检索结果-内蒙古大学图书馆

Effective DNA binding protein prediction by using key features via Chou's general PseAAC

JOURNAL OF THEORETICAL BIOLOGY 2019年 460卷 64-78页

作者： Adilina, Sheikh Farid, Dewan Md Shatabda, Swakkhar United Int Univ Dept Comp Sci & Engn Plot 2Madani Ave Dhaka 1212 Bangladesh

DNA-binding proteins (DBPs) are responsible for several cellular functions, starting from our immunity system to the transport of oxygen. In the recent studies, scientists have used supervised machine learning based methods that use information from the protein sequence only to classify the DBPs. Most of the methods work effectively on the train sets but performance of most of them degrades in the independent test set. It shows a room for improving the prediction method by reducing over-fitting. In this paper, we have extracted several features solely using the protein sequence and carried out two different types of feature selection on them. Our results have proven comparable on training set and significantly improved on the independent test set. On the independent test set our accuracy was 82.26% which is 1.62% improved compared to the previous best state-of-the-art methods. Performance in terms of sensitivity and area under receiver operating characteristic curve for the independent test set was also higher and they were 0.95 and 0.823 respectively. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： DNA binding proteins classification algorithm Sequence based features Feature selection Handling overfitting Independent test set

来源：评论

学校读者我要写书评

暂无评论

Identification of Human Protein Subcellular Location with Multiple Networks

引用

CURRENT PROTEOMICS 2022年第4期19卷 344-356页

作者： Wang, Rui Chen, Lei Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China

Background: Protein function is closely related to its location within the cell. Determination of protein subcellular location is helpful in uncovering its functions. However, traditional biological experiments to determine the subcellular location are of high cost and low efficiency, which cannot meet today's needs. In recent years, many computational models have been set up to identify the subcellular location of proteins. Most models use features derived from protein sequences. Recently, features extracted from the protein-protein interaction (PPI) network have become popular in studying various protein-related problems. Objective: A novel model with features derived from multiple PPI networks was proposed to predict protein subcellular location. Methods: Protein features were obtained by a newly designed network embedding algorithm, Mnode2vec, which is a generalized version of the classic Node2vec algorithm. Two classic classification algorithms: support vector machine and random forest, were employed to build the model. Results: Such model provided good performance and was superior to the model with features extracted by Node2vec. Also, this model outperformed some classic models. Furthermore, Mnode2vec was found to produce powerful features when the path length was small. Conclusion: The proposed model can be a powerful tool to determine protein subcellular location, and Mnode2vec can efficiently extract informative features from multiple networks.

关键词： Protein subcellular location multiple networks network embedding algorithm Mnode2vec node2vec classification algorithm

来源：评论

学校读者我要写书评

暂无评论

Patterns of brain structural connectivity differentiate normal weight from overweight subjects

引用

NEUROIMAGE-CLINICAL 2015年 7卷 506-517页

作者： Gupta, Arpana Mayer, Emeran A. Sanmiguel, Claudia P. Van Horn, John D. Woodworth, Davis Ellingson, Benjamin M. Fling, Connor Love, Aubrey Tillisch, Kirsten Labus, Jennifer S. Univ Calif Los Angeles Gail & Gerald Oppenheimer Family Ctr Neurobiol St IBOP Los Angeles CA USA Univ Calif Los Angeles David Geffen Sch Med Los Angeles CA 90095 USA Univ Calif Los Angeles Div Digest Dis Los Angeles CA USA Univ Calif Los Angeles Ahmanson Lovelace Brain Mapping Ctr Los Angeles CA USA Univ Calif Los Angeles GLA VHA Integrat Med Los Angeles CA USA Univ So Calif Keck Sch Med Inst Neuroimaging & Informat Los Angeles CA 90033 USA Univ Calif Los Angeles David Geffen Sch Med Radiol Los Angeles CA 90095 USA

