The Database of Genotypes and Phenotypes (dbGaP) was developed by National Center for Biotechnology Information (NCBI) to archive and distribute the results of various studies that have examined the interaction of genotype and phenotype. It is public repository for individual level phenotype, exposure, genotype, sequence data and the associations between them. Searching relevant studies of particular interest accurately and completely is challenging task due to keyword based search method of dbGaP Entrez system. Text mining is emerging research field which enable users to extract useful information from text documents and deals with retrieval, classification, clustering and machine learning techniques to classify different text document. In this work we proposed and implemented text classification (naïve bayes) and text clustering (K means) algorithm trained on dbGaP study text to identify heart,lung and blood studies. Classifiers performance compared with keyword based search result of dbGaP.It was determined that text classifiers are always best complement to document retrieval system of dbGaP. Keywords: Bioinformatics, Data Mining, Text classification, database of Genotypes and Phenotypes. Introduction 1.1 dbGaP The National Library of Medicine (NLM), part of the National Institutes of Health (NIH), announces the dbGaP, a new database designed to archive and distribute data from genome wide association studies. GWA studies discover the association between specific genes and observable traits, such as weight and blood pressure, or the presence or absence of a disease or condition (phenotype information). Connecting phenotype and genotype data gives information about the genes that may be involved in a disease process or c... ... middle of paper ... ...ext mining the pattern are extracted from natural language text rather than structural databases. Text mining, data mining and machine learning algorithms are in great demand in the field of bioinformatics. Text mining techniques applied to bioinformatics importantly involve methods like - Classification Text documents are arranged into groups of pre-labeled class. Learning schemes learn through training text documents and efficiency of these system is tested by using test text documents. Common algorithms include decision tree learning, naive Bayesian classification, nearest neighbor and neural network. This is called supervised learning. Clustering This is un-supervised learning method. Text documents here are unlabelled and inherent patterns in text are revealed through cluster formation. This can also be used as prior step for other text mining methods.
In conclusion, it is important for nurses to have proper training and information in the area of genetics and genomics so that it can be used in daily clinical practice (Thompson & Brooks, 2011). Using this information with clients and conducting a detailed genetic nursing assessment is a valuable component of being an effective health care provider and can help clients recognize, prevent, and/or treat diseases that are unique to their particular
Proteogenomics is a kind of science field that includes proteomics and genomics. Proteomic consists of protein sequence information and genomic consists of genome sequence information. It is used to annotate whole genome and protein coding genes. Proteomic data provides genome analysis by showing genome annotation and using of peptides that is gained from expressed proteins and it can be used to correct coding regions.Identities of protein coding regions in terms of function and sequence is more important than nucleotide sequences because protein coding genes have more function in a cell than other nucleotide sequences. Genome annotation process includes all experimental and computational stages.These stages can be identification of a gene ,function and structure of a gene and coding region locations.To carry out these processes, ab initio gene prediction methods can be used to predict exon and splice sites. Annotation of protein coding genes is very time consuming process ,therefore gene prediction methods are used for genome annotations. Some web site programs provides these genome annotations such as NCBI and Ensembl. These tools shows sequenced genomes and gives more accurate gene annotations. However, these tools may not explain the presence of a protein. Main idea of proteogenomic methods is to identify peptides in samples by using these tools and also with the help of mass spectrometry.Mass spectrometry searches translation of genome sequences rather than protein database searching. This method also annotate protein protein interactions.MS/MS data searching against translation of genome can determine and identify peptide sequences.Thus genome data can be understood by using genomic and transcriptomic information with this proteogenomic methods and tools. Many of proteomic information can be achieved by gene prediction algorithms, cDNA sequences and comparative genomics. Large proteomic datasets can be gained by peptide mass spectrophotometry for proteogenomics because it uses proteomic data to annotate genome. If there is genome sequence data for an organism or closely related genomes are present,proteogenomic tools can be used. Gained proteogenomic data provides comparing of these data between many related species and shows homology relationships among many species proteins to make annotations with high accuracy.From these studies, proteogenomic data demonstrates frame shifts regions, gene start sites and exon and intron boundaries , alternative splicing sites and its detection , proteolytic sites that is found in proteins, prediction of genes and post translational modification sites for protein.
This book is about the amazing task of mapping and showing all the sequences of the thousands and thousands of genes in the human body. The book is split up into nine chapters each of which covers a different aspect of this incredible project. The book tells all about almost every aspect of the project. It tells all about the project and what the point is, what has been accomplished so far, and when they expect it to be finished. According to the introduction the project is actually expected to be finished sometime this year.
In April 2003, researchers successfully completed the Human Genome Project, more than two years ahead of schedule. The Human Genome Project has already led to the discovery of more than 1,800 genes that cause disease (“NIH Fact Sheets…”). As a result of the Human Genome Project, researchers can find a gene suspected of causing an inherited disease in a matter of days, rather than the years it would have taken before. “One major step was the development of the HapMap. The HapMap is a catalog of common genetic differences in the human genome. The HapMap has accelerated the search for genes that have a say in common human disease, and have already produced results in finding genetic factors involved in conditions ranging from age-related blindness to obesity”(NIH Fact Sheet). The Can...
