Publications by authors named "Shengwei Tian"

9 Publications

  • Page 1 of 1

Anti-cancer Peptide Recognition Based on Grouped Sequence and Spatial Dimension Integrated Networks.

Interdiscip Sci 2021 Oct 12. Epub 2021 Oct 12.

People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China.

The diversification of the characteristic sequences of anti-cancer peptides has imposed difficulties on research. To effectively predict new anti-cancer peptides, this paper proposes a more suitable feature grouping sequence and spatial dimension-integrated network algorithm for anti-cancer peptide sequence prediction called GRCI-Net. The main process is as follows: First, we implemented the fusion reduction of binary structure features and K-mer sparse matrix features through principal component analysis and generated a set of new features; second, we constructed a new bidirectional long- and short-term memory network. We used traditional convolution and dilated convolution to acquire features in the spatial dimension using the memory network's grouping sequence model, which is designed to better handle the diversification of anti-cancer peptide feature sequences and to fully learn the contextual information between features. Finally, we achieved the fusion of grouping sequence features and spatial dimensional integration features through two sets of dense network layers, achieved the prediction of anti-cancer peptides through the sigmoid function, and verified the approach with two public datasets, ACP740 (accuracy reached 0.8230) and ACP240 (accuracy reached 0.8750). The following is a link to the model code and datasets mentioned in this article: https://github.com/ YouHongfeng101/ACP-DL.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s12539-021-00481-0DOI Listing
October 2021

Development and Validation of Prognostic Nomogram for Young Patients with Kidney Cancer.

Int J Gen Med 2021 1;14:5091-5103. Epub 2021 Sep 1.

Department of Urology, Zhongda Hospital, Southeast University, Nanjing, 210009, People's Republic of China.

Background: The aim of this study was to establish a nomogram model to evaluate the prognosis of early-onset kidney cancer (EOKC) in terms of overall survival (OS) and cancer-specific survival (CSS).

Methods: Patients with EOKC diagnosed between 2004 and 2015 were collected from Surveillance, Epidemiology and End Results (SEER) and randomly assigned to the training and validation set at a ratio of 2 to 1. Important variables for constructing nomograms were screened by univariate and multivariate Cox analysis. The nomogram model was evaluated using concordance index (C-index), decision curve analysis (DCA) curves, and receiver operating characteristic (ROC) curves.

Results: A total of 12,526 EOKC patients were included in the study. OS nomogram was constructed based on gender, age, race, grade, AJCC stage, TNM stage, histology, chemotherapy and radiotherapy. CSS nomogram was constructed based on listed above except gender. In the external validation, the C-index for the OS nomogram was 0.855 (95% CI 0.834-0.976), and the C-index for the CSS nomogram was 0.938 (0.925-0.951). High-quality calibration curves were noted in both OS and CSS nomogram models. ROC and DCA curves showed that nomograms had better predictive performance than TNM stage and SEER stage.

Conclusion: The nomogram model provides an applicable tool for evaluating the OS and CSS prognosis of EOKC.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.2147/IJGM.S331627DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8420796PMC
September 2021

Polycyclic Aromatic Hydrocarbons and the Risk of Kidney Stones in US Adults: An Exposure-Response Analysis of NHANES 2007-2012.

Int J Gen Med 2021 21;14:2665-2676. Epub 2021 Jun 21.

Department of Urology, Zhongda Hospital, Southeast University, Nanjing, 210009, People's Republic of China.

Background: Polycyclic aromatic hydrocarbons (PAHs) exposure may cause various diseases. However, the association between PAHs exposure and kidney stones remains unclear. The purpose of this study was to examine the relationship between PAHs and the risk of kidney stones in the US population.

Methods: The study included a total of 30,442 individuals (≥20 years) from the 2007-2012 National Health and Nutrition Examination Survey (NHANES). Nine urinary PAHs were included in this study. Logistic regression and dose-response curves were used to evaluate the association between PAHs and the risk of kidney stones.

Results: We selected 4385 participants. The dose-response curves showed a significant positive association between total PAHs, 2-hydroxynaphthalene, 1-hydroxyphenanthrene, 2-hydroxyphenanthrene, 9-hydroxyfluorene and the risk of kidney stones after adjusting for confounding factors. Compared with the low group, an increased risk of kidney stones was observed in the high group of total PAHs [OR (95% CI), 1.32 (1.06-1.64), P=0.013], 2-hydroxynaphthalene [OR (95% CI), 1.37 (1.10-1.71), P=0.005], 1-hydroxyphenanthrene [OR (95% CI), 1.24 (1.00-1.54), P=0.046] and 9-hydroxyfluorene [OR (95% CI), 1.36 (1.09-1.70), P=0.007].

Conclusion: High levels of PAHs were positively associated with the risk of kidney stones in the US population.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.2147/IJGM.S319779DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8232959PMC
June 2021

Medical image segmentation using boundary-enhanced guided packet rotation dual attention decoder network.

