• 제목/요약/키워드: profile hidden Markov model

검색결과 9건 처리시간 0.034초

유비쿼터스 홈 네트워크 시스템에서 은닉 마르코프 모델을 이용한 사용자 행동 상태 분석 및 예측 알고리즘 (Analysis and Prediction Algorithms on the State of User's Action Using the Hidden Markov Model in a Ubiquitous Home Network System)

  • 신동규;신동일;황구연;최진욱
    • 인터넷정보학회논문지
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    • 제12권2호
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    • pp.9-17
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    • 2011
  • 본 논문은 유비쿼터스 홈 네트워크 시스템에서 저장된 사용자 행동 프로파일 데이터에 은닉 마르코프 모델에 적용하여 사용자의 행동 상태를 예측하는 알고리즘을 제안한다. 은닉 마르코프 모델은, 순차 데이터를 갖는 패턴을 인식하기 위해서 데이터에 내포되어 있는 시간성을 적절히 표현하고, 그것으로부터 원하는 정보를 추론할 수 있는 대표적인 모델이다. 제안 알고리즘에서는 "행동 인지 시스템(Activity Recognition System)"에 의하여 저장된 행동 발생 횟수, 행동 지속시간, 행동이 발생된 위치 데이터를 학습 데이터로 이용하였다. 사용자의 행동에 가중치를 부여하여 사용자의 행동에 대한 흥미를 객관적으로 수식화 하는 방법을 제안하였으며 은닉 마르코프 모델을 이용하여 시간에 따른 가중치 변화를 구하여 사용자의 행동 상태 변화를 예측하였다. 제안 알고리즘은 현실적인 유비쿼터스 홈 네트워크 구축에 도움을 준다.

예쁜꼬마선충의 수영 행동 영상과 기계학습 모델을 이용한 수질 오염 물질 구분 방법 (A Method for the Classification of Water Pollutants using Machine Learning Model with Swimming Activities Videos of Caenorhabditis elegans)

  • 강승호;정인선;임형석
    • 한국정보통신학회논문지
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    • 제25권7호
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    • pp.903-909
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    • 2021
  • 예쁜꼬마선충(Caenorhabditis elegans)은 염기서열이 완전히 밝혀진 동물로 유전자 기능 분석, 동물 행동 연구 등 다양한 연구 분야에 사용되는 대표적인 생물 종이다. 그동안 선충을 이용해 물의 오염 여부를 판별하기 위한 바이오 모니터링 시스템에 대한 여러 연구들이 있었다. 본 논문은 하천의 수질 오염의 원인이 되는 화학물질을 식별하기 위해 선충의 수영 행동이 활용 가능한 지를 보여주기 위해 기계학습 기반의 바이오 모니터링 시스템을 제안한다. 선충의 수영 행동을 대표하기 위해 선충을 대상으로 가지 길이 유사성(Branch Length Similarity) 엔트로피를 계산한다. 그리고 BLS 엔트로피의 조합인 BLS 엔트로피 프로파일을 클러스터링 알고리즘을 사용해 몇 가지 패턴으로 유형화하여 데이터 집합을 만든다. 0.1ppm 농도의 포름알데히드, 벤젠, 톨루엔이 첨가된 아레나에서 선충의 수영 행동을 촬영하고 개발한 히든 마코프 모델(Hidden Markov Model: HMM)의 성능을 검증한다.

컬러 영상 위에서 DCT 기반의 빠른 문자 열 구간 분리 모델 (Fast Text Line Segmentation Model Based on DCT for Color Image)

  • 신현경
    • 정보처리학회논문지D
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    • 제17D권6호
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    • pp.463-470
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    • 2010
  • 본 논문에서는 DCT 데이터에서 영상 데이터로의 해독 및 이진화 과정을 생략하고 컬러 영상의 DCT 관련 원자료를 사용하는 방법에 기반을 둔 매우 빠르고 안정적인 문자열 구간 분리 모형을 제안하였다. DCT 블록에 저장된 DC 및 3개의 주요 AC 변수들을 조합하여 축소된 저해상도 회색 영상을 만들고 횡렬 및 종렬 투영법을 통해 얻어진 픽셀 값의 히스토그램을 분석하여 문자 열 구간 사이에 존재하는 백색의 띠 공간을 찾아내었다. 이 과정 중 탐색되지 않은 문자 열 구간은 마코프 모델을 사용하여 숨겨진 주기를 찾아내어 복원하였다. 본 논문에 실험 결과를 제시하였으며 기존의 방법보다 약 40 - 100배 빠른 방법임을 입증하였다.

