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Prediction of microRNA-regulated protein interaction pathways in Arabidopsis using machine learning algorithms

COMPUTERS IN BIOLOGY AND MEDICINE 2013年第11期43卷 1645-1652页

作者： Kurubanjerdjit, Nilubon Huang, Chien-Hung Lee, Yu-Liang Tsai, Jeffrey J. P. Ng, Ka-Lok Asia Univ Dept Biomed Informat Taichung 41354 Taiwan Mea Fah Luang Univ Sch Informat Technol Chiang Rai 57100 Thailand Natl Formosa Univ Dept Comp Sci & Informat Engn Yunlin 63205 Taiwan China Med Univ Sch Pharm Taichung 40402 Taiwan

MicroRNAs are small, endogenous RNAs found in many different species and are known to have an influence on diverse biological phenomena. They also play crucial roles in plant biological processes, such as metabolism, leaf sidedness and flower development. However, the functional roles of most microRNAs are still unknown. The identification of closely related microRNAs and target genes can be an essential first step towards the discovery of their combinatorial effects on different cellular states. A lot of research has tried to discover microRNAs and target gene interactions by implementing machine learning classifiers with target prediction algorithms. However, high rates of false positives have been reported as a result of undetermined factors which will affect recognition. Therefore, integrating diverse techniques could improve the prediction. In this paper we propose identifying microRNAs target of Arabidopsis thaliana by integrating prediction scores from PITA, miRanda and RNAHybrid algorithms used as a feature vector of microRNA-target interactions, and then implementing SVM, random forest tree and neural network machine learning algorithms to make final predictions by majority voting. Furthermore, microRNA target genes are linked with their protein-protein interaction (PPI) partners. We focus on plant resistance genes and transcription factor information to provide new insights into plant pathogen interaction networks. Downstream pathways are characterized by the Jaccard coefficient, which is implemented based on Gene Ontology. The database is freely accessible at http://***/At_miRNA/. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Arabidopsis thaliana microRNA target prediction machine learning algorithm Protein-protein interaction Enrichment analysis Jacard coefficient

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A Novel Data Mining Method on Falling Detection and Daily Activities Recognition

A Novel Data Mining Method on Falling Detection and Daily Ac...

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International Conference on Tools with Artificial Intelligence

作者： Yingli Peng Tingting Zhang Li Sun Jie Chen School of Computer Science and Technology Donghua University Shanghai P.R.China Department of Information Technology and Media Mid Sweden University Sundsvall Sweden

ISBN: (纸本)9781509001644

With the intensification of aging population, a growing number of elderly people have to live alone due to domestic and social reasons. Falling becomes one of the most crucial factors in threatening the elderly's lives, which is always difficult to be detected as it is instantaneous and easy to be confused with other motions, such as lying down. In this paper, a new method is proposed for accurate falling detection and activities recognition. It applies hierarchical classifiers to the time series data set including eleven activities of daily living (ADLs), collected by four wearable sensors. The new method combines two machine learning algorithms, performs concrete analysis on the original outcome and then obtains several scarcely-confused groups separately. The experiment indicates that the new method improves the accuracy of classification to a larger extent, reached to more than 90%. Furthermore, the matched algorithm for applying these classifiers, called Hierarchical Classifier algorithm (HCA), is proposed as well.

关键词： Falling detection wearable sensor machine learning algorithm Hierarchical Classifier algorithm (HCA)

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Cancer survival classification using integrated data sets and intermediate information

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2014年第1期62卷 23-31页

作者： Kim, Shinuk Park, Taesung Kon, Mark Sangmyung Univ Coll Liberal Arts Cheonan 330729 Chungnam South Korea Seoul Natl Univ Dept Stat Seoul 151747 South Korea Boston Univ Dept Math & Stat Boston MA 02215 USA