Technol Health Care 2021 May 19. Epub 2021 May 19.

School of Educational Science, Xinjiang Normal University, Urumqi, Xinjiang, China.

Objective: The automatic segmentation of medical images is an important task in clinical applications. However, due to the complexity of the background of the organs, the unclear boundary, and the variable size of different organs, some of the features are lost during network learning, and the segmentation accuracy is low. To address these issues, This prompted us to study whether it is possible to better preserve the deep feature information of the image and solve the problem of low segmentation caused by unclear image boundaries.

Methods: In this study, we (1) build a reliable deep learning network framework, named BGRANet,to improve the segmentation performance for medical images; (2) propose a packet rotation convolutional fusion encoder network to extract features; (3) build a boundary enhanced guided packet rotation dual attention decoder network, which is used to enhance the boundary of the segmentation map and effectively fuse more prior information; and (4) propose a multi-resolution fusion module to generate high-resolution feature maps. We demonstrate the effffectiveness of the proposed method on two publicly available datasets.

Results: BGRANet has been trained and tested on the prepared dataset and the experimental results show that our proposed model has better segmentation performance. For 4 class classifification (CHAOS dataset), the average dice similarity coeffiffifficient reached 91.73%. For 2 class classifification (Herlev dataset), the prediction, sensitivity, specifificity, accuracy, and Dice reached 93.75%, 94.30%, 98.19%, 97.43%, and 98.08% respectively. The experimental results show that BGRANet can improve the segmentation effffect for medical images.

Conclusion: We propose a boundary-enhanced guided packet rotation dual attention decoder network. It achieved high segmentation accuracy with a reduced parameter number.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3233/THC-202789DOI Listing
May 2021

Integrative Analysis of DNA Methylation Identified 12 Signature Genes Specific to Metastatic ccRCC.

Front Oncol 2020 8;10:556018. Epub 2020 Oct 8.

Department of Urology, Zhongda Hospital, Southeast University, Nanjing, China.

Abnormal epigenetic alterations can contribute to the development of human malignancies. Identification of these alterations for early screening and prognosis of clear cell renal cell carcinoma (ccRCC) has been a highly sought-after goal. Bioinformatic analysis of DNA methylation data provides broad prospects for discovery of epigenetic biomarkers. However, there is short of exploration of methylation-driven genes of ccRCC. Gene expression data and DNA methylation data in metastatic ccRCC were sourced from the Gene Expression Omnibus (GEO) database. Differentially methylated genes (DMGs) at 5'-C-phosphate-G- 3' (CpG) sites and differentially expressed genes (DEGs) were screened and the overlapping genes in DMGs and DEGs were then subject to gene set enrichment analysis. Next, the weighted gene co-expression network analysis (WGCNA) was used to search hub DMGs associated with ccRCC. Cox regression and ROC analyses were performed to screen potential biomarkers and develop a prognostic model based on the screened hub genes. Three hundred and fourteen overlapping DMGs were obtained from two independent GEO datasets. The turquoise module contained 79 hub DMGs, which represent the most significant module screened by WGCNA. Furthermore, a total of 12 hub genes (, and ) were identified in the TCGA database by multivariate Cox regression analyses. All the 12 genes were then used to generate the model for diagnosis and prognosis of ccRCC. ROC analysis showed that these genes exhibited good diagnostic efficiency for metastatic and non-metastatic ccRCC. Furthermore, the prognostic model with the 12 methylation-driven genes demonstrated a good prediction of 5-year survival rates for ccRCC patients. Integrative analysis of DNA methylation data identified 12 signature genes, which could be used as epigenetic biomarkers for prognosis of metastatic ccRCC. This prognostic model has a good prediction of 5-year survival for ccRCC patients.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fonc.2020.556018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7578385PMC
October 2020

MD-MLI: Prediction of miRNA-lncRNA Interaction by Using Multiple Features and Hierarchical Deep Learning.

IEEE/ACM Trans Comput Biol Bioinform 2020 Oct 30;PP. Epub 2020 Oct 30.

Long non-coding RNA(lncRNA) can interact with microRNA(miRNA) and play an important role in inhibiting or activating the expression of target genes and the occurrence and development of tumors. Accumulating studies focus on the prediction of miRNA-lncRNA interaction, and mostly are concerned with biological experiments and machine learning methods. These methods are found with long cycles, high costs, and requiring over much human intervention. In this paper, a data-driven hierarchical deep learning framework was proposed, which was composed of a capsule network, an independent recurrent neural network with attention mechanism and bi-directional long short-term memory network. This framework combines the advantages of different networks, uses multiple sequencederived features of the original sequence and features of secondary structure to mine the dependency between features, and devotes to obtain better results. In the experiment, five-fold cross-validation was used to evaluate the performance of the model, and the zea mays data set was compared with the different model to obtain better classification effect. In addition, sorghum, brachypodium distachyon and bryophyte data sets were used to test the model, and the accuracy reached 0.9850, 0.9859 and 0.9777, respectively, which verified the model's good generalization ability.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1109/TCBB.2020.3034922DOI Listing
October 2020

Fully convolutional attention network for biomedical image segmentation.

