International Journal of Computational Biology and Drug Design最新文献

In immune response, major histocompatibility complex class II (MHC II) molecules bind peptides derived from antigens and present them. The formation of MHC II-peptide complexes was a critical signal in inducing of antibodies production of B cells. In this research, we tried to investigate whether there was a similarity or relationship between the segments in B cell conformational epitopes and MHC II peptides. All the segments from the conformational epitopes and MHC II peptides were extracted and the relationship between them was analysed. From current results, it can be hinted that the epitope segments have a higher similarity to the known MHC II peptides. Furthermore, the majority of known conformational epitopes were found to contain at least one segment identical or similar to a MHC II peptide. Such results may not only facilitate the study of antigen information processing but also help to improve the conformational epitope prediction algorithm.

Traditional two-dimensional (2D) X-ray mammography is the most commonly used method for breast cancer diagnosis. Recently, a three-dimensional (3D) Digital Breast Tomosynthesis (DBT) system has been invented, which is likely to challenge the current mammography technology. The DBT system provides stunning 3D information, giving physicians increased detail of anatomical information, while reducing the chance of false negative screening. In this research, two reconstruction algorithms, Back Projection (BP) and Shift-And-Add (SAA), were used to investigate and compare View Angle (VA) and the number of projection images (N) with parallel imaging configurations. In addition, in order to better determine which method displayed better-quality imaging, Modulation Transfer Function (MTF) analyses were conducted with both algorithms, ultimately producing results which improve upon better breast cancer detection. Research studies find evidence that early detection of the disease is the best way to conquer breast cancer, and earlier detection results in the increase of life span for the affected person.

Clustering is an unsupervised method that allows researchers to group objects and gather information about their relationships. In chemoinformatics, clustering enables hypotheses to be drawn about a compound's biological, chemical and physical property in comparison to another. We introduce a novel improved spectral clustering algorithm, proposed for chemical compound clustering, using multiple data sources. Tensor-based spectral methods, used in this paper, provide chemically appropriate and statistically significant results when attempting to cluster compounds from both the GSK-Chembl Malaria data set and the Zinc database. Spectral clustering algorithms based on the tensor method give robust results on the mid-size compound sets used here. The goal of this paper is to present the clustering of chemical compounds, using a tensor-based multi-view method which proves of value to the medicinal chemistry community. Our findings show compounds of extremely different chemotypes clustering together, this is a hint to the chemogenomics nature of our method.

Sample size determination is an important issue in the experimental design of biomedical research. Because of the complexity of RNA-seq experiments, however, the field currently lacks a sample size method widely applicable to differential expression studies utilising RNA-seq technology. In this report, we propose several methods for sample size calculation for single-gene differential expression analysis of RNA-seq data under Poisson distribution. These methods are then extended to multiple genes, with consideration for addressing the multiple testing problem by controlling false discovery rate. Moreover, most of the proposed methods allow for closed-form sample size formulas with specification of the desired minimum fold change and minimum average read count, and thus are not computationally intensive. Simulation studies to evaluate the performance of the proposed sample size formulas are presented; the results indicate that our methods work well, with achievement of desired power. Finally, our sample size calculation methods are applied to three real RNA-seq data sets.

The colony-based laser scatter imaging provides a convincing solution to microbial source tracking. The optical scattering patterns of bacterial colonies are tightly correlated to the corresponding growth patterns. This relationship is manifested as the development of optical scattering patterns with the increment of colony size. An investigation was conducted into this relationship and the optimal range of colony size for improving the accuracy of microbial source tracking technique. All the bacterial samples from five host species were cultivated under the same conditions. The optical scattering patterns were recorded for the average colony diameter from 0.1 mm to 1.5 mm, using a bench top laser imaging system. Gabor wavelet was utilised to encode image signatures. Fuzzy-C-means was employed to cluster the colony patterns from the same host species. The experimental results demonstrate that the optimal range of the colony diameters is 0.8-1.0 mm. The corresponding identification rate of microbial source tracking is >80%.

Pharmacogenomics (PGx) studies are to identify genetic variants that may affect drug efficacy and toxicity. A machine understandable drug-gene relationship knowledge is important for many computational PGx studies and for personalised medicine. A comprehensive and accurate PGx-specific gene lexicon is important for automatic drug-gene relationship extraction from the scientific literature, rich knowledge source for PGx studies. In this study, we present a bootstrapping learning technique to rank 33,310 human genes with respect to their relevance to drug response. The algorithm uses only one seed PGx gene to iteratively extract and rank co-occurred genes using 20 million MEDLINE abstracts. Our ranking method is able to accurately rank PGx-specific genes highly among all human genes. Compared to randomly ranked genes (precision: 0.032, recall: 0.013, F1: 0.018), the algorithm has achieved significantly better performance (precision: 0.861, recall: 0.548, F1: 0.662) in ranking the top 2.5% of genes.

In this paper, we present a solution to the extreme similarity sequencing problem. The extreme similarity sequencing problem consists of finding occurrences of a pattern p in a set S(0), S(1), …, S(k), of sequences of equal length, where S(i), for all 1≤i≤k, differs from S(0) by a constant number of errors - around 10 in practice. We present an asymptotically fast O(n + occ logocc) time algorithm, as well as a practical O(nk/w) time algorithm for solving this problem, where n is the length of a sequence, occ is the number of candidate occurrences reported by our technique, w is the size of the machine word, and the total number of errors is bounded by k - the number of sequences.

Molecular polar surface (PS) descriptors are very useful parameters in prediction of drug transport properties. They could be also used to investigate the blood-brain barrier (BBB) permeation rate for various chemical compounds. In this study, a dataset of drugs (n = 19) from various pharmacological groups was studied to estimate their potential properties to permeate across the BBB. Experimental logBB data were available as steady-state distribution values of the in vivo rat model for these molecules. Including accurate calculation of the electrostatic potential maps, polar surface descriptors, such as a two-dimensional polar surface area (2D-PSA), topological polar surface area (TPSA) and three-dimensional polar surface area or polar area (3D-PSA; PA) were measured and analysed. We report the strong correlation of these descriptors with logBB values for the prediction of BBB permeation using the linear partial least squares (PLS) fitting technique. The 3D-PSA descriptor showed the best fit to logBB values with R² = 0.92 and RMSD = 0.29 (p-value < 0.0001). The obtained results demonstrate that all descriptors bear high predictive powers and could provide an efficient strategy to envisage the pharmacokinetic properties of chemical compounds to permeate across the BBB at an early stage of the drug development process.

In this paper, a set of non-rigid image registration and neural activity modelling methods using functional MR Images (fMRI) are proposed based on transform-invariant feature representations. Our work made two contributions. First, we propose to use a transform-invariant feature to improve image registration performance of Iterative Closest Point (ICP) based methods. The proposed feature utilises Gaussian Mixture Models (GMM) to describe the local topological structure of fMRI data. Second, we propose to use a 3-dimensional Scale-Invariant Feature Transform (SIFT) based descriptor to represent neural activities related to drinking behaviour. As a result, neural activities patterns of different subjects drinking water or intaking glucose can be recognised, with strong robustness against various artefacts.