检索结果-内蒙古大学图书馆

Research Octane Number Prediction Based on Feature Selection and Multi-model Fusion

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第3期15卷 1145-1152页

作者： Gu, Junlin Jiangsu Vocat Coll Elect & Informat Huaian Peoples R China

The catalytic cracking-based process for lightening heavy oil yields gasoline products with sulfur and olefin contents surpassing 95%, consequently diminishing the Research Octane Number (RON) of gasoline during desulfurization and olefin reduction stages. Hence, investigating methodologies to mitigate RON loss in gasoline while maintaining effective desulfurization is imperative. This study addresses this challenge by initially performing data cleaning and augmentation, employing box plot modeling and Grubbs' test for outlier detection and removal. Subsequently, through the integration of mutual information and the Lasso method, data dimensionality is reduced, with the top 30 variables selected as primary factors. A predictive model for RON loss is then established based on these 30 variables, utilizing random forest and support vector Regression (SVR) models. Employing this model enables the computation of RON loss for each data sample. Comparing with existing methods, our approach could ensure a balance between effective desulfurization and mitigated RON loss in gasoline products.

关键词： Feature selection random forest model support vector machine model RON loss

来源：评论

学校读者我要写书评

暂无评论

Prediction and classification of aminoacyl tRNA synthetases using PROSITE domains

引用

BMC GENOMICS 2010年第1期11卷 507-507页

作者： Panwar, Bharat Raghava, Gajendra P. S. Bioinformat Ctr Inst Microbial Technol Chandigarh India

Background: Aminoacyl tRNA synthetases (aaRSs) catalyse the first step of protein synthesis in all organisms. They are responsible for the precise attachment of amino acids to their cognate transfer RNAs. There are twenty different types of aaRSs, unique for each amino acid. These aaRSs have been divided into two classes, each comprising ten enzymes. It is important to predict and classify aaRSs in order to understand protein synthesis. Results: In this study, all models were developed on a non-redundant dataset containing 117 aaRSs and an equal number of non-aaRSs, in which no two sequences have more than 30% similarity. First, we applied the similarity search technique, BLAST, and achieved a maximum accuracy of 67.52%. We observed that 62% of tRNA synthetases contain one or more domains from amongst the following four PROSITE domains: PS50862, PS00178, PS50860 and PS50861. An SVM-based model was developed to discriminate between aaRSs, and non-aaRSs, and achieved a maximum MCC of 0.68 with accuracy of 83.73%, using selective dipeptide composition. We developed a hybrid approach and achieved a maximum MCC of 0.72 with accuracy of 85.49%, where SVM model developed using selected dipeptide composition and information of four PROSITE domains. We further developed an SVM-based model for classifying the aaRSs into class-1 and class-2, using selective dipeptide composition and achieved an MCC of 0.79. We also observed that two domains (PS00178, PS50889) in class-1 and three domains (PS50862, PS50860, PS50861) in class-2 were preferred. A hybrid method was developed using these domains as descriptor, along with selected dipeptide composition, and achieved an MCC of 0.87 with a sensitivity of 94.55% and an accuracy of 93.19%. All models were evaluated using a five-fold cross-validation technique. Conclusions: We have analyzed protein sequences of aaRSs (class-1 and class-2) and non-aaRSs and identified interesting patterns. The high accuracy achieved by our SVM models usin

关键词： support vector machine Amino Acid Composition Dipeptide support vector machine model machine Learning Technique

来源：评论

学校读者我要写书评

暂无评论

Inference of brain pathway activities for Alzheimer's disease classification

引用

BMC MEDICAL INFORMATICS AND DECISION MAKING 2015年第Sup1期15卷 S1-S1页

作者： Lee, Jongan Kim, Younghoon Jeong, Yong Na, Duk L. Kim, Jong-Won Lee, Kwang H. Lee, Doheon Sungkyunkwan Univ Sch Med Samsung Med Ctr Dept Lab Med & Genet Seoul South Korea Korea Adv Inst Sci & Technol Dept Bio & Brain Engn Daejeon South Korea Sungkyunkwan Univ Sch Med Samsung Med Ctr Dept Neurol Seoul South Korea