Artif Intell Med 2020 07 5;107:101899. Epub 2020 Jun 5.

College of Software Engineering, Xin Jiang University, Urumqi 830000, China.

In this paper, we embed two types of attention modules in the dilated fully convolutional network (FCN) to solve biomedical image segmentation tasks efficiently and accurately. Different from previous work on image segmentation through multiscale feature fusion, we propose the fully convolutional attention network (FCANet) to aggregate contextual information at long-range and short-range distances. Specifically, we add two types of attention modules, the spatial attention module and the channel attention module, to the Res2Net network, which has a dilated strategy. The features of each location are aggregated through the spatial attention module, so that similar features promote each other in space size. At the same time, the channel attention module treats each channel of the feature map as a feature detector and emphasizes the channel dependency between any two channel maps. Finally, we weight the sum of the output features of the two types of attention modules to retain the feature information of the long-range and short-range distances, to further improve the representation of the features and make the biomedical image segmentation more accurate. In particular, we verify that the proposed attention module can seamlessly connect to any end-to-end network with minimal overhead. We perform comprehensive experiments on three public biomedical image segmentation datasets, i.e., the Chest X-ray collection, the Kaggle 2018 data science bowl and the Herlev dataset. The experimental results show that FCANet can improve the segmentation effect of biomedical images. The source code models are available at https://github.com/luhongchun/FCANet.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2020.101899DOI Listing
July 2020

AC-Caps: Attention Based Capsule Network for Predicting RBP Binding Sites of LncRNA.

Interdiscip Sci 2020 Dec 22;12(4):414-423. Epub 2020 Jun 22.

Dalian Key Lab of Digital Technology for National Culture, Dalian Minzu University, Dalian, 116600, China.

Long non-coding RNA(lncRNA) is one of the non-coding RNAs longer than 200 nucleotides and it has no protein encoding function. LncRNA plays a key role in many biological processes. Studying the RNA-binding protein (RBP) binding sites on the lncRNA chain helps to reveal epigenetic and post-transcriptional mechanisms, to explore the physiological and pathological processes of cancer, and to discover new therapeutic breakthroughs. To improve the recognition rate of RBP binding sites and reduce the experimental time and cost, many calculation methods based on domain knowledge to predict RBP binding sites have emerged. However, these prediction methods are independent of nucleotides and do not take into account nucleotide statistics. In this paper, we use a high-order statistical-based encoding scheme, then the encoded lncRNA sequences are fed into a hybrid deep learning architecture named AC-Caps. It consists of a joint processing layer(composed of attention mechanism and convolutional neural network) and a capsule network. The AC-Caps model was evaluated using 31 independent experimental data sets from 12 lncRNA-binding proteins. In experiments, our method achieves excellent performance, with an average area under the curve (AUC) of 0.967 and an average accuracy (ACC) of 92.5%, which are 0.014, 2.3%, 0.261, 28.9%, 0.189, and 21.8% higher than HOCCNNLB, iDeepS, and DeepBind, respectively. The results show that the AC-Caps method can reliably process the large-scale RBP binding site data on the lncRNA chain, and the prediction performance is better than existing deep-learning models. The source code of AC-Caps and the datasets used in this paper are available at https://github.com/JinmiaoS/AC-Caps .
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s12539-020-00379-3DOI Listing
December 2020

Rapid screening of hepatitis B using Raman spectroscopy and long short-term memory neural network.

Lasers Med Sci 2020 Oct 13;35(8):1791-1799. Epub 2020 Apr 13.

Department of Laboratory Medicine, The First Affiliated Hospital of Xinjiang Medical University, Urumuqi, 830000, China.

This study presents a rapid method to screen hepatitis B patients using serum Raman spectroscopy combined with long short-term memory neural network (LSTM). The serum samples taken from 435 hepatitis B patients and 699 non-hepatitis B people were measured in this experiment. Specific biomolecular changes in three groups of serum samples could be seen in the tentative assignment of Raman peaks. First, principal component analysis (PCA) was used for extracting key features of spectral data, which reduces the dimension of the multidimensional spectrum. Then, LSTM is used to train the spectral data. Finally, the full connection layer completes the classification of HBV. The diagnostic accuracy of the first LSTM model is 97.32%, and the value of AUC is 0.995. The results from the study demonstrate that the combination of serum Raman spectroscopy technique and LSTM provides an effective technical approach to the screening of hepatitis B.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10103-020-03003-4DOI Listing
October 2020
-->