Systematic Search for Recipes to Generate Induced Pluripotent Stem Cells

Generation of induced pluripotent stem cells (iPSCs) opens a new avenue in regenerative medicine. One of the major hurdles for therapeutic applications is to improve the efficiency of generating iPSCs and also to avoid the tumorigenicity, which requires searching for new reprogramming recipes. We present a systems biology approach to efficiently evaluate a large number of possible recipes and find those that are most effective at generating iPSCs. We not only recovered several experimentally confirmed recipes but we also suggested new ones that may improve reprogramming efficiency and quality. In addition, our approach allows one to estimate the cell-state landscape, monitor the progress of reprogramming, identify important regulatory transition states, and ultimately understand the mechanisms of iPSC generation

Biomedic Organizations: An intelligent dynamic architecture for KDD

The application of information technology in the field of biomedicine has become increasingly important over the last several years. This study presents the Intelligent Biomedic Organizations (IBOs) model, an intelligent dynamic architecture for knowledge discovery in biomedical databases. It involves an organizational model specially designed to support medical personnel in their daily tasks and to establish an innovative intelligent system to make classifications and predictions with huge volumes of information. IBO is based on a multi-agent architecture with Web service integration capability. The core of the system is a type of agent that integrates a novel strategy based on a case-based planning mechanism for automatic reorganization. This agent proposes a new reasoning agent model, where the complex processes are modeled as external services. In this sense, the agents act as coordinators of Web services that implement the four stages of the case-based planning cycle. The multi-agent system has been implemented in a real scenario to classify leukemia patients, and the classification strategy includes services such as a novel ESOINN neural network and statistical methods to analyze patient data. The results obtained are presented within this paper and demonstrate the effectiveness of the proposed organizational model

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..