This paper discusses the weighting of two-dimensional fingerprints for similarity-based virtual screening, specifically the use of weights that assign greatest importance to the substructural fragments that occur least frequently in the database that is being screened. Virtual screening experiments using the MDL Drug Data Report and World of Molecular Bioactivity databases show that the use of such inverse frequency weighting schemes can result, in some circumstances, in marked increases in screening effectiveness when compared with the use of conventional, unweighted fingerprints. Analysis of the characteristics of the various schemes demonstrates that such weights are best used to weight the fingerprint of the reference structure in a similarity search, with the database structures' fingerprints unweighted. However, the increases in performance resulting from such weights are only observed with structurally homogeneous sets of active molecules; when the actives are diverse, the best results are obtained using conventional, unweighted fingerprints for both the reference structure and the database structures.
Gene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR) to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C) as a direct interaction (A → C). Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5.
The mango (Mangifer indica L.) is an important species of the family Anacardiaceae and is one of the most important crops cultivated commercially in many parts of the world. Hence, a better understanding of the phylogeny in this species is crucial as it is the basis knowledge of improving its genetic resources which is beneficial for breeding programs. Phylogenetic relationships among 13 mango cultivars from Indonesia, Malaysia and Taiwan were carried out by comparing DNA sequence data sets derived from the Internal Transcribed Spacer (ITS) region pfnuclear ribosomal DNA (nrDNA). Analysis using parsimony method showed that the cultivars were classified into three major groups. The first group composed almost Malaysian cultivars although with low bootstrap value, the second group consisted of mainly Taiwan cultivars and the last group included mostly Indonesia one. The results indicated that some cultivars have a close relationships with each other even it is originated from different countries. With regards to the relationship among these cultivars, this gives better insight for generating new cultivar.
A gene regulatory network (GRN) is a large and complex network consisting of interacting elements that, over time, affect each other's state. The dynamics of complex gene regulatory processes are difficult to understand using intuitive approaches alone. To overcome this problem, we propose an algorithm for inferring the regulatory interactions from knock-out data using a Gaussian model combines with Pearson Correlation Coefficient (PCC). There are several problems relating to GRN construction that have been outlined in this paper. We demonstrated the ability of our proposed method to (1) predict the presence of regulatory interactions between genes, (2) their directionality and (3) their states (activation or suppression). The algorithm was applied to network sizes of 10 and 50 genes from DREAM3 datasets and network sizes of 10 from DREAM4 datasets. The predicted networks were evaluated based on AUROC and AUPR. We discovered that high false positive values were generated by our GRN prediction methods because the indirect regulations have been wrongly predicted as true relationships. We achieved satisfactory results as the majority of sub-networks achieved AUROC values above 0.5.
In this work we report on the isolation of a local molybdenum-reducing bacterium. The bacterium reduced molybdate or Mo(6+) to molybdenum blue (oxidation states between 5+ to 6+). Electron donors that supported cellular growth were sucrose, maltose, mannitol, fructose, glucose and starch (in decreasing order) with sucrose supporting formation of the highest amount of molybdenum blue at 10 g/l after 24 hours of static incubation. The optimum molybdate and phosphate concentrations that supported molybdate reduction were 20 and 5 mM, respectively. Molybdate reduction was optimal at 37 degrees C. The molybdenum blue produced from cellular reduction exhibited a unique absorption spectrum with a maximum peak at 865 nm and a shoulder at 700 nm. The isolate was tentatively identified as S. marcescens strain Dr.Y9 based on carbon utilization profiles using Biolog GN plates and partial 16S rDNA molecular phylogeny. No inhibition of molybdenum-reducing activity was seen using electron transport system (ETS) inhibitors such as antimycin A, 1HQNO (Hydroxyquinoline-N-Oxide), sodium azide and cyanide suggesting that the ETS of this bacterium is not the site of molybdate reduction.