Background: Alzheimer's disease (AD) is a neurodegenerative and progressive disorder that results in brain malfunctions. Resting-state (RS) functional magnetic resonance imaging (fMRI) techniques have been successfully applied for quantifying brain activities of both Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) patients. Region-based approaches are widely utilized to classify patients from cognitively normal subjects (CN). Nevertheless, region-based approaches have a few limitations, reproducibility owing to selection of diseasespecific brain regions, and heterogeneity of brain activities during disease progression. For coping with these issues, network-based approaches have been suggested in the field of molecular bioinformatics. In comparison with individual gene-based approaches, they acquired more accurate results in diverse disease classification, and reproducibility was confirmed by replication studies. In our work, we applied a similar methodology integrating brain pathway information into pathway activity inference, and permitting classification of both aMCI and AD patients based on pathway activities rather than single region activities. Results: After aggregating the 59 brain pathways from literature, we estimated brain pathway activities by using exhaustive search algorithms between patients and cognitively normal subjects, and identified discriminatory pathways according to disease progression. We used three different data sets and each data set consists of two different groups. Our results show that the pathway-based approach (AUC = 0.89, 0.9, 0.75) outperformed the region-based approach (AUC = 0.69, 0.8, 0.68). Also, our approach provided enhanced diagnostic power achieving higher accuracy, sensitivity, and specificity (pathway-based approach: accuracy = 83%;sensitivity = 86%;specificity = 78%, region-based approach: accuracy = 74%;sensitivity = 78%;specificity = 76%). Conclusions: We proposed a novel method inferring brain pa

关键词： Functional Connectivity Default Mode Network support vector machine model Cognitively Normal aMCI Patient

来源：评论

学校读者我要写书评

暂无评论

Prediction of intracranial findings on CT-scans by alternative modelling techniques

引用

BMC MEDICAL RESEARCH METHODOLOGY 2011年第1期11卷 143-143页

作者： van der Ploeg, Tjeerd Smits, Marion Dippel, Diederik W. Hunink, Myriam Steyerberg, Ewout W. Erasmus MC Rotterdam Netherlands Alkmaar Inholland Univ Med Ctr Alkmaar Netherlands

Background: Prediction rules for intracranial traumatic findings in patients with minor head injury are designed to reduce the use of computed tomography (CT) without missing patients at risk for complications. This study investigates whether alternative modelling techniques might improve the applicability and simplicity of such prediction rules. Methods: We included 3181 patients with minor head injury who had received CT scans between February 2002 and August 2004. Of these patients 243 (7.6%) had intracranial traumatic findings and 17 (0.5%) underwent neurosurgical intervention. We analyzed sensitivity, specificity and area under the ROC curve (AUC-value) to compare the performance of various modelling techniques by 10 x 10 cross-validation. The techniques included logistic regression, Bayes network, Chi-squared Automatic Interaction Detection (CHAID), neural net, support vector machines, Classification And Regression Trees (CART) and "decision list" models. Results: The cross-validated performance was best for the logistic regression model (AUC 0.78), followed by the Bayes network model and the neural net model (both AUC 0.74). The other models performed poorly (AUC < 0.70). The advantage of the Bayes network model was that it provided a graphical representation of the relationships between the predictors and the outcome. Conclusions: No alternative modelling technique outperformed the logistic regression model. However, the Bayes network model had a presentation format which provided more detailed insights into the structure of the prediction problem. The search for methods with good predictive performance and an attractive presentation format should continue.