Objective: Although numerous studies related to cancer survival have been published, increasing the prediction accuracy of survival classes still remains a challenge. Integration of different data sets, such as microRNA (miRNA) and mRNA, might increase the accuracy of survival class prediction. Therefore, we suggested a machine learning (ML) approach to integrate different data sets, and developed a novel method based on feature selection with Cox proportional hazard regression model (FSCOX) to improve the prediction of cancer survival time. Methods: FSCOX provides us with intermediate survival information, which is usually discarded when separating survival into 2 groups (short- and long-term), and allows us to perform survival analysis. We used an ML-based protocol for feature selection, integrating information from miRNA and mRNA expression profiles at the feature level. To predict survival phenotypes, we used the following classifiers, first, existing ML methods, support vector machine (SVM) and random forest (RF), second, a new median-based classifier using FSCOX (FSCOX_median), and third, an SVM classifier using FSCOX (FSCOX_SVM). We compared these methods using 3 types of cancer tissue data sets: (i) miRNA expression, (ii) mRNA expression, and (iii) combined miRNA and mRNA expression. The latter data set included features selected either from the combined miRNA/mRNA profile or independently from miRNAs and mRNAs profiles (IFS). Results: In the ovarian data set, the accuracy of survival classification using the combined miRNA/mRNA profiles with IFS was 75% using RF, 86.36% using SVM, 84.09% using FSCOX_median, and 88.64% using FSCOX_SVM with a balanced 22 short-term and 22 long-term survivor data set. These accuracies are higher than those using miRNA alone (70.45%, RF;75%, SVM;75%, FSCOX_median;and 75%, FSCOX_SVM) or mRNA alone (65.91%, RF;63.64%, SVM;72.73%, FSCOX_median;and 70.45%, FSCOX_SVM). Similarly in the glioblastoma multiforme data, the accuracy of m

关键词： machine learning algorithm Integration of data sets Intermediate information Survival time classification

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Gaussian processes retrieval of leaf parameters from a multi-species reflectance, absorbance and fluorescence dataset

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JOURNAL OF PHOTOCHEMISTRY AND PHOTOBIOLOGY B-BIOLOGY 2014年 134卷 37-48页

作者： Van Wittenberghe, Shari Verrelst, Jochem Rivera, Juan Pablo Alonso, Luis Moreno, Jose Samson, Roeland Univ Antwerp Fac Sci Dept Biosci B-2020 Antwerp Belgium Univ Valencia Image Proc Lab E-46980 Valencia Spain

Biochemical and structural leaf properties such as chlorophyll content (Chl), nitrogen content (N), leaf water content (LWC), and specific leaf area (SLA) have the benefit to be estimated through nondestructive spectral measurements. Current practices, however, mainly focus on a limited amount of wavelength bands while more information could be extracted from other wavelengths in the full range (400-2500 nm) spectrum. In this research, leaf characteristics were estimated from a field-based multi-species dataset, covering a wide range in leaf structures and Chl concentrations. The dataset contains leaves with extremely high Chl concentrations (>100 mu g cm(-2)), which are seldom estimated. Parameter retrieval was conducted with the machine learning regression algorithm Gaussian Processes (GP), which is able to perform adaptive, nonlinear data fitting for complex datasets. Moreover, insight in relevant bands is provided during the development of a regression model. Consequently, the physical meaning of the model can be explored. Best estimates of SLA, LWC and Chl yielded a best obtained normalized root mean square error of 6.0%, 7.7%, 9.1%, respectively. Several distinct wavebands were chosen across the whole spectrum. A band in the red edge (710 nm) appeared to be most important for the estimation of Chl. Interestingly, spectral features related to biochemicals with a structural or carbon storage function (e.g. 1090, 1550, 1670, 1730 nm) were found important not only for estimation of SLA, but also for LWC, Chl or N estimation. Similar, Chl estimation was also helped by some wavebands related to water content (950, 1430 nm) due to correlation between the parameters. It is shown that leaf parameter retrieval by GP regression is successful, and able to cope with large structural differences between leaves. (C) 2014 Elsevier B.V. All rights reserved.

