BackgroundIn the context of drug discovery, drug target interactions (DTIs) can be predicted based on observed topological features of a semantic network across the chemical and biological space. In a semantic network, the types of the nodes and links are different. In order to take into account the heterogeneity of the semantic network, meta-path-based topological patterns were investigated for link prediction.ResultsSupervised machine learning models were constructed based on meta-path topological features of an enriched semantic network, which was derived from Chem2Bio2RDF, and was expanded by adding compound and protein similarity neighboring links obtained from the PubChem databases. The additional semantic links significantly improved the predictive performance of the supervised learning models. The binary classification model built upon the enriched feature space using the Random Forest algorithm significantly outperformed an existing semantic link prediction algorithm, Semantic Link Association Prediction (SLAP), to predict unknown links between compounds and protein targets in an evolving network. In addition to link prediction, Random Forest also has an intrinsic feature ranking algorithm, which can be used to select the important topological features that contribute to link prediction.ConclusionsThe proposed framework has been demonstrated as a powerful alternative to SLAP in order to predict DTIs using the semantic network that integrates chemical, pharmacological, genomic, biological, functional, and biomedical information into a unified framework. It offers the flexibility to enrich the feature space by using different normalization processes on the topological features, and it can perform model construction and feature selection at the same time.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1005-x) contains supplementary material, which is available to authorized users.

BackgroundPredicting novel drug–target associations is important not only for developing new drugs, but also for furthering biological knowledge by understanding how drugs work and their modes of action. As more data about drugs, targets, and their interactions becomes available, computational approaches have become an indispensible part of drug target association discovery. In this paper we apply random walk with restart (RWR) method to a heterogeneous network of drugs and targets compiled from DrugBank database and investigate the performance of the method under parameter variation and choice of chemical fingerprint methods.ResultsWe show that choice of chemical fingerprint does not affect the performance of the method when the parameters are tuned to optimal values. We use a subset of the ChEMBL15 dataset that contains 2,763 associations between 544 drugs and 467 target proteins to evaluate our method, and we extracted datasets of bioactivity ≤1 and ≤10 μM activity cutoff. For 1 μM bioactivity cutoff, we find that our method can correctly predict nearly 47, 55, 60% of the given drug–target interactions in the test dataset having more than 0, 1, 2 drug target relations for ChEMBL 1 μM dataset in top 50 rank positions. For 10 μM bioactivity cutoff, we find that our method can correctly predict nearly 32.4, 34.8, 35.3% of the given drug–target interactions in the test dataset having more than 0, 1, 2 drug target relations for ChEMBL 1 μM dataset in top 50 rank positions. We further examine the associations between 110 popular top selling drugs in 2012 and 3,519 targets and find the top ten targets for each drug.ConclusionsWe demonstrate the effectiveness and promise of the approach—RWR on heterogeneous networks using chemical features—for identifying novel drug target interactions and investigate the performance.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-015-0089-z) contains supplementary material, which is available to authorized users.

SUMMARYThere is substantial interspecific variation in nuclear DNA content in Festuca much of which cannot be accounted for by variation in chromosome number.The distribution of species DNA contents closely matches the taxonomic divisions within the genus. Polyploid taxa, especially high polyploids, tend to have less DNA than expected.(a) There may be errors in the proposed modes of origin of polyploid forms. In particular, the distinction between European and Moroccan forms may have been underestimated, and/or (b) polyploid species may have undergone a reduction in nuclear DNA content since their divergence, relative to their progenitor species of lower ploidy.

BackgroundMycobacterium tuberculosis encodes 11 putative serine-threonine proteins Kinases (STPK) which regulates transcription, cell development and interaction with the host cells. From the 11 STPKs three kinases namely PknA, PknB and PknG have been related to the mycobacterial growth. From previous studies it has been observed that PknB is essential for mycobacterial growth and expressed during log phase of the growth and phosphorylates substrates involved in peptidoglycan biosynthesis. In recent years many high affinity inhibitors are reported for PknB. Previously implementation of data fusion has shown effective enrichment of active compounds in both structure and ligand based approaches .In this study we have used three types of data fusion ranking algorithms on the PknB dataset namely, sum rank, sum score and reciprocal rank. We have identified reciprocal rank algorithm is capable enough to select compounds earlier in a virtual screening process. We have also screened the Asinex database with reciprocal rank algorithm to identify possible inhibitors for PknB.ResultsIn our work we have used both structure-based and ligand-based approaches for virtual screening, and have combined their results using a variety of data fusion methods. We found that data fusion increases the chance of actives being ranked highly. Specifically, we found that the ranking of Pharmacophore search, ROCS and Glide XP fused with a reciprocal ranking algorithm not only outperforms structure and ligand based approaches but also capable of ranking actives better than the other two data fusion methods using the BEDROC, robust initial enhancement (RIE) and AUC metrics. These fused results were used to identify 45 candidate compounds for further experimental validation.ConclusionWe show that very different structure and ligand based methods for predicting drug-target interactions can be combined effectively using data fusion, outperforming any single method in ranking of actives. Such fused results show promise for a coherent selection of candidates for biological screening.

SUMMARYInbreeding species of Lolium have about 40 per cent more nuclear DNA than the outbreeding species. Even so inbreedcrs may be readily hybridised with outbreeders to produce viable F1's which, in many cases, are fertile, producing F2 progenies on intercrossing or backcross progenies when crossed with the parents.Recombination between and within chromosomes at meiosis in the F, results in the quantitative segregation of DNA to the nuclei of gametes and of F2 and backcross progenies. An assay of the effects of the DNA Variation among these progenies showed no influence of DNA amount upon the expression of 19 phenotypic characters, ranging from early seedling growth to flowering. There was, however, a very slight reduction from expectation in the mean DNA amount of backcross and F2 progenies. Apart from this slight effect on viability, either of gametes or of zygotes with high nuclear DNA content, our assay shows that the supplementary DNA fraction which distinguishes outbreeding from inbreeding species has, at most, surprisingly little effect upon the growth and development of the Lolium hybrids.