Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy

Background: It is necessary and essential to discovery protein function from the novel primary sequences. Wet lab experimental procedures are not only time-consuming, but also costly, so predicting protein structure and function reliably based only on amino acid sequence has significant value. TATA-binding protein (TBP) is a kind of DNA binding protein, which plays a key role in the transcription regulation. Our study proposed an automatic approach for identifying TATA-binding proteins efficiently, accurately, and conveniently. This method would guide for the special protein identification with computational intelligence strategies. Results: Firstly, we proposed novel fingerprint features for TBP based on pseudo amino acid composition, physicochemical properties, and secondary structure. Secondly, hierarchical features dimensionality reduction strategies were employed to improve the performance furthermore. Currently, Pretata achieves 92.92% TATA- binding protein prediction accuracy, which is better than all other existing methods. Conclusions: The experiments demonstrate that our method could greatly improve the prediction accuracy and speed, thus allowing large-scale NGS data prediction to be practical. A web server is developed to facilitate the other researchers, which can be accessed at http://server.malab.cn/preTata/

FKRR-MVSF: A Fuzzy Kernel Ridge Regression Model for Identifying DNA-Binding Proteins by Multi-View Sequence Features via Chou\u27s Five-Step Rule

DNA-binding proteins play an important role in cell metabolism. In biological laboratories, the detection methods of DNA-binding proteins includes yeast one-hybrid methods, bacterial singles and X-ray crystallography methods and others, but these methods involve a lot of labor, material and time. In recent years, many computation-based approachs have been proposed to detect DNA-binding proteins. In this paper, a machine learning-based method, which is called the Fuzzy Kernel Ridge Regression model based on Multi-View Sequence Features (FKRR-MVSF), is proposed to identifying DNA-binding proteins. First of all, multi-view sequence features are extracted from protein sequences. Next, a Multiple Kernel Learning (MKL) algorithm is employed to combine multiple features. Finally, a Fuzzy Kernel Ridge Regression (FKRR) model is built to detect DNA-binding proteins. Compared with other methods, our model achieves good results. Our method obtains an accuracy of 83.26% and 81.72% on two benchmark datasets (PDB1075 and compared with PDB186), respectively

Diagnosis of Brain Diseases via Multi-Scale Time-Series Model

The functional magnetic resonance imaging (fMRI) data and brain network analysis have been widely applied to automated diagnosis of neural diseases or brain diseases. The fMRI time series data not only contains specific numerical information, but also involves rich dynamic temporal information, those previous graph theory approaches focus on local topology structure and lose contextual information and global fluctuation information. Here, we propose a novel multi-scale functional connectivity for identifying the brain disease via fMRI data. We calculate the discrete probability distribution of co-activity between different brain regions with various intervals. Also, we consider nonsynchronous information under different time dimensions, for analyzing the contextual information in the fMRI data. Therefore, our proposed method can be applied to more disease diagnosis and other fMRI data, particularly automated diagnosis of neural diseases or brain diseases. Finally, we adopt Support Vector Machine (SVM) on our proposed time-series features, which can be applied to do the brain disease classification and even deal with all time-series data. Experimental results verify the effectiveness of our proposed method compared with other outstanding approaches on Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and Major Depressive Disorder (MDD) dataset. Therefore, we provide an efficient system via a novel perspective to study brain networks

Transcriptome changes in the phenylpropanoid pathway of Glycine max in response to Pseudomonas syringae infection

BACKGROUND: Reports of plant molecular responses to pathogenic infections have pinpointed increases in activity of several genes of the phenylpropanoid pathway leading to the synthesis of lignin and flavonoids. The majority of those findings were derived from single gene studies and more recently from several global gene expression analyses. We undertook a global transcriptional analysis focused on the response of genes of the multiple branches of the phenylpropanoid pathway to infection by the Pseudomonas syringae pv. glycinea with or without the avirulence gene avrB to characterize more broadly the contribution of the multiple branches of the pathway to the resistance response in soybean. Transcript abundance in leaves was determined from analysis of soybean cDNA microarray data and hybridizations to RNA blots with specific gene probes. RESULTS: The majority of the genes surveyed presented patterns of increased transcript accumulation. Some increased rapidly, 2 and 4 hours after inoculation, while others started to accumulate slowly by 8 – 12 hours. In contrast, transcripts of a few genes decreased in abundance 2 hours post inoculation. Most interestingly was the opposite temporal fluctuation in transcript abundance between early responsive genes in defense (CHS and IFS1) and F3H, the gene encoding a pivotal enzyme in the synthesis of anthocyanins, proanthocyanidins and flavonols. F3H transcripts decreased rapidly 2 hours post inoculation and increased during periods when CHS and IFS transcripts decreased. It was also determined that all but one (CHS4) family member genes (CHS1, CHS2, CHS3, CHS5, CHS6 and CHS7/8) accumulated higher transcript levels during the defense response provoked by the avirulent pathogen challenge. CONCLUSION: Based on the mRNA profiles, these results show the strong bias that soybean has towards increasing the synthesis of isoflavonoid phytoalexins concomitant with the down regulation of genes required for the synthesis of anthocyanins and proanthocyanins. Although proanthocyanins are known to be toxic compounds, the cells in the soybean leaves seem to be programmed to prioritize the synthesis and accumulation of isoflavonoid and pterocarpan phytoalexins during the resistance response. It was known that CHS transcripts accumulate in great abundance rapidly after inoculation of the soybean plants but our results have demonstrated that all but one (CHS4) member of the gene family member genes accumulated higher transcript levels during the defense response