关键词： machine learning algorithm Spectral features Hyperspectral Parameter retrieval Chlorophyll Leaf structure Specific leaf area Leaf water content

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Bioinformatics approaches for improved recombinant protein production in Escherichia coli: protein solubility prediction

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BRIEFINGS IN BIOINFORMATICS 2014年第6期15卷 953-962页

作者： Chang, Catherine Ching Han Song, Jiangning Tey, Beng Ti Ramanan, Ramakrishnan Nagasundara Monash Univ Dept Biochem & Mol Biol Bandar Sunway 46150 Selangor Malaysia Chinese Acad Sci Tianjin Inst Ind Biotechnol Beijing 100864 Peoples R China Monash Univ Chem Engn Discipline Sch Engn Bandar Sunway 46150 Selangor Malaysia

The solubility of recombinant protein expressed in Escherichia coli often represents the production yield. However, up-to-date, instances of successful production of soluble recombinant proteins in E. coli expression system with high yield remain scarce. This is mainly due to the difficulties in improving the overall production capacity, as most of the well-established strategies usually involve a series of trial and error steps with unguaranteed success. One way to concurrently improve the production yield and minimize the production cost would be incorporating the potency of bioinformatics tools to conduct in silico studies, which forecasts the outcome before actual experimental work. In this article, we review and compare seven prediction tools available, which predict the solubility of protein expressed in E. coli, using the following criteria: prediction performance, usability, utility, prediction tool development and validation methodologies. This comprehensive review will be a valuable resource for researchers with limited prior experience in bioinformatics tools. As such, this will facilitate their choice of appropriate tools for studies related to enhancement of intracellular recombinant protein production in E. coli.

关键词： protein solubility heterologous expression in silico prediction intracellular expression machine learning algorithm prediction model

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Emotions for Accessibility Introduction to the Special Thematic Session

Emotions for Accessibility Introduction to the Special Thema...

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14th International Conference on Computers Helping People with Special Needs (ICCHP)

作者： Mohamad, Yehya Fraunhofer Inst Appl Informat Technol FIT St Augustin Germany

ISBN: (纸本)9783319085968;9783319085951

This STS on Emotions for Accessibility is targeted towards the description of affective computing approaches e.g. emotional state detection, emotional state elicitation and their potential usage in the context of users who are disabled or older people. These user groups may gain a lot of benefits from affective computing systems e.g. in support of communication especially none verbal, management of emotional states or stress, or just improving therapy procedures etc.

关键词： Affective computing Accessibility Sensors GSR PVB machine learning algorithm Disabled person Context Emotion Emotion management User interface e-learning Web-based

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A comprehensive study of named entity recognition in Chinese clinical text

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2014年第5期21卷 808-814页

作者： Lei, Jianbo Tang, Buzhou Lu, Xueqin Gao, Kaihua Jiang, Min Xu, Hua Peking Univ Ctr Med Informat Beijing 100871 Peoples R China Univ Texas Sch Biomed Informat Houston Houston TX USA Shenzhen Grad Sch Harbin Inst Technol Dept Comp Sci Shenzhen Guangdong Peoples R China

Objective Named entity recognition (NER) is one of the fundamental tasks in natural language processing. In the medical domain, there have been a number of studies on NER in English clinical notes;however, very limited NER research has been carried out on clinical notes written in Chinese. The goal of this study was to systematically investigate features and machine learning algorithms for NER in Chinese clinical text. Materials and methods We randomly selected 400 admission notes and 400 discharge summaries from Peking Union Medical College Hospital in China. For each note, four types of entity clinical problems, procedures, laboratory test, and medications were annotated according to a predefined guideline. Two-thirds of the 400 notes were used to train the NER systems and one-third for testing. We investigated the effects of different types of feature including bag-of-characters, word segmentation, part-of-speech, and section information, and different machine learning algorithms including conditional random fields (CRF), support vector machines (SVM), maximum entropy (ME), and structural SVM (SSVM) on the Chinese clinical NER task. All classifiers were trained on the training dataset and evaluated on the test set, and micro-averaged precision, recall, and F-measure were reported. Results Our evaluation on the independent test set showed that most types of feature were beneficial to Chinese NER systems, although the improvements were limited. The system achieved the highest performance by combining word segmentation and section information, indicating that these two types of feature complement each other. When the same types of optimized feature were used, CRF and SSVM outperformed SVM and ME. More specifically, SSVM achieved the highest performance of the four algorithms, with F-measures of 93.51% and 90.01% for admission notes and discharge summaries, respectively.

