A polynomial time algorithm for computing the area under a GDT curve

Background Progress in the field of protein three-dimensional structure prediction depends on the development of new and improved algorithms for measuring the quality of protein models. Perhaps the best descriptor of the quality of a protein model is the GDT function that maps each distance cutoff θ to the number of atoms in the protein model that can be fit under the distance θ from the corresponding atoms in the experimentally determined structure. It has long been known that the area under the graph of this function (GDT_A) can serve as a reliable, single numerical measure of the model quality. Unfortunately, while the well-known GDT_TS metric provides a crude approximation of GDT_A, no algorithm currently exists that is capable of computing accurate estimates of GDT_A. Methods We prove that GDT_A is well defined and that it can be approximated by the Riemann sums, using available methods for computing accurate (near-optimal) GDT function values. Results In contrast to the GDT_TS metric, GDT_A is neither insensitive to large nor oversensitive to small changes in model’s coordinates. Moreover, the problem of computing GDT_A is tractable. More specifically, GDT_A can be computed in cubic asymptotic time in the size of the protein model. Conclusions This paper presents the first algorithm capable of computing the near-optimal estimates of the area under the GDT function for a protein model. We believe that the techniques implemented in our algorithm will pave ways for the development of more practical and reliable procedures for estimating 3D model quality

Tempus CaSA Project – A Sustainable Tool for Knowledge and Innovation Transfer in Animal Sciences

Although regulated by law and other policies knowledge transfer in animal sciences (zootechnics) is rather limited after students’ graduation from the Faculty of Agriculture. There is a lack of courses for professional development of teachers from agricultural middle schools, including those teaching subjects in animal sciences. There is as well a need of permanent improvement and upgrading of courses and trainings created for advisors in agricultural advisory services. The TEMPUS CaSA project objective is to contribute to the improvement of agricultural education to meet the needs of Serbian society. CaSA foresees: upgrading quality and availability of vocational agricultural education by strengthening professional and pedagogical competences of educators (University teachers, secondary school teachers, advisors) and creation of the repository for courses and additional contents important for agricultural education. Improvement of agricultural education will be achieved by introducing trainings in active teaching learning (ATL), communication skills, e-learning, together with newest knowledge emerging from research activities incorporated in vocational courses. Creation of the National Repository for Agricultural Education (NaRA), will enable networking of all stakeholders in agricultural education and assure sustainability. In addition, among 13 project partners, the Ministry of education is a compulsory partner for Structural Measures TEMPUS projects. This is important for recognition of the National repository by relevant state authorities. Online courses and teaching material, live stream trainings, results from the research projects, and different data bases will be available in NaRA after project life-time

Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis

Advances in next-generation sequencing and high-throughput techniques have enabled the generation of vast amounts of diverse omics data. These big data provide an unprecedented opportunity in biology, but impose great challenges in data integration, data mining, and knowledge discovery due to the complexity, heterogeneity, dynamics, uncertainty, and high-dimensionality inherited in the omics data. Network has been widely used to represent relations between entities in biological system, such as protein-protein interaction, gene regulation, and brain connectivity (i.e. network construction) as well as to infer novel relations given a reconstructed network (aka link prediction). Particularly, heterogeneous multi-layered network (HMLN) has proven successful in integrating diverse biological data for the representation of the hierarchy of biological system. The HMLN provides unparalleled opportunities but imposes new computational challenges on establishing causal genotype-phenotype associations and understanding environmental impact on organisms. In this review, we focus on the recent advances in developing novel computational methods for the inference of novel biological relations from the HMLN. We first discuss the properties of biological HMLN. Then we survey four categories of state-of-the-art methods (matrix factorization, random walk, knowledge graph, and deep learning). Thirdly, we demonstrate their applications to omics data integration and analysis. Finally, we outline strategies for future directions in the development of new HMLN models

Hyperbolic matrix factorization improves prediction of drug-target associations

Past research in computational systems biology has focused more on the development and applications of advanced statistical and numerical optimization techniques and much less on understanding the geometry of the biological space. By representing biological entities as points in a low dimensional Euclidean space, state-of-the-art methods for drug-target interaction (DTI) prediction implicitly assume the flat geometry of the biological space. In contrast, recent theoretical studies suggest that biological systems exhibit tree-like topology with a high degree of clustering. As a consequence, embedding a biological system in a flat space leads to distortion of distances between biological objects. Here, we present a novel matrix factorization methodology for drug-target interaction prediction that uses hyperbolic space as the latent biological space. When benchmarked against classical, Euclidean methods, hyperbolic matrix factorization exhibits superior accuracy while lowering embedding dimension by an order of magnitude. We see this as additional evidence that the hyperbolic geometry underpins large biological networks

Active Teaching/Learning at Faculty of Agriculture – 10 Years of Experience

The course in active teaching/learning (ATL) together with the method of lecture assessment by sequential analysis was developed by a pioneering group of experts from the Institute of Psychology. It was developed further and improved by adding research skills, aspects on quality assessment and examination procedures, relevant drama skills and techniques for interactive e-learning. The modules which we developed and implemented at Faculty of Agriculture several times in last 10 years had an impact on the quality of teaching of teachers as well as positive consequences to the student success