Development of an Analysis Program of Type I Polyketide Synthase Gene Clusters Using Homology Search and Profile Hidden Markov Model

  • Tae, Hong-Seok;Sohng, Jae-Kyung;Park, Kie-Jung
    • Journal of Microbiology and Biotechnology
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    • 제19권2호
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    • pp.140-146
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    • 2009
  • MAPSI(Management and Analysis for Polyketide Synthase Type I) has been developed to offer computational analysis methods to detect type I PKS(polyketide synthase) gene clusters in genome sequences. MAPSI provides a genome analysis component, which detects PKS gene clusters by identifying domains in proteins of a genome. MAPSI also contains databases on polyketides and genome annotation data, as well as analytic components such as new PKS assembly and domain analysis. The polyketide data and analysis component are accessible through Web interfaces and are displayed with diverse information. MAPSI, which was developed to aid researchers studying type I polyketides, provides diverse components to access and analyze polyketide information and should become a very powerful computational tool for polyketide research. The system can be extended through further studies of factors related to the biological activities of polyketides.

Evaluating Mental State of Final Year Students Based on POMS Questionnaire and HRV Signal

  • Handri, Santoso;Nomura, Shusaku;Nakamura, Kazuo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제10권1호
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    • pp.37-42
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    • 2010
  • Final year students are normally encountering high pressing in their study. In view of this fact, this research focuses on determining mental states condition of college student in final year based on the psycho-physiological information. The experiments were conducted in two times, i.e., prior- and post- graduation seminar examination. The early results indicated that the student profile of mood states (POMS) in prior final graduation seminar showed higher scores than students in post final graduation seminar. Thus, in this research, relation between biosignal representing by heart rate variability (HRV) and questionnaire responses were evaluated by hidden Markov model (HMM) and neural networks (NN).

A Computational Approach for the Classification of Protein Tyrosine Kinases

  • Park, Hyun-Chul;Eo, Hae-Seok;Kim, Won
    • Molecules and Cells
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    • 제28권3호
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    • pp.195-200
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    • 2009
  • Protein tyrosine kinases (PTKs) play a central role in the modulation of a wide variety of cellular events such as differentiation, proliferation and metabolism, and their unregulated activation can lead to various diseases including cancer and diabetes. PTKs represent a diverse family of proteins including both receptor tyrosine kinases (RTKs) and non-receptor tyrosine kinases (NRTKs). Due to the diversity and important cellular roles of PTKs, accurate classification methods are required to better understand and differentiate different PTKs. In addition, PTKs have become important targets for drugs, providing a further need to develop novel methods to accurately classify this set of important biological molecules. Here, we introduce a novel statistical model for the classification of PTKs that is based on their structural features. The approach allows for both the recognition of PTKs and the classification of RTKs into their subfamilies. This novel approach had an overall accuracy of 98.5% for the identification of PTKs, and 99.3% for the classification of RTKs.

고집적어레이 기반의 비교유전체보합법(CGH)을 통한 신경아세포종 Neuro2a 세포의 유전체이상 분석 (High Resolution Genomic Profile of Neuro2a Murine Neuroblastoma Cell Line by Array-based Comparative Genomic Hybridization)

  • 도진환;김인수;고현명;최동국
    • 생명과학회지
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    • 제19권4호
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    • pp.449-456
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    • 2009
  • 신경아세포종은 미분화된 신경외배엽 세포로부터 유래한 신경능세포에 의해 형성된 소아기에 보는 가장 많이 발생하는 악성 종양 중 하나이다. 신경아세포종인 Neuro-2a 세포는 신경세포의 분화, 세포사 억제 효능, 세포독성 검정 등에 활용되고 있다. Neuro-2a 역시 다른 신경아세종과 같이 염색체 변이를 가지고 있지만, 이에 대해 고밀도의 게놈수준에서 염색체 변이에 대해 보고된 바가 없다. 본 연구에서는 고집적 마이크로어레이(최소 43,000 개의 코딩, non-코딩 유전자 서열이 집적된 마이크로어레이)기반의 비교유전체보합법을 활용하여, 고해상도의 Neuro-2a 유전체 이상을 분석하였다. 마이크로 어레이 데이터는 Hidden Markov Model을 활용하여, 유전체 변이를 double loss, single loss, normal, single gain 그리고 amplification으로 나누어 분석하였다. Neuro2a는 MYCN 유전자의 증폭은 관찰되지 않았고, GDNF, BDNF, NENF등의 neurotrophic factor 가운데 NENF의 gain 현상이 관찰 되었다. 염색체의 이상은 4,8,10,11,15번에서 발견되었으며, 염색체 3,17,18,19에서는 전부 20개 미만의 염색체 이상이 발견되었다. 염색체 이상이 연속적으로 일어난 부위 중 gain으로서 가장 긴 부분은 Chr8:8,427,841-35,162,415의 약 26.7 Mb이며, single loss로서 가장 긴 곳은 Chr4:73,265,785-88,374,165의 약 15.1 Mb였다. 염색체의 위치는 UCSC 데이터베이스 (UCSC mm8, NCBI Build 36)에 근거하였다.

