Significantly improved prediction of subcellular localization by integrating text and protein sequence data

Computational prediction of protein subcellular localization is a challenging problem. Several approaches have been presented during the past few years; some attempt to cover a wide variety of localizations, while others focus on a small number of localizations and on specific organisms. We present a comprehensive system, integrating protein sequence-derived data and text-based information. It is tested on three large data sets, previously used by leading prediction methods. The results demonstrate that our system performs significantly better than previously reported results, for a wide range of eukaryotic subcellular localizations. 1

A scientific-research activities information system

Cilj - Cilj istraživanja je razvoj modela, implementacija prototipa i verifikacija sistema za ekstrakciju metodologija iz naučnih članaka iz oblasti Informatike. Da bi se, pomoću tog sistema, naučnicima mogao obezbediti bolji uvid u metodologije u svojim oblastima potrebno je ekstrahovane metodolgije povezati sa metapodacima vezanim za publikaciju iz koje su ekstrahovani. Iz tih razloga istraživanje takoñe za cilj ima i razvoj modela sistema za automatsku ekstrakciju metapodataka iz naučnih članaka. Metodologija - Ekstrahovane metodologije se kategorizuju u četiri kategorije: kategorizuju se u četiri semantičke kategorije: zadatak (Task), metoda (Method), resurs/osobina (Resource/Feature) i implementacija (Implementation). Sistem se sastoji od dva nivoa: prvi je automatska identifikacija metodoloških rečenica; drugi nivo vrši prepoznavanje metodoloških fraza (segmenata). Zadatak ekstrakcije i kategorizacije formalizovan je kao problem označavanja sekvenci i upotrebljena su četiri zasebna Conditional Random Fields modela koji su zasnovani na sintaktičkim frazama. Sistem je evaluiran na ručno anotiranom korpusu iz oblasti Automatske Ekstrakcije Termina koji se sastoji od 45 naučnih članaka. Sistem za automatsku ekstrakciju metapodataka zasnovan je na klasifikaciji. Klasifikacija metapodataka vrši se u osam unapred definisanih sematičkih kategorija: Naslov, Autori, Pripadnost, Adresa, Email, Apstrakt, Ključne reči i Mesto publikacije. Izvršeni su eksperimenti sa svim standardnim modelima za klasifikaciju: naivni bayes, stablo odlučivanja, k-najbližih suseda i mašine potpornih vektora. Rezultati - Sistem za ekstrakciju metodologija postigao je sledeće rezultate: F-mera od 53% za identifikaciju Task i Method kategorija (sa preciznošću od 70%) dok su vrednosti za F-mere za Resource/Feature i Implementation kategorije bile 60% (sa preciznošću od 67%) i 75% (sa preciznošću od 85%) respektivno. Nakon izvršenih klasifikacionih eksperimenata, za sistem za ekstrakciju metapodataka, utvrñeno je da mašine potpornih vektora (SVM) pružaju najbolje performanse. Dobijeni rezultati SVM modela su generalno dobri, F-mera preko 85% kod skoro svih kategorija, a preko 90% kod većine. Ograničenja istraživanja/implikacije - Sistem za ekstrakciju metodologija, kao i sistem za esktrakciju metapodataka primenljivi su samo na naučne članke na engleskom jeziku. Praktične implikacije - Predloženi modeli mogu se, pre svega, koristiti za analizu i pregled razvoja naučnih oblasti kao i za kreiranje sematički bogatijih informacionih sistema naučno-istraživačke delatnosti. Originalnost/vrednost - Originalni doprinosi su sledeći: razvijen je model za ekstrakciju i semantičku kategorijzaciju metodologija iz naučnih članaka iz oblasti Informatike, koji nije opisan u postojećoj literaturi. Izvršena je analiza uticaja različitih vrsta osobina na ekstrakciju metodoloških fraza. Razvijen je u potpunosti automatizovan sistem za ekstrakciju metapodataka u informacionim sistemima naučno-istraživačke delatnosti.Purpose - The purpose of this research is model development, software prototype implementation and verification of the system for the identification of methodology mentions in scientific publications in a subdomain of automatic terminology extraction. In order to provide a better insight for scientists into the methodologies in their fields extracted methodologies should be connected with the metadata associated with the publication from which they are extracted. For this reason the purpose of this research was also a development of a system for the automatic extraction of metadata from scientific publications. Design/methodology/approach - Methodology mentions are categorized in four semantic categories: Task, Method, Resource/Feature and Implementation. The system comprises two major layers: the first layer is an automatic identification of methodological sentences; the second layer highlights methodological phrases (segments). Extraction and classification of the segments was 171 formalized as a sequence tagging problem and four separate phrase-based Conditional Random Fields were used to accomplish the task. The system has been evaluated on a manually annotated corpus comprising 45 full text articles. The system for the automatic extraction of metadata from scientific publications is based on classification. The metadata are classified eight pre-defined categories: Title, Authors, Affiliation, Address, Email, Abstract, Keywords and Publication Note. Experiments were performed with standard classification models: Decision Tree, Naive Bayes, K-nearest Neighbours and Support Vector Machines. Findings - The results of the system for methodology extraction show an Fmeasure of 53% for identification of both Task and Method mentions (with 70% precision), whereas the Fmeasures for Resource/Feature and Implementation identification was 60% (with 67% precision) and 75% (with 85% precision) respectively. As for the system for the automatic extraction of metadata Support Vector Machines provided the best performance. The Fmeasure was over 85% for almost all of the categories and over 90% for the most of them. Research limitations/implications - Both the system for the extractions of methodologies and the system for the extraction of metadata are only applicable to the scientific papers in English language. 172 Practical implications - The proposed models can be used in order to gain insight into a development of a scientific discipline and also to create semantically rich research activity information systems. Originality/Value - The main original contributions are: a novel model for the extraction of methodology mentions from scientific publications. The impact of the various types of features on the performance of the system was determined and presented. A fully automated system for the extraction of metadata for the rich research activity information systems was developed