关键词： Medical concept recognition Chinese clinic notes Natural language processing machine learning algorithm

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An Improved K-nearest-neighbor algorithm using Genetic algorithm for Sentiment Classification

An Improved K-nearest-neighbor algorithm using Genetic Algor...

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IEEE International Conference on Circuits, Power and Computing Technologies (ICCPCT)

作者： Kalaivani, P. Shunmuganathan, K. L. Sathyabama Univ Dept CSE Chennai Tamil Nadu India RMK Engn Coll Chennai Tamil Nadu India

ISBN: (纸本)9781479923977

Sentiment classification is to find the polarity of product or user reviews. Supervised machine learning algorithms are used for opinion mining such as Navie Bayes, K-nearest neighbor and Support vector machine. KNN is simple algorithm but less efficient classification algorithm. In this paper we propose an improved KNN algorithm, genetic algorithm is developed which is a hybrid genetic algorithm that incorporates the information gain for feature selection and combined with KNN to improve its classification performance. Specifically, we compared other supervised machine learning approaches such as Navie Bayes and traditional kNN for Sentiment Classification of movie reviews and book reviews. The experimental results using genetic algorithm with improved indicate high performance levels with Fmeasure of over 87% on the movie reviews.

关键词： Opinion mining sentiment classification machine learning algorithm genetic algorithm features selection

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Recognition and Extraction of Honorifics in Chinese Diachronic Corpora 15

Recognition and Extraction of Honorifics in Chinese Diachron...

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15th Chinese Lexical Semantics Workshop (CLSW)

作者： Xiong, Dan Xu, Jian Lu, Qin Lo, Fengju Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China Yuan Ze Univ Dept Chinese Linguist & Literature Zhongli Taiwan

ISBN: (纸本)9783319143316;9783319143309

Honorifics in this paper refer to names of official positions and titles of nobility or honor. They can be found in various written records in different periods and have great historical significance. This paper introduces a machine learning system to recognize the honorifics in diachronic corpora. A tagged corpus of four classic novels written in the Ming and Qing dynasties is used to train the system. The system is then used to automatically recognize and extract the honorifics in pre-Qin classics, Tang-dynasty poems, and modern Chinese news. Experimental results show that the system can achieve relatively good results in recognizing the honorifics in the pre-Qin classics and Tang-dynasty poems. This work is an attempt to improve the performance of automatic recognition of honorifics in diachronic corpora. The system can be a helpful tool in the studies on the evolution of honorifics throughout Chinese history.

关键词： Honorifics Chinese diachronic corpora machine learning algorithm

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Differential Evolution based Multiobjective Optimization for Biomedical Entity Extraction 3

Differential Evolution based Multiobjective Optimization for...

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3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Sikdar, Utpal Kumar Ekbal, Asif Saha, Sriparna Indian Inst Technol Dept CSE Patna Bihar India

ISBN: (纸本)9781479930807

In this paper, we propose multi-objective differential evolution (DE) based feature selection and ensemble learning techniques for biomedical entity extraction. The algorithm operates in two layers, first step of which concerns with the problem of automatic feature selection for a machine learning algorithm, namely Conditional Random Field (CRF). The solutions of the final best population provides different feature combinations. The classifiers generated with these feature representations are combined together using a multi-objective differential based ensemble technique. We evaluate the proposed algorithm for named entity (NE) extraction in biomedical text. Experiments on the benchmark setup yield recall, precision and F-measure values of 73.50%, 77.02% and 75.22%, respectively.

关键词： evolutionary computation feature extraction feature selection learning (artificial intelligence) medical computing pattern classification random processes text analysis CRF DE based feature selection NE extraction automatic feature selection biomedical entity extraction biomedical text classifiers conditional random field differential evolution based multiobjective optimization ensemble learning techniques feature combinations feature representations machine learning algorithm multiobjective differential based ensemble technique multiobjective differential evolution named entity Biological cells Feature extraction Linear programming Optimization Sociology Statistics Vectors

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