关键词： support vector machine model Prediction Rule Fracture Skull Intracranial Complication Minor Head Injury

来源：评论

学校读者我要写书评

暂无评论

Identification of ATP binding residues of a protein from its primary sequence

引用

BMC BIOINFORMATICS 2009年第1期10卷 434-434页

作者： Chauhan, Jagat S. Mishra, Nitish K. Raghava, Gajendra P. S. Inst Microbial Technol Chandigarh India

Background: One of the major challenges in post-genomic era is to provide functional annotations for large number of proteins arising from genome sequencing projects. The function of many proteins depends on their interaction with small molecules or ligands. ATP is one such important ligand that plays critical role as a coenzyme in the functionality of many proteins. There is a need to develop method for identifying ATP interacting residues in a ATP binding proteins (ABPs), in order to understand mechanism of protein-ligands interaction. Results: We have compared the amino acid composition of ATP interacting and non-interacting regions of proteins and observed that certain residues are preferred for interaction with ATP. This study describes few models that have been developed for identifying ATP interacting residues in a protein. All these models were trained and tested on 168 non-redundant ABPs chains. First we have developed a support vector machine (SVM) based model using primary sequence of proteins and obtained maximum MCC 0.33 with accuracy of 66.25%. Secondly, another SVM based model was developed using position specific scoring matrix (PSSM) generated by PSI-BLAST. The performance of this model was improved significantly (MCC 0.5) from the previous one, where only the primary sequence of the proteins were used. Conclusion: This study demonstrates that it is possible to predict 'ATP interacting residues' in a protein with moderate accuracy using its sequence. The evolutionary information is important for the identification of 'ATP interacting residues', as it provides more information compared to the primary sequence. This method will be useful for researchers studying ATP-binding proteins. Based on this study, a web server has been developed for predicting 'ATP interacting residues' in a protein http://***/raghava/atpint/.

关键词： support vector machine Window Size support vector machine model Evolutionary Information Interact Residue

来源：评论

学校读者我要写书评

暂无评论

Detection of extremely low concentration waterborne pathogen using a multiplexing self-referencing SERS microfluidic biosensor

引用

JOURNAL OF BIOLOGICAL ENGINEERING 2017年第1期11卷 9-9页

作者： Wang, Chao Madiyar, Foram Yu, Chenxu Li, Jun Iowa State Univ Agr & Biosyst Engn Dept Ames IA 50011 USA Kansas State Univ Chem Dept Manhattan KS 66506 USA

Background: It is challenging to achieve ultrasensitive and selective detection of waterborne pathogens at extremely low levels (i.e., single cell/mL) using conventional methods. Even with molecular methods such as ELISA or PCR, multi-enrichment steps are needed which are labor and cost intensive. In this study, we incorporated nano-dielectrophoretic microfluidic device with Surface enhanced Raman scattering (SERS) technique to build a novel portable biosensor for easy detection and characterization of Escherichia coli O157:H7 at high sensitivity level (single cell/mL). Results: A multiplexing dual recognition SERS scheme was developed to achieve one-step target detection without the need to separate target-bound probes from unbound ones. With three different SERS-tagged molecular probes targeting different epitopes of the same pathogen being deployed simultaneously, detection of pathogen targets was achieved at single cell level with sub-species specificity that has not been reported before in single-step pathogen detection. Conclusion: The self-referencing protocol implements with a Nano-dielectrophoretic microfluidic device potentially can become an easy-to-use, field-deployable spectroscopic sensor for onsite detection of pathogenic microorganisms.

关键词： Surface Enhance Raman Scattering Microfluidic Device support vector machine model Pathogen Detection Surface Enhance Raman Scattering Spectrum

来源：评论

学校读者我要写书评

暂无评论

Automated systems to identify relevant documents in product risk management

引用

BMC MEDICAL INFORMATICS AND DECISION MAKING 2012年第1期12卷 13-13页

作者： Wee, Xue Ting Koh, Yvonne Yap, Chun Wei Natl Univ Singapore Dept Pharm Singapore 117543 Singapore Hlth Sci Author Hlth Prod Regulat Grp Compliance & Enforcement Div Vigilance Branch Singapore 138667 Singapore