Background: Alterations in the hedonic component of ingestive behaviors have been implicated as a possible risk factor in the pathophysiology of overweight and obese individuals. Neuroimaging evidence from individuals with increasing body mass index suggests structural, functional, and neurochemical alterations in the extended reward network and associated networks. Aim: To apply a multivariate pattern analysis to distinguish normal weight and overweight subjects based on gray and white-matter measurements. Methods: Structural images (N = 120, overweight N = 63) and diffusion tensor images (DTI) (N = 60, overweight N = 30) were obtained from healthy control subjects. For the total sample the mean age for the overweight group (females = 32, males = 31) was 28.77 years (SD = 9.76) and for the normal weight group (females = 32, males = 25) was 27.13 years (SD = 9.62). Regional segmentation and parcellation of the brain images was performed using Freesurfer. Deterministic tractography was performed to measure the normalized fiber density between regions. A multivariate pattern analysis approach was used to examine whether brain measures can distinguish overweight from normal weight individuals. Results: 1. White-matter classification: The classification algorithm, based on 2 signatures with 17 regional connections, achieved 97% accuracy in discriminating overweight individuals from normal weight individuals. For both brain signatures, greater connectivity as indexed by increased fiber density was observed in overweight compared to normal weight between the reward network regions and regions of the executive control, emotional arousal, and somatosensory networks. In contrast, the opposite pattern (decreased fiber density) was found between ventromedial prefrontal cortex and the anterior insula, and between thalamus and executive control network regions. 2. Gray-matter classification: The classification algorithm, based on 2 signatures with 42 morphological features, achi

关键词： Obesity Overweight Morphological gray-matter Anatomical white-matter connectivity Reward network Multivariate analysis classification algorithm

来源：评论

学校读者我要写书评

暂无评论

A computational platform to identify origins of replication sites in eukaryotes

引用

BRIEFINGS IN BIOINFORMATICS 2021年第2期22卷 1940-1950页

作者： Dao, Fu-Ying Lv, Hao Zulfiqar, Hasan Yang, Hui Su, Wei Gao, Hui Ding, Hui Lin, Hao Univ Elect Sci & Technol China Ctr Informat Biol Chengdu 610054 Peoples R China

The locations of the initiation of genomic DNA replication are defined as origins of replication sites (ORIs), which regulate the onset of DNA replication and play significant roles in the DNA replication process. The study of ORIs is essential for understanding the cell-division cycle and gene expression regulation. Accurate identification of ORIs will provide important clues for DNA replication research and drug development by developing computational methods. In this paper, the first integrated predictor named iORI-Euk was built to identify ORIs in multiple eukaryotes and multiple cell types. In the predictor, seven eukaryotic (Homo sapiens, Mus musculus, Drosophila melanogaster, Arabidopsis thaliana, Pichia pastoris, Schizosaccharomyces pombe and Kluyveromyces lactis) ORI data was collected from public database to construct benchmark datasets. Subsequently, three feature extraction strategies which are k-mer, binary encoding and combination of k-mer and binary were used to formulate DNA sequence samples. We also compared the different classification algorithms' performance. As a result, the best results were obtained by using support vector machine in 5-fold cross-validation test and independent dataset test. Based on the optimal model, an online web server called iORI-Euk (http://***/server/iO RI- Euk/) was established for the novel ORI identification.

关键词： origins of replication site eukaryote feature extraction webserver classification algorithm

来源：评论

学校读者我要写书评

暂无评论

A New algorithm to Classify the Homogeneity of ERS-2 Wave Mode SAR Imagette

引用

JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2014年第1期42卷 13-21页

作者： Song, Guiting Panda, Jagabandhu Zhang, Yanhui Chen, Haoliang Krishna, K. Muni Zhejiang Ocean Univ Dept Marine Sci Zhoushan 316000 Peoples R China Nanyang Technol Univ Sch Phys & Math Sci Singapore 639798 Singapore Shandong Agr Univ Coll Resources & Environm Tai An Shandong Peoples R China Shandong Univ Key Lab Agr Environm Jinan 250100 Peoples R China Singapore MIT Alliance Res & Technol Singapore Singapore Andhra Univ Dept Meteorol & Oceanog Visakhapatnam Andhra Pradesh India

A new classification parameter is developed using 1535 ERS-2 wave mode synthetic aperture radar (SAR) test imagettes to better differentiate homogeneous and inhomogeneous imagettes. The comparison between the new parameter (Min) and the previous one (Inhomo) (Schulz-Stellenfleth and Lehner, 2004) was done under varied threshold values of Inhomo. It is concluded that the performance of 'Min' is much better than 'Inhomo' when applying to the 1535 test imagettes. Furthermore, both Min and Inhomo are applied to nearly 1 million imagettes collected for the period from 1 September 1998 to 30 November 2000. The comparisons of the global inhomogeneous distribution between 'Min' and 'Inhomo' reveal that both the areas and percentage of inhomogeneity calculated by 'Min' are larger than that calculated by 'Inhomo'. By analyzing the low wind speed distribution of HOAPS data, we found that low wind speed over the ocean is one of the key reasons for the inhomogeneity of SAR imagettes.

