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With the emergence of large-scale experimental data on genes and proteins, drug discovery and repositioning will be more difficult in the field of biomedical research. More and more resources are needed for detecting drug–target interactions (DTIs) in the experimental works. The interactions between drugs and targets could been seen as a bipartite network. Many computational methods have been developed to identify DTIs. However, most of them did not integrate multiple information and filter noise or outlier points. In this paper, we develop a fuzzy bipartite local model (FBLM) based on fuzzy least squares support vector machine and multiple kernel learning (MKL) for predicting DTIs. First, multiple kernels are constructed in drug and target spaces, respectively. Then, all corresponding kernels are combined by MKL algorithm in two spaces. Finally, FBLM is employed to identify DTIs. Our proposed approach is tested on four benchmark datasets under three types of cross validation. Comparing with existing outstanding methods, our method is a useful tool for the DTIs prediction. [ABSTRACT FROM AUTHOR]

Ricin is a toxic protein derived from castor beans and composed of a cytotoxic A chain and a galactose-binding B chain linked by a disulfide bond, which can inhibit protein synthesis and cause cell death. Owing to its high toxicity, ease of preparation, and lack of medical countermeasures, ricin has been listed as both chemical and biological warfare agents. For homeland security or public safety, the unambiguous, sensitive, and rapid methods for identification and quantification of ricin in complicated matrices are of urgent need. Mass spectrometric analysis, which provides specific and sensitive characterization of protein, can be applied to confirm and quantify ricin. Here, we report a liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) method in which ricin was extracted and enriched from serum by immunocapture using anti-ricin monoclonal antibody 3D74 linked to magnetic beads, then digested by trypsin, and analyzed by LC-ESI-MS/MS. Among 19 distinct peptides observed in LC-quadrupole/time of flight-MS (LC-QTOF-MS), two specific and sensitive peptides, T (VGLPINQR) and T (DNCLTSDSNIR), were chosen, and a highly sensitive determination of ricin was established in LC-triple quadrupole-MS (LC-QqQ-MS) operating in multiple reaction monitoring mode. These specific peptides can definitely distinguish ricin from the homologous protein Ricinus communis agglutinin (RCA120), even though the amino acid sequence homology of the A-chain of ricin and RCA120 is up to ca. 93 % and that of B-chain is ca. 85 %. Furthermore, peptide T was preferred in the quantification of ricin because its sensitivity was at least one order of magnitude higher than that of the peptide T. Combined with immunocapture enrichment, this method provided a limit of detection of ca. 2.5 ng/mL and the limit of quantification was ca. 5 ng/mL of ricin in serum, respectively. Both precision and accuracy of this method were determined and the RSD was less than 15 %. This established method was then applied to measure ricin in serum samples collected from rats exposed to ricin at the dosage of 50 μg/kg in an intravenous injection manner. The results showed that ca. 10 ng/mL of the residual ricin in poisoned rats serum could be detected even at 12 h after exposure. [ABSTRACT FROM AUTHOR]

Transcription factors (TFs) binding to specific DNA sequences or motifs, are elementary to the regulation of transcription. The gene is regulated by a combination of TFs in close proximity. Analysis of co-TFs is an important problem in understanding the mechanism of transcriptional regulation. Recently, ChIP-seq in mapping TF provides a large amount of experimental data to analyze co-TFs. Several studies show that if two TFs are co-associated, the relative distance between TFs exhibits a peak-like distribution. In order to analyze co-TFs, we develop a novel method to evaluate the associated situation between TFs. We design an adjacency score based on ordered differences, which can illustrate co-TF binding affinities for motif analysis. For all candidate motifs, we calculate corresponding adjacency scores, and then list descending-order motifs. From these lists, we can find co-TFs for candidate motifs. On ChIP-seq datasets, our method obtains best AUC results on five datasets, 0.9432 for NMYC, 0.9109 for KLF4, 0.9006 for ZFX, 0.8892 for ESRRB, 0.8920 for E2F1. Our method has great stability on large sample datasets. AUC results of our method on all datasets are above 0.8. [ABSTRACT FROM AUTHOR]

Retinal diseases can be found timely by observing retinal fundus images. So extracting blood vessels from retinal images is an important part because it is the way to show the changes of vessels. However, most of the previous methods based on deep learning cared more about accuracy and ignored the complexity of the model for segmenting retinal vessels, which makes these methods difficult to apply to medical equipment. Besides, due to the great differences in the width of retinal vessels, some methods cannot well-extract all blood vessels at the same time. Based on above limitations, we propose a new lightweight network, called Res2Unet. It applies a multi-scale strategy to extract blood vessels of different widths and integrates the strategy into the channels to greatly reduce parameters and computation resources. Res2Unet also uses channel-attention mechanism to promote the communication between channels and recalibrate the relationship of channel features. Then, we propose two post-processing methods. One called the local threshold method(LTM) uses a lower local threshold to excavate hidden blood vessels in discontinuous blood vessels of the probability maps. The other named weighted correction method (WCM) combines the probability maps of Unet and Res2Unet to remove false positive and false negative samples. On the DRIVE dataset, the Dice, IOU and AUC of our Res2Unet reach 0.8186, 0.6926 and 0.9772, respectively, which are better than that of Unet with 0.8109, 0.6817 and 0.9751. Importantly, the number of parameters of Res2Unet are about one-third of Unet. It means that Res2Unet has less hardware requirements. [ABSTRACT FROM AUTHOR]

RNA-binding proteins play an important role in the biological process. However, the traditional experiment technology to predict RNA-binding residues is time-consuming and expensive, so the development of an effective computational approach can provide a strategy to solve this issue. In recent years, most of the computational approaches are constructed on protein sequence information, but the protein structure has not been considered. In this paper, we use a novel computational model of RNA-binding residues prediction, using protein sequence and structure information. Our hybrid features are encoded by local sequence and structure feature extraction models. Our predictor is built by employing the Granular Multiple Kernel Support Vector Machine with Repetitive Under-sampling (GMKSVM-RU). In order to evaluate our method, we use fivefold cross-validation on the RBP129, our method achieves better experimental performance with MCC of 0.3367 and accuracy of 88.84%. In order to further evaluate our model, an independent data set (RBP60) is employed, and our method achieves MCC of 0.3921 and accuracy of 87.52%. Above results demonstrate that integrating sequence and structure information is beneficial to improve the prediction ability of RNA-binding residues. [ABSTRACT FROM AUTHOR]