Background: Product risk management involves critical assessment of the risks and benefits of health products circulating in the market. One of the important sources of safety information is the primary literature, especially for newer products which regulatory authorities have relatively little experience with. Although the primary literature provides vast and diverse information, only a small proportion of which is useful for product risk assessment work. Hence, the aim of this study is to explore the possibility of using text mining to automate the identification of useful articles, which will reduce the time taken for literature search and hence improving work efficiency. In this study, term-frequency inverse document-frequency values were computed for predictors extracted from the titles and abstracts of articles related to three tumour necrosis factors-alpha blockers. A general automated system was developed using only general predictors and was tested for its generalizability using articles related to four other drug classes. Several specific automated systems were developed using both general and specific predictors and training sets of different sizes in order to determine the minimum number of articles required for developing such systems. Results: The general automated system had an area under the curve value of 0.731 and was able to rank 34.6% and 46.2% of the total number of 'useful' articles among the first 10% and 20% of the articles presented to the evaluators when tested on the generalizability set. However, its use may be limited by the subjective definition of useful articles. For the specific automated system, it was found that only 20 articles were required to develop a specific automated system with a prediction performance (AUC 0.748) that was better than that of general automated system. Conclusions: Specific automated systems can be developed rapidly and avoid problems caused by subjective definition of useful articles. Thus the efficiency o

关键词： support vector machine Automate System Prediction Performance Text Mining support vector machine model

来源：评论

学校读者我要写书评

暂无评论

Systems-level analysis of age-related macular degeneration reveals global biomarkers and phenotype-specific functional networks

引用

GENOME MEDICINE 2012年第2期4卷 16-16页

作者： Newman, Aaron M. Gallo, Natasha B. Hancox, Lisa S. Miller, Norma J. Radeke, Carolyn M. Maloney, Michelle A. Cooper, James B. Hageman, Gregory S. Anderson, Don H. Johnson, Lincoln V. Radeke, Monte J. Univ Calif Santa Barbara Neurosci Res Inst Ctr Study Macular Degenerat Santa Barbara CA 93106 USA Univ Iowa Dept Ophthalmol & Visual Sci Iowa City IA 52242 USA Univ Utah Dept Ophthalmol & Visual Sci John A Moran Eye Ctr Salt Lake City UT 84132 USA Univ Calif Santa Barbara Dept Mol Cellular & Dev Biol Santa Barbara CA 93106 USA

Background: Age-related macular degeneration (AMD) is a leading cause of blindness that affects the central region of the retinal pigmented epithelium (RPE), choroid, and neural retina. Initially characterized by an accumulation of sub-RPE deposits, AMD leads to progressive retinal degeneration, and in advanced cases, irreversible vision loss. Although genetic analysis, animal models, and cell culture systems have yielded important insights into AMD, the molecular pathways underlying AMD's onset and progression remain poorly delineated. We sought to better understand the molecular underpinnings of this devastating disease by performing the first comparative transcriptome analysis of AMD and normal human donor eyes. Methods: RPE-choroid and retina tissue samples were obtained from a common cohort of 31 normal, 26 AMD, and 11 potential pre-AMD human donor eyes. Transcriptome profiles were generated for macular and extramacular regions, and statistical and bioinformatic methods were employed to identify disease-associated gene signatures and functionally enriched protein association networks. Selected genes of high significance were validated using an independent donor cohort. Results: We identified over 50 annotated genes enriched in cell-mediated immune responses that are globally over-expressed in RPE-choroid AMD phenotypes. Using a machine learning model and a second donor cohort, we show that the top 20 global genes are predictive of AMD clinical diagnosis. We also discovered functionally enriched gene sets in the RPE-choroid that delineate the advanced AMD phenotypes, neovascular AMD and geographic atrophy. Moreover, we identified a graded increase of transcript levels in the retina related to wound response, complement cascade, and neurogenesis that strongly correlates with decreased levels of phototransduction transcripts and increased AMD severity. Based on our findings, we assembled protein-protein interactomes that highlight functional networks likely to be

关键词： support vector machine model Retinal Pigment Epithelium Disease Module Geographic Atrophy Disease Module Gene

来源：评论

学校读者我要写书评

暂无评论

Analysis of syntactic and semantic features for fine-grained event-spatial understanding in outbreak news reports