Text Mining and Gene Expression Analysis Towards Combined Interpretation of High Throughput Data

Microarrays can capture gene expression activity for thousands of genes simultaneously and thus make it possible to analyze cell physiology and disease processes on molecular level. The interpretation of microarray gene expression experiments profits from knowledge on the analyzed genes and proteins and the biochemical networks in which they play a role. The trend is towards the development of data analysis methods that integrate diverse data types. Currently, the most comprehensive biomedical knowledge source is a large repository of free text articles. Text mining makes it possible to automatically extract and use information from texts. This thesis addresses two key aspects, biomedical text mining and gene expression data analysis, with the focus on providing high-quality methods and data that contribute to the development of integrated analysis approaches. The work is structured in three parts. Each part begins by providing the relevant background, and each chapter describes the developed methods as well as applications and results. Part I deals with biomedical text mining: Chapter 2 summarizes the relevant background of text mining; it describes text mining fundamentals, important text mining tasks, applications and particularities of text mining in the biomedical domain, and evaluation issues. In Chapter 3, a method for generating high-quality gene and protein name dictionaries is described. The analysis of the generated dictionaries revealed important properties of individual nomenclatures and the used databases (Fundel and Zimmer, 2006). The dictionaries are publicly available via a Wiki, a web service, and several client applications (Szugat et al., 2005). In Chapter 4, methods for the dictionary-based recognition of gene and protein names in texts and their mapping onto unique database identifiers are described. These methods make it possible to extract information from texts and to integrate text-derived information with data from other sources. Three named entity identification systems have been set up, two of them building upon the previously existing tool ProMiner (Hanisch et al., 2003). All of them have shown very good performance in the BioCreAtIvE challenges (Fundel et al., 2005a; Hanisch et al., 2005; Fundel and Zimmer, 2007). In Chapter 5, a new method for relation extraction (Fundel et al., 2007) is presented. It was applied on the largest collection of biomedical literature abstracts, and thus a comprehensive network of human gene and protein relations has been generated. A classification approach (Küffner et al., 2006) can be used to specify relation types further; e. g., as activating, direct physical, or gene regulatory relation. Part II deals with gene expression data analysis: Gene expression data needs to be processed so that differentially expressed genes can be identified. Gene expression data processing consists of several sequential steps. Two important steps are normalization, which aims at removing systematic variances between measurements, and quantification of differential expression by p-value and fold change determination. Numerous methods exist for these tasks. Chapter 6 describes the relevant background of gene expression data analysis; it presents the biological and technical principles of microarrays and gives an overview of the most relevant data processing steps. Finally, it provides a short introduction to osteoarthritis, which is in the focus of the analyzed gene expression data sets. In Chapter 7, quality criteria for the selection of normalization methods are described, and a method for the identification of differentially expressed genes is proposed, which is appropriate for data with large intensity variances between spots representing the same gene (Fundel et al., 2005b). Furthermore, a system is described that selects an appropriate combination of feature selection method and classifier, and thus identifies genes which lead to good classification results and show consistent behavior in different sample subgroups (Davis et al., 2006). The analysis of several gene expression data sets dealing with osteoarthritis is described in Chapter 8. This chapter contains the biomedical analysis of relevant disease processes and distinct disease stages (Aigner et al., 2006a), and a comparison of various microarray platforms and osteoarthritis models. Part III deals with integrated approaches and thus provides the connection between parts I and II: Chapter 9 gives an overview of different types of integrated data analysis approaches, with a focus on approaches that integrate gene expression data with manually compiled data, large-scale networks, or text mining. In Chapter 10, a method for the identification of genes which are consistently regulated and have a coherent literature background (Küffner et al., 2005) is described. This method indicates how gene and protein name identification and gene expression data can be integrated to return clusters which contain genes that are relevant for the respective experiment together with literature information that supports interpretation. Finally, in Chapter 11 ideas on how the described methods can contribute to current research and possible future directions are presented