Improved genome-scale multitarget virtual screening via a novel collaborative filtering approach to cold-start problem

Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigm. Although many methods for single-target virtual screening have been developed to improve the efficiency of drug discovery, few of these algorithms are designed for polypharmacology. Here, we present a novel theoretical framework and a corresponding algorithm for genome-scale multitarget virtual screening based on the one-class collaborative filtering technique. Our method overcomes the sparseness of the protein-chemical interaction data by means of interaction matrix weighting and dual regularization from both chemicals and proteins. While the statistical foundation behind our method is general enough to encompass genome-wide drug off-target prediction, the program is specifically tailored to find protein targets for new chemicals with little to no available interaction data. We extensively evaluate our method using a number of the most widely accepted gene-specific and cross-gene family benchmarks and demonstrate that our method outperforms other state-of-the-art algorithms for predicting the interaction of new chemicals with multiple proteins. Thus, the proposed algorithm may provide a powerful tool for multi-target drug design

Histologija jetre i variranje površine jedara hepatocita pastrmke gajene u kaveznom sistemu

U hidroakumulacionom jezeru ”Bočac” gajena je kalifornijska pastrmka (Oncorhynchus mykiss, Walbaum, 1792) u dva odvojena eksperimenta u trajanju od po 90 dana – jedan u periodu jesen – zima, a drugi u periodu proleće – leto. Pastrmke su hranjene sa šest različitih komercijalnih hraniva i ispitivan je njihov uticaj na histološku gradju jetre riba. U eksperimentu je preovladavala normalna histološka građa jetre, a malobrojne histopatološke promene koje su uočene se mogu pripisati periodu godine i sastavu hrane. Kvantifikacija rezultata je pokazala da se sa rastom temperature vode i količine hrane kojom su ribe hranjene, prosečna površina jedara hepatocita povećava, dok se sa opadanjem temperature i količine hrane prosečna površina jedara hepatocita povećava, nezavisno od tipa hrane koja je korišćena