关键词： Synthetic aperture radar imagette Homogeneity classification algorithm

来源：评论

学校读者我要写书评

暂无评论

Classifying for interval and applying for image based on the extracted texture feature

引用

GRANULAR COMPUTING 2024年第2期9卷 1-16页

作者： Nguyen-Thihong, Dan Vo-Van, Tai Vietnam Natl Univ Ho Chi Minh City Ho Chi Minh City Vietnam Ho Chi Minh City Univ Technol HCMUT Fac Appl Sci Ho Chi Minh City Vietnam Can Tho Univ Coll Nat Sci Can Tho Vietnam

This study develops a classification algorithm designed to handle interval data and to apply effectively in image processing. The proposed algorithm utilizes an innovative measure called overlap distance to assess the similarity between two intervals within multidimensional space. In addition, it integrates an improved method for determining prior probabilities by employing a fuzzy clustering technique. Furthermore, the study introduces a classification rule based on the quasi-Bayes method specifically tailored for interval data. According to this rule, an interval is assigned to a particular group if it holds the highest prior probability and the minimum distance to that group. The proposed algorithm is systematically presented in a step-by-step manner, elucidated by a numerical example, and executed using a well-established Matlab procedure. Another significant contribution of this study is its application to images, wherein texture features are extracted and represented as two-dimensional intervals. The effectiveness and superiority of the proposed algorithm are demonstrated through its application to various sets of medical images.

关键词： Interval data classification algorithm Prior probability Texture feature of image

来源：评论

学校读者我要写书评

暂无评论

Computerized Adaptive Test vs. decision trees: Development of a support decision system to identify suicidal behavior

引用

JOURNAL OF AFFECTIVE DISORDERS 2016年 206卷 204-209页

作者： Delgado-Gomez, D. Baca-Garcia, E. Aguado, D. Courtet, P. Lopez-Castroman, J. Carlos III Univ Dept Stat Madrid Spain IIS Jimenez Diaz Fdn CIBERSAM Dept Psychiat Madrid Spain Columbia Univ Dept Psychiat New York State Psychiat Inst New York NY USA Univ Autonoma Madrid Inst Ingn Conocimiento Madrid Spain CHRU Montpellier Montpellier France Univ Montpellier Montpellier France INSERM Unit 1061 Montpellier France CHU Nimes Nimes France

Background: Several Computerized Adaptive Tests (CATs) have been proposed to facilitate assessments in mental health. These tests are built in a standard way, disregarding useful and usually available information not included in the assessment scales that could increase the precision and utility of CATs, such as the history of suicide attempts. Methods: Using the items of a previously developed scale for suicidal risk, we compared the performance of a standard CAT and a decision tree in a support decision system to identify suicidal behavior. We included the history of past suicide attempts as a class for the separation of patients in the decision tree. Results: The decision tree needed an average of four items to achieve a similar accuracy than a standard CAT with nine items. The accuracy of the decision tree, obtained after 25 cross-validations, was 81.4%. A shortened test adapted for the separation of suicidal and non-suicidal patients was developed. Conclusion: CATs can be very useful tools for the assessment of suicidal risk. However, standard CATs do not use all the information that is available. A decision tree can improve the precision of the assessment since they are constructed using a priori information. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Suicide attempt classification algorithm Evaluation Screening Performance

来源：评论

学校读者我要写书评

暂无评论

Screening gene signatures for clinical response subtypes of lung transplantation

引用

MOLECULAR GENETICS AND GENOMICS 2022年第5期297卷 1301-1313页

作者： Zhang, Yu-Hang Li, Zhan Dong Zeng, Tao Chen, Lei Huang, Tao Cai, Yu-Dong Shanghai Univ Sch Life Sci Shanghai 200444 Peoples R China Harvard Med Sch Brigham & Womens Hosp Channing Div Network Med Boston MA 02115 USA Jilin Engn Normal Univ Coll Food Engn Changchun 130052 Peoples R China Chinese Acad Sci Univ Chinese Acad Sci Biomed Big Data Ctr Shanghai Inst Nutr & HlthCAS Key Lab Computat Bi Shanghai 200031 Peoples R China Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Chinese Acad Sci Univ Chinese Acad Sci Shanghai Inst Nutr & Hlth CAS Key Lab Tissue Microenvironm & Tumor Shanghai Peoples R China