There are many different techniques involved in gene screening. With the start of the Human Genome Mapping Project some of these techniques have been altered to speed up the screening process. Examples of these techniques include PCR (polymerize chain reaction), RFLP's (restricti...
To begin discussion about the HGP, we first must understand what it is. It is a massive undertaking of collaboration of geneticists that begin in 1990. Their goals are to identify all the estimated 80,000 to 100,000 genes in human DNA and determine the sequences of 3 billion bases composed of adenine, thymine, cytosine, and guanine. The project is being funded jointly by the Department of Energy and the National Institute of Health. This massive undertaking is estimated at a cost of three billion dollars, with the most current target date for the project's completion at the year 2003. They will then store this information in a centralized database so it can be used as tools for their analysis. Also as a first for science, they are going to address the logical, ethical, and social issues that the project will give rise to.
Support Vector Machine(SVM): Over the past several years, there has been a significant amount of research on support vector machines and today support vector machine applications are becoming more common in text classification. In essence, support vector machines define hyperplanes, which try to separate the values of a given target field. The hyperplanes are defined using kernel functions. The most popular kernel types are supported: linear, polynomial, radial basis and sigmoid. Support Vector Machines can be used for both, classification and regression. Several characteristics have been observed in vector space based methods for text classification [15,16], including the high dimensionality of the input space, sparsity of document vectors, linear separability in most text classification problems, and the belief that few features are relevant.
Document clustering is the process of organizing a particular electronic corpus of documents into subgroups of similar text features. Previously, a number of statistical algorithms had been applied to perform clustering to the data including the text documents. There are recent endeavors to enhance the performance of the clustering with the optimization based algorithms such as the evolutionary algorithms. Thus, document clustering with evolutionary algorithms became an emerging topic that gained more attention in the recent years. This paper presents an up-to-date review fully devoted to evolutionary algorithms designed for document clustering. Its firstly provides comprehensive inspection to the document clustering model revealing its various components and related concepts. Then it shows and analyzes the principle research work in this topic. Finally, it brings together and classifies various objective functions from the collection of research papers. The paper ends up by addressing some important issues and challenges that can be subject of future work.
In 2003, et al. Jerome R. Bellegarda, showed the conventional mail filtering techniques based on unsupervised learning where the classification is done on the basis keyword matching. But if spammers change the tricks of spam mails framing than the old classifiers will than not able to give the accurate results. That is the worst part of the unsupervised learning. On the other hand, in the same paper, machine learning techniques based on supervised learning is introduced where the classifiers are regularly fed with the changing patterns of spam mails with different data sets[15].
...fman R. A. - "Data Mining and Knowledge Discovery" - A Review of issues and Multi- strategy Approach". Reports of the Machine Learning and Inference Laboratory, MCI 97-2, George Mason University, Fairfax, V.A. 1997. http://www.mli.gmu.edu/~kaufman/97-1.ps
... applied on different Domain data sets and sub level data sets. The data sets are applied on Maximum entropy, Support Vector Machine Method, Multinomial naïve bayes algorithms, I got 60-70% of accuracy. The above is also applied for the Unigrams of Maximum entropy, Support Vector Machine Method, Multinomial naïve bayes algorithms achieved an accuracy of 65-75%. Applied the same data on proposed lexicon Based Semantic Orientation Analysis Algorithm, we received better accuracy of 85%. In subjective Feature Relation Networks Chi-square model using n-grams, POS tagging by applying linguistic rules performed with highest accuracy of 80% to 93% significantly better than traditional naïve bayes with unigram model. The after applying proposed model on different sets the results are validated with test data and proved our methods are more accurate than the other methods.
The first step is collecting information from multiple sources, such as online documents, databases, etc. Before indexing the information, several pre-processes are required:
Machine learning is one of the part of Computer science that gives computers ability to learn without being explicitly programmed. Which evolve from study of pattern recognition and computational learning theory in artificial intelligence plus It explores the study and construction of algorithms that can learn from and make predictions or decision.
Machine learning systems can be categorized according to many different criteria. We will discuss three criteria: Classification on the basis of the underlying learning strategies used, Classification on the basis of the representation of knowledge or skill acquired by the learner and Classification in terms of the application domain of the performance system for which knowledge is acquired.
Expressed sequence tags (ESTs) are short, unverified nucleotide fragment usually of 200-800 nucleotide bases. It is randomly selected by single-pass sequencing of either the 5’- or 3’-end of cDNA derived from cDNA libraries that constructed based on mRNA of specific gene. EST data sets has been recognized as the ‘poor man’s genome’ because EST data are widely used as a substitute to the genome sequencing.