Doprinos CaSA Tempus projekta obrazovanju i praksi u oblasti akvakulture

Building Capacity of Serbian Agricultural Education to Link with Society, CaSA, 544072-TEMPUS-1-2013-1-RS-TEMPUS-SMHES (2013 – 4604 / 001 - 001) is a Structural Measures (SM) project funded by EACEA (Education, Audiovisual and Culture Executive Agency) under the 2013 Call for TEMPUS projects. Priorities for this, last call of TEMPUS projects, were: Higher Education (HE) and society; training of non university teachers; and lifelong learning (LLL). The project is conceived to make necessary trainings of main players of agricultural education: university teachers (UT), agricultural vocational secondary/middle school (AMS) teachers and advisors in Agricultural advisory services; and subsequently to establish a National Repository for Agricultural education, NaRA (Poleksić et al. 2014). In such a repository all relevant content for agriculture will be placed, and NaRA will be an interactive platform since it will contain courses, classical and online that will be taken by AMS teachers and advisors. Creation of courses foreseen for NaRA is the activity prepared by trainings: in modern teaching methodology of active teaching/learning (ATL) and eLearning. These trainings were provided to UT and AMS teachers. In addition academic and communication skills were also in the list of trainings for professionals involved in agricultural education. Under “relevant content” to be placed in NaRA, a broad spectrum of data bases and information, all linked to modern and sustainable practice in all areas of agriculture is identified: scientific and professional journals published by agricultural faculties participating CaSA, Proceedings and Books of Abstracts from meetings organized by these faculties, reports from national projects supported by Ministries of Education, Science and Technological Development and of Agriculture and Environmental Protection, as well as content relevant to development of professional skills - academic, teaching, communication, and management. There are 13 partner institutions in CaSA: 5 Universities (4 state and one private), the Association of Agricultural Middle Schools, the Institute for Science Application in Agriculture, 2 training organizations, the Ministry of Education (ME), and 3 EU partners Universities (Timisoara, Maribor and Foggia). The project is coordinated by the University of Belgrade. CaSA consists of 11 work packages that include: trainings; equipment purchase; establishment of NaRA, both construction of the virtual platform and its functionalities; NaRA Advisory Board establishment, courses creation, implementation and submission for accreditation/certification; and finally compulsory work packages: quality assurance - QA, dissemination and project management.Izgradnja kapaciteta srpskog obrazovanja u oblasti poljoprivrede radi povezivanja sa društvom (CaSA) 544072-TEMPUS-1-2013-1-RS-TEMPUS-SMHES (2013 – 4604 / 001 - 001) je projekat iz grupe Strukturnih Mera (SM) finansiran od strane EACEA (Izvršna agencija za obrazovanje, medije i kulturu) u pozivu za TEMPUS projekte iz 2013. godine. Prioriteti za poslednji poziv za TEMPUS projekte su bili: visoko obrazovanje i društvo, obuka ne- univerzitetskih nastavnika i celoživotno učenje (lifelong learning, LLL). Projekat je osmišljen da obezbedi neophodne obuke glavnih aktera poljoprivrednog obrazovanja : univerzitetskih nastavnika (UT), nastavnika stručnih predmeta u srednjim poljoprivrednim školama (AMS) i savetodavaca u poljoprivrednim stručnim savetodavnim službama (PSSS). Nadalje, predviđeno je osnivanje Nacionalnog repozitorijuma za poljoprivredno obrazovanje, NaRA (Poleksić et al. 2014). U takvom repozitorijumu/riznici znanja će biti smešteni i dostupni svi, za poljoprivredu, relevantni sadržaji. NaRA će biti interaktivna platforma jer će, osim dokumenata sadržati i kurseve, klasične i onlajn, koji će biti na raspolaganju nastavnicima i savetodavcima. Kreiranje kurseva predviđenih za NaRA je aktivnost pripremljena kroz obuke za savremene metode aktivnog učenja/nastave i e–učenja za univerzitetske i nastavnike srednjih škola. Takođe, akademske i komunikacione veštine su uključene u obuke za profesionalce u poljoprivrednom obrazovanju. Pod «relevantnim sadržajem« koji će biti pohranjen na NaRA podrazumeva se čitav spektar dokumenata - baza podataka i informacija vezanih za savremenu i održivu praksu u svim oblastima poljoprivrede, kao što su: naučni i stručni časopisi koje izdaju poljoprivredni fakulteti učesnici CaSA projekta, zbornici radova i apstrakata skupova koje pomenuti fakulteti organizuju, izveštaji nacionalnih projekata koje finansira Ministarstvo prosvete, nauke i tehnološkog razvoja i Ministarstvo poljoprivrede i zaštite životne sredine, kao i sadržaji relevantni za razvoj profesionalnih veština – akademskih, učenja/nastave, komunikacijskih i upravljačkih. U projektu CaSA učestvuje 13 partnerskih institucija: 5 univerziteta (4 državna i 1 privatni), udruženja srednjih škola područja rada poljoprivrede, proizvodnje i prerade hrane, Institut za primenu nauke u poljoprivredi, 2 organizacije koje obavljaju obuku, Ministarstvo prosvete, nauke i tehnološkog razvoja i 3 partnerska univerziteta iz EU (Maribor, Temišvar i Foggia). Projektom rukovodi Univerzitet u Beogradu, Poljoprivredni fakultet. CaSA ima 11 radnih paketa koji uključuju: obuke, nabavku opreme, uspostavljanje NaRA (stvaranje virtuelne platforme sa onlajn kursevima i širokom bazom podataka), osnivanje Savetodavnog Odbora NaRA, kreiranje kurseva, njihova implementacija i podnošenje kurseva za akreditaciju/sertifikaciju i na kraju obavezne radne pakete: obezbeđenje kvaliteta, diseminacija rezultata projekta i upravljanje projektom

Unapređenje održive akvakulture – projekat “ROSA”

ROSA is part of the European FP7 projects, and is realized at the Faculty of Agriculture, University of Belgrade. Project partners are The Institute of Aquaculture Research - AKVAFORSK from Norway (now under new research group called NOFIMA - MARIN) and Research Institute for Fisheries, Aquaculture and Irrigation - HAKI from Hungary. The main concept of the project ROSA is reinforcement of the S&T capacities in Aquaculture in Serbia and Western Balkan through support to the improvement of carp breeding technology concomitant with reduction of pollution of fish pond environment, by upgrading both human and material resources for research in sustainable fish production. The overall objective of the three years project is to strengthen education programs in animal science with new knowledge in fish nutrition, fish breeding, management and molecular biological methods used in biological research. In the ROSA project accomplishment of tasks is carried out through five work packages: WP 1 - Project Management and Coordination; WP 2 - Human resources reinforcement; WP 3 - Reinforcement of material resources; WP 4 - Reinforcement of knowledge in aquaculture and WP 5 - Promotion and dissemination

Predicting serious rare adverse reactions of novel chemicals

Motivation: Adverse drug reactions (ADRs) are one of the main causes of death and a major financial burden on the world\u27s economy. Due to the limitations of the animal model, computational prediction of serious and rare ADRs is invaluable. However, current state-of-the-art computational methods do not yield significantly better predictions of rare ADRs than random guessing. Results: We present a novel method, based on the theory of \u27compressed sensing\u27 (CS), which can accurately predict serious side-effects of candidate and market drugs. Not only is our method able to infer new chemical-ADR associations using existing noisy, biased and incomplete databases, but our data also demonstrate that the accuracy of CS in predicting a serious ADR for a candidate drug increases with increasing knowledge of other ADRs associated with the drug. In practice, this means that as the candidate drug moves up the different stages of clinical trials, the prediction accuracy of our method will increase accordingly. Availability and implementation: The program is available at https://github.com/poleksic/side-effects. Supplementary information: Supplementary data are available at Bioinformatics online