Lung is the most important organ in the human respiratory system, whose normal functions are quite essential for human beings. Under certain pathological conditions, the normal lung functions could no longer be maintained in patients, and lung transplantation is generally applied to ease patients' breathing and prolong their lives. However, several risk factors exist during and after lung transplantation, including bleeding, infection, and transplant rejections. In particular, transplant rejections are difficult to predict or prevent, leading to the most dangerous complications and severe status in patients undergoing lung transplantation. Given that most common monitoring and validation methods for lung transplantation rejections may take quite a long time and have low reproducibility, new technologies and methods are required to improve the efficacy and accuracy of rejection monitoring after lung transplantation. Recently, one previous study set up the gene expression profiles of patients who underwent lung transplantation. However, it did not provide a tool to predict lung transplantation responses. Here, a further deep investigation was conducted on such profiling data. A computational framework, incorporating several machine learning algorithms, such as feature selection methods and classification algorithms, was built to establish an effective prediction model distinguishing patient into different clinical subgroups, corresponding to different rejection responses after lung transplantation. Furthermore, the framework also screened essential genes with functional enrichments and create quantitative rules for the distinction of patients with different rejection responses to lung transplantation. The outcome of this contribution could provide guidelines for clinical treatment of each rejection subtype and contribute to the revealing of complicated rejection mechanisms of lung transplantation.

关键词： Lung transplantation Gene signatures classification algorithm Feature selection

来源：评论

学校读者我要写书评

暂无评论

Identification of a Hidden movement by Using LLCPT algorithm

引用

NATIONAL ACADEMY SCIENCE LETTERS-INDIA 2024年 1-3页

作者： Sharma, Pharindra Kumar Sahu, Neeraj Sahu, Swatantra Kumar Jain, Neha Res Scientist PRAA Robot Res & Dev Labs Gwalior Madhya Pradesh India G H Raisoni Univ Dept Comp Sci & Engn Amravati Maharashtra India Mahatma Gandhi Chitrakoot Gramodaya Vishwavidyala Dept Phys Sci Satna Madhya Pradesh India

In this paper, proposed an algorithm with the name LLCPT, that will trace out the offender/offenders related to a crime. The idea used in this algorithm takes into consideration that, almost everybody uses mobile phones now a days be it basic phone or smart phone. This leads us to notice that, offender/s too may use or carry a mobile phone at or near the site of crime. The LLCPT algorithm works on the principle of earth position and calculate path of mobile.

关键词： Latitude Longitude GPS Mobile technology classification algorithm

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence Based Approach for classification of Human Activities Using MEMS Sensors Data

引用

SENSORS 2023年第3期23卷 1275页

作者： Khan, Yusuf Ahmed Imaduddin, Syed Singh, Yash Pratap Wajid, Mohd Usman, Mohammed Abbas, Mohamed Aligarh Muslim Univ Dept Elect Engn ZHCET Aligarh 202002 India King Khalid Univ Dept Elect Engn Abha 61411 Saudi Arabia King Khalid Univ Coll Engn Elect Engn Dept Abha 61421 Saudi Arabia Delta Univ Sci & Technol Coll Engn Elect & Commun Dept Gamasa 35712 Egypt

The integration of Micro Electronic Mechanical Systems (MEMS) sensor technology in smartphones has greatly improved the capability for Human Activity Recognition (HAR). By utilizing Machine Learning (ML) techniques and data from these sensors, various human motion activities can be classified. This study performed experiments and compiled a large dataset of nine daily activities, including Laying Down, Stationary, Walking, Brisk Walking, Running, Stairs-Up, Stairs-Down, Squatting, and Cycling. Several ML models, such as Decision Tree Classifier, Random Forest Classifier, K Neighbors Classifier, Multinomial Logistic Regression, Gaussian Naive Bayes, and Support Vector Machine, were trained on sensor data collected from accelerometer, gyroscope, and magnetometer embedded in smartphones and wearable devices. The highest test accuracy of 95% was achieved using the random forest algorithm. Additionally, a custom-built Bidirectional Long-Short-Term Memory (Bi-LSTM) model, a type of Recurrent Neural Network (RNN), was proposed and yielded an improved test accuracy of 98.1%. This approach differs from traditional algorithmic-based human activity detection used in current wearable technologies, resulting in improved accuracy.

关键词： Bi-LSTM network classification algorithm deep learning deep neural network human activity recognition machine learning MEMS sensors mobile & wearable devices recurrent neural network

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：