5 research outputs found
Detection of Molecular Paths Associated with Insulitis and Type 1 Diabetes in Non-Obese Diabetic Mouse
Recent clinical evidence suggests important role of lipid and amino acid metabolism in early pre-autoimmune stages of type 1 diabetes pathogenesis. We study the molecular paths associated with the incidence of insulitis and type 1 diabetes in the Non-Obese Diabetic (NOD) mouse model using available gene expression data from the pancreatic tissue from young pre-diabetic mice. We apply a graph-theoretic approach by using a modified color coding algorithm to detect optimal molecular paths associated with specific phenotypes in an integrated biological network encompassing heterogeneous interaction data types. In agreement with our recent clinical findings, we identified a path downregulated in early insulitis involving dihydroxyacetone phosphate acyltransferase (DHAPAT), a key regulator of ether phospholipid synthesis. The pathway involving serine/threonine-protein phosphatase (PP2A), an upstream regulator of lipid metabolism and insulin secretion, was found upregulated in early insulitis. Our findings provide further evidence for an important role of lipid metabolism in early stages of type 1 diabetes pathogenesis, as well as suggest that such dysregulation of lipids and related increased oxidative stress can be tracked to beta cells
Data integration, pathway analysis and mining for systems biology
Post-genomic molecular biology embodies high-throughput experimental techniques and hence is a data-rich field. The goal of this thesis is to develop bioinformatics methods to utilise publicly available data in order to produce knowledge and to aid mining of newly generated data. As an example of knowledge or hypothesis generation, consider function prediction of biological molecules. Assignment of protein function is a non-trivial task owing to the fact that the same protein may be involved in different biological processes, depending on the state of the biological system and protein localisation. The function of a gene or a gene product may be provided as a textual description in a gene or protein annotation database. Such textual descriptions lack in providing the contextual meaning of the gene function. Therefore, we need ways to represent the meaning in a formal way. Here we apply data integration approach to provide rich representation that enables context-sensitive mining of biological data in terms of integrated networks and conceptual spaces. Context-sensitive gene function annotation follows naturally from this framework, as a particular application. Next, knowledge that is already publicly available can be used to aid mining of new experimental data. We developed an integrative bioinformatics method that utilises publicly available knowledge of protein-protein interactions, metabolic networks and transcriptional regulatory networks to analyse transcriptomics data and predict altered biological processes. We applied this method to a study of dynamic response of Saccharomyces cerevisiae to oxidative stress. The application of our method revealed dynamically altered biological functions in response to oxidative stress, which were validated by comprehensive in vivo metabolomics experiments. The results provided in this thesis indicate that integration of heterogeneous biological data facilitates advanced mining of the data. The methods can be applied for gaining insight into functions of genes, gene products and other molecules, as well as for offering functional interpretation to transcriptomics and metabolomics experiments
Finite state models in information extraction
Disertacija je posvećena istraživanju naučne oblasti nazvane ekstrakcija
informacija (engl. information extraction), koja predstavlja podoblast veštačke
inteligencije, a u sebi kombinuje i koristi tehnike i dostignuća više različitih oblasti
računarstva. Termin "ekstrakcija informacija" će biti korišćen u dva različita konteksta.
U jednom od njih misli se na ekstrakciju informacije kao naučnu oblast i tada će se
koristiti skraćenica IE, preuzeta iz anglosaksonske literature u značenju "Information
Extraction". U drugom slučaju, kada se bude mislilo na sam proces i postupak
izdvajanja informacija iz teksta, koristiće se oblik "ekstrakcija informacija".