引用

JOURNAL OF BIOMEDICAL SEMANTICS 2010年第2010期1卷 3-3页

作者： Chanlekha, Hutchatai Collier, Nigel Res Org Informat & Syst Natl Inst Informat Chiyoda Ku Tokyo 1018430 Japan

Background: Previous studies have suggested that epidemiological reasoning needs a fine-grained modelling of events, especially their spatial and temporal attributes. While the temporal analysis of events has been intensively studied, far less attention has been paid to their spatial analysis. This article aims at filling the gap concerning automatic event-spatial attribute analysis in order to support health surveillance and epidemiological reasoning. Results: In this work, we propose a methodology that provides a detailed analysis on each event reported in news articles to recover the most specific locations where it occurs. Various features for recognizing spatial attributes of the events were studied and incorporated into the models which were trained by several machine learning techniques. The best performance for spatial attribute recognition is very promising;85.9% F-score (86.75% precision/85.1% recall). Conclusions: We extended our work on event-spatial attribute recognition by focusing on machine learning techniques, which are CRF, SVM, and Decision tree. Our approach avoided the costly development of an external knowledge base by employing the feature sources that can be acquired locally from the analyzed document. The results showed that the CRF model performed the best. Our study indicated that the nearest location and previous event location are the most important features for the CRF and SVM model, while the location extracted from the verb's subject is the most important to the Decision tree model.

关键词： support vector machine Spatial Attribute Noun Phrase Relative Clause support vector machine model

来源：评论

学校读者我要写书评

暂无评论

Expression profiling in canine osteosarcoma: identification of biomarkers and pathways associated with outcome

引用

BMC CANCER 2010年第1期10卷 506-506页

作者： O'Donoghue, Liza E. Ptitsyn, Andrey A. Kamstock, Debra A. Siebert, Janet Thomas, Russell S. Duval, Dawn L. Colorado State Univ Dept Clin Sci Ft Collins CO 80523 USA Colorado State Univ Dept Microbiol Immunol & Pathol Ft Collins CO 80523 USA CytoAnalytics Analyt Serv Denver CO USA Hamner Inst Hlth Sci Res Triangle Pk NC USA

Background: Osteosarcoma (OSA) spontaneously arises in the appendicular skeleton of large breed dogs and shares many physiological and molecular biological characteristics with human OSA. The standard treatment for OSA in both species is amputation or limb-sparing surgery, followed by chemotherapy. Unfortunately, OSA is an aggressive cancer with a high metastatic rate. Characterization of OSA with regard to its metastatic potential and chemotherapeutic resistance will improve both prognostic capabilities and treatment modalities. Methods: We analyzed archived primary OSA tissue from dogs treated with limb amputation followed by doxorubicin or platinum-based drug chemotherapy. Samples were selected from two groups: dogs with disease free intervals (DFI) of less than 100 days (n = 8) and greater than 300 days (n = 7). Gene expression was assessed with Affymetrix Canine 2.0 microarrays and analyzed with a two-tailed t-test. A subset of genes was confirmed using qRT-PCR and used in classification analysis to predict prognosis. Systems-based gene ontology analysis was conducted on genes selected using a standard J5 metric. The genes identified using this approach were converted to their human homologues and assigned to functional pathways using the GeneGo MetaCore platform. Results: Potential biomarkers were identified using gene expression microarray analysis and 11 differentially expressed (p < 0.05) genes were validated with qRT-PCR (n = 10/group). Statistical classification models using the qRT-PCR profiles predicted patient outcomes with 100% accuracy in the training set and up to 90% accuracy upon stratified cross validation. Pathway analysis revealed alterations in pathways associated with oxidative phosphorylation, hedgehog and parathyroid hormone signaling, cAMP/Protein Kinase A (PKA) signaling, immune responses, cytoskeletal remodeling and focal adhesion. Conclusions: This profiling study has identified potential new biomarkers to predict patient outcome in OSA

关键词： Pathway Analysis support vector machine model Poor Responder Hedgehog Pathway Hedgehog Signaling Pathway Pathway Analysis support vector machine model Poor Responder Hedgehog Pathway Hedgehog Signaling Pathway

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：