Genome-Wide Analysis of Type VI System Clusters and Effectors in Burkholderia Species

  • Nguyen, Thao Thi;Lee, Hyun-Hee;Park, Inmyoung;Seo, Young-Su
    • The Plant Pathology Journal
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    • 제34권1호
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    • pp.11-22
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    • 2018
  • Type VI secretion system (T6SS) has been discovered in a variety of gram-negative bacteria as a versatile weapon to stimulate the killing of eukaryotic cells or prokaryotic competitors. Type VI secretion effectors (T6SEs) are well known as key virulence factors for important pathogenic bacteria. In many Burkholderia species, T6SS has evolved as the most complicated secretion pathway with distinguished types to translocate diverse T6SEs, suggesting their essential roles in this genus. Here we attempted to detect and characterize T6SSs and potential T6SEs in target genomes of plant-associated and environmental Burkholderia species based on computational analyses. In total, 66 potential functional T6SS clusters were found in 30 target Burkholderia bacterial genomes, of which 33% possess three or four clusters. The core proteins in each cluster were specified and phylogenetic trees of three components (i.e., TssC, TssD, TssL) were constructed to elucidate the relationship among the identified T6SS clusters. Next, we identified 322 potential T6SEs in the target genomes based on homology searches and explored the important domains conserved in effector candidates. In addition, using the screening approach based on the profile hidden Markov model (pHMM) of T6SEs that possess markers for type VI effectors (MIX motif) (MIX T6SEs), 57 revealed proteins that were not included in training datasets were recognized as novel MIX T6SE candidates from the Burkholderia species. This approach could be useful to identify potential T6SEs from other bacterial genomes.

Epigenetic Regulation of Fungal Development and Pathogenesis in the Rice Blast Fungus

  • Jeon, Junhyun
    • 한국균학회소식:학술대회논문집
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    • 한국균학회 2014년도 추계학술대회 및 정기총회
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    • pp.11-11
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    • 2014
  • Fungal pathogens have huge impact on health and economic wellbeing of human by causing life-threatening mycoses in immune-compromised patients or by destroying crop plants. A key determinant of fungal pathogenesis is their ability to undergo developmental change in response to host or environmental factors. Genetic pathways that regulate such morphological transitions and adaptation are therefore extensively studied during the last few decades. Given that epigenetic as well as genetic components play pivotal roles in development of plants and mammals, contribution of microbial epigenetic counterparts to this morphogenetic process is intriguing yet nearly unappreciated question to date. To bridge this gap in our knowledge, we set out to investigate histone modifications among epigenetic mechanisms that possibly regulate fungal adaptation and processes involved in pathogenesis of a model plant pathogenic fungus, Magnaporthe oryzae. M. oryzae is a causal agent of rice blast disease, which destroys 10 to 30% of the rice crop annually. Since the rice is the staple food for more than half of human population, the disease is a major threat to global food security. In addition to the socioeconomic impact of the disease it causes, the fungus is genetically tractable and can undergo well-defined morphological transitions including asexual spore production and appressorium (a specialized infection structure) formation in vitro, making it a model to study fungal development and pathogenicity. For functional and comparative analysis of histone modifications, a web-based database (dbHiMo) was constructed to archive and analyze histone modifying enzymes from eukaryotic species whose genome sequences are available. Histone modifying enzymes were identified applying a search pipeline built upon profile hidden Markov model (HMM) to proteomes. The database incorporates 22,169 histone-modifying enzymes identified from 342 species including 214 fungal, 33 plants, and 77 metazoan species. The dbHiMo provides users with web-based personalized data browsing and analysis tools, supporting comparative and evolutionary genomics. Based on the database entries, functional analysis of genes encoding histone acetyltransferases and histone demethylases is under way. Here I provide examples of such analyses that show how histone acetylation and methylation is implicated in regulating important aspects of fungal pathogenesis. Current analysis of histone modifying enzymes will be followed by ChIP-Seq and RNA-seq experiments to pinpoint the genes that are controlled by particular histone modifications. We anticipate that our work will provide not only the significant advances in our understanding of epigenetic mechanisms operating in microbial eukaryotes but also basis to expand our perspective on regulation of development in fungal pathogens.

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