central
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    • v.3 no.4
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    • pp.14.1-14.9
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    • 2011
  • Protein homology detection is an important issue in comparative genomics. Because of the exponential growth of sequence databases, fast and efficient homology detection tools are urgently needed. Currently, for homology detection, sequence comparison methods using local alignment such as BLAST are generally used as they give a reasonable measure for sequence similarity. However, these methods have drawbacks in offering overall sequence similarity, especially in dealing with eukaryotic genomes that often contain many insertions and duplications on sequences. Also these methods do not provide the explicit models for speciation, thus it is difficult to interpret their similarity measure into homology detection. Here, we present a novel method based on Word Conservation Score (WCS) to address the current limitations of homology detection. Instead of counting each amino acid, we adopted the concept of 'Word' to compare sequences. WCS measures overall sequence similarity by comparing word contents, which is much faster than BLAST comparisons. Furthermore, evolutionary distance between homologous sequences could be measured by WCS. Therefore, we expect that sequence comparison with WCS is useful for the multiple-species-comparisons of large genomes. In the performance comparisons on protein structural classifications, our method showed a considerable improvement over BLAST. Our method found bigger micro-syntenic blocks which consist of orthologs with conserved gene order. By testing on various datasets, we showed that WCS gives faster and better overall similarity measure compared to BLAST.

A Parallel Sequence Extraction Algorithm for Generating Assembly BOM (조립 BOM 생성을 위한 병렬순서 추출 알고리듬)

  • Yeo, Myung-Koo;Choi, Hoo-Gon;Kim, Kwang-Soo
    • Journal of Korean Institute of Industrial Engineers
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    • v.29 no.1
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    • pp.49-64
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    • 2003
  • Although assembly sequence planning is an essential task in assembly process planning, it is known as one of the most difficult and time consuming jobs because its complexity is increased geometrically when the number of parts in an assembly is increased. The purpose of this study is to develop a more efficient algorithm for generating assembly sequences automatically. By considering subassemblies, a new heuristic method generates a preferred parallel assembly sequence that can be used in robotic assembly systems. A parallel assembly sequence concept provides a new representation scheme for an assembly in which the assembly sequence precedence information is not required. After an user inputs both the directional mating relation information and the mating condition information, an assembly product is divided into subgroups if the product has cut-vertices. Then, a virtual disassembly process is executed to generate alternate parallel assembly sequences with intermediate assembly stability. Through searching parts relations in the virtual disassembly process, stable subassemblies are extracted from translation-free parts along disassembling directions and this extraction continues until no more subassemblies are existed. Also, the arithmetic mean parallelism formula as a preference criterion is adapted to select the best parallel assembly sequence among others. Finally a preferred parallel assembly sequence is converted to an assembly BOM structure. The results from this study can be utilized for developing CAAPP(Computer-Aided Assembly Process Planning) systems as an efficient assembly sequence planning algorithm.