FKRR-MVSF: A Fuzzy Kernel Ridge Regression Model for Identifying DNA-Binding Proteins by Multi-View Sequence Features via Chou\u27s Five-Step Rule

DNA-binding proteins play an important role in cell metabolism. In biological laboratories, the detection methods of DNA-binding proteins includes yeast one-hybrid methods, bacterial singles and X-ray crystallography methods and others, but these methods involve a lot of labor, material and time. In recent years, many computation-based approachs have been proposed to detect DNA-binding proteins. In this paper, a machine learning-based method, which is called the Fuzzy Kernel Ridge Regression model based on Multi-View Sequence Features (FKRR-MVSF), is proposed to identifying DNA-binding proteins. First of all, multi-view sequence features are extracted from protein sequences. Next, a Multiple Kernel Learning (MKL) algorithm is employed to combine multiple features. Finally, a Fuzzy Kernel Ridge Regression (FKRR) model is built to detect DNA-binding proteins. Compared with other methods, our model achieves good results. Our method obtains an accuracy of 83.26% and 81.72% on two benchmark datasets (PDB1075 and compared with PDB186), respectively

Identify RNA-Associated Subcellular Localizations Based on Multi-Label Learning Using Chou’s 5-Steps Rule

Background: Biological functions of biomolecules rely on the cellular compartments where they are located in cells. Importantly, RNAs are assigned in specific locations of a cell, enabling the cell to implement diverse biochemical processes in the way of concurrency. However, lots of existing RNA subcellular localization classifiers only solve the problem of single-label classification. It is of great practical significance to expand RNA subcellular localization into multi-label classification problem. Results: In this study, we extract multi-label classification datasets about RNA-associated subcellular localizations on various types of RNAs, and then construct subcellular localization datasets on four RNA categories. In order to study Homo sapiens, we further establish human RNA subcellular localization datasets. Furthermore, we utilize different nucleotide property composition models to extract effective features to adequately represent the important information of nucleotide sequences. In the most critical part, we achieve a major challenge that is to fuse the multivariate information through multiple kernel learning based on Hilbert-Schmidt independence criterion. The optimal combined kernel can be put into an integration support vector machine model for identifying multi-label RNA subcellular localizations. Our method obtained excellent results of 0.703, 0.757, 0.787, and 0.800, respectively on four RNA data sets on average precision. Conclusion: To be specific, our novel method performs outstanding rather than other prediction tools on novel benchmark datasets. Moreover, we establish user-friendly web server with the implementation of our method

Hexagonal Boron Nitride Thick Film Grown on a Sapphire Substrate via Low-Pressure Chemical Vapor Deposition

Hexagonal boron nitride (h-BN) with a certain thickness has wide applications in semiconductor electronic devices. In this study, the relationship between the amount of ammonia borane and the thickness of h-BN films was investigated via low-pressure chemical vapor deposition (LPCVD) on a noncatalytic c-plane Al2O3 substrate. Through various characterization methods, the grown film was confirmed to be h-BN. The effect of the precursor mass on the growth thickness of the h-BN film was studied, and it was found that the precursor mass significantly affected the growth rate of the h-BN film. The results from SEM show that the amount of ammonia borane is 2000 mg and a 1.295-μm h-BN film is obtained. It will provide an experimental reference for the growth of thicker h-BN materials to prepare high-efficiency neutron detectors for radiation detection

Designer carbon nanotubes for contaminant removal in water and wastewater: A critical review

The search for effective materials for environmental cleanup is a scientific and technological issue of paramount importance. Among various materials, carbon nanotubes (CNTs) possess unique physicochemical, electrical, and mechanical properties that make them suitable for potential applications as environmental adsorbents, sensors, membranes, and catalysts. Depending on the intended application and the chemical nature of the target contaminants, CNTs can be designed through specific functionalization or modification processes. Designer CNTs can remarkably enhance contaminant removal efficiency and facilitate nanomaterial recovery and regeneration. An increasing number of CNT-based materials have been used to treat diverse organic, inorganic, and biological contaminants. These success stories demonstrate their strong potential in practical applications, including wastewater purification and desalination. However, CNT-based technologies have not been broadly accepted for commercial use due to their prohibitive cost and the complex interactions of CNTs with other abiotic and biotic environmental components. This paper presents a critical review of the existing literature on the interaction of various contaminants with CNTs in water and soil environments. The preparation methods of various designer CNTs (surface functionalized and/or modified) and the functional relationships between their physicochemical characteristics and environmental uses are discussed. This review will also help to identify the research gaps that must be addressed for enhancing the commercial acceptance of CNTs in the environmental remediation industry