Ova disertacija predstavlja, pored pregleda postojećih metoda iz ove oblasti, i
jedan originalni pristup i metod za ekstrakciju informacija baziran na konačnim
transduktorima. Tokom istraživanja i rada na disertaciji, a primenom pomenutog
metoda, kao rezultat formirana je baza podataka o mikroorganizmima koja sadrži
fenotipske i genotipske karakteristike za 2412 vrsta i 873 rodova, namenjena za
istraživanja iz oblasti bioinformatike i genetike. Baza i korišćeni metod su detaljno
prikazani u nekoliko radova, publikovanih u časopisima ili izlaganih na međunarodnim
konferencijama (Pajić, 2011; Pajić i sar. 2011a; Pajić i sar. 2011b)
U glavi 1 dat je uvod u oblast ekstrakcije informacije, unutar koga je opisan
istorijat i razvoj metoda ove oblasti. Dalje je opisana klasifikacija tekstualnih resursa
nad kojima se vrši ekstrakcija informacija, kao i klasifikacija samih informacija. Na
kraju glave 1 oblast ekstrakcije informacije je upoređena sa drugim srodnim
disciplinama računarstva.
Glava 2 je posvećena prikazu teorijskih osnova na kojima su zasnovana
istraživanja ove disertacije. Razmatrana je teorija formalnih jezika i modela konačnih
stanja, kao i njihova uzajamna veza i veza sa ekstrakcijom informacija. Akcenat je
stavljen na konačne modele i metode koji su zasnovani na modelima konačnih stanja.
Ovi metodi pokazuju veću preciznost od drugih metoda za ekstrakciju informacije, te su
nezamenljivi u situacijama kada je tačnost izdvojenih podataka iz teksta od presudnog
značaja. Pojedini pojmovi ekstrakcije informacija - jezik relevantnih informacija, jezik
izdvojenih informacija, pravila ekstrakcije, definisani su iz ugla teorije formalnih jezika.
Formulisano je i dokazano osnovno svojstvo relacije transdukcije za zadato pravilo
ekstrakcije. Definisan je i pojam jezika konteksta informacija i dokazano je njegovo
svojstvo regularnosti...This dissertation is on research and studying in scientific field called
information extraction, which can be seen as a sub-area of artificial intelligence and
which combines and uses techniques and achievements of several computer science
areas. The term „information extraction“ will be used in two different contexts. In the
first one, the term will refer to the scientific area and the acronym IE will be used in that
case. In the second case, this term will refer to the very process of extracting
information.
Beside the IE state-of-the-art survey, an original approach and a method for
information extraction based on finite state transducers are presented. A database with
microbial phenotype and genotype characteristics, for 2412 species and 873 genera has
been created, as a result of the research and the work on the dissertation. The database is
intended for research, in bioinformatics and genetics. The method used for the creation
of the database and the database itself are described in details and published in several
journals and conference proceedings (Pajić, 2011; Pajić et al. 2011a; Pajić et al. 2011b).
In the Section 1, the introduction to IE is given, together with the history of
development of methods in this area. The classification of textual resources that are
used for information extraction and classification of the information itself are described.
At the end of the Section 1, IE is compared with other related disciplines of computer
science.
Section 2 contains some excerpts from formal language theory and abstract
automata, on which the dissertation is based. The mutual relationship between these two
areas and their connection with IE are described. The emphasis is put on the final state
models and methods based on them. These methods show higher precision than other
methods for extracting information, and are indispensable in situations where the
accuracy of data extracted from the text is of crucial importance. Some specific terms of
information extraction - the language of the relevant information, the language of
extracted information and extraction rules, are defined from the perspective of formal
language theory. The basic feature of the transduction relation for the given rule
extraction is formulated and proved. The language of information context is defined and
its regularilty is proven..
Lipoprotein ontology: a formal representation of Lipoproteins
Lipoproteins serve as a mode of transport for the uptake, storage and metabolism of lipids. Dysregulation in lipoprotein metabolism, known as dyslipidaemia, is strongly correlated to various diseases such as cardiovascular disease. Lipoprotein Ontology provides a formal representation of lipoprotein concepts and relationships that can be used to support the intelligent retrieval of information, faciliate collaboration between research groups, and provide the basis for the development of tools for the diagnosis and treatment of dyslipidaemia