| 1. |
- Yang, Yang, et al.
(författare)
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NDDVD : an integrated and manually curated Neurodegenerative Diseases Variation Database
- 2018
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Ingår i: Database: the journal of biological databases and curation. - : Oxford University Press (OUP). - 1758-0463. ; 2018
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Tidskriftsartikel (refereegranskat)abstract
- Neurodegenerative diseases (NDDs) are associated with genetic variations including point substitutions, copy number alterations, insertions and deletions. At present, a few genetic variation repositories for some individual NDDs have been created, however, these databases are needed to be integrated and expanded to all the NDDs for systems biological investigation. We here build a relational database termed as NDDVD to integrate all the variations of NDDs using Leiden Open Variation Database (LOVD) platform. The items in the NDDVD are collected manually from PubMed or extracted from the existed variation databases. The cross-disease database includes over 6374 genetic variations of 289 genes associated with 37 different NDDs. The patterns, conservations and biological functions for variations in different NDDs are statistically compared and a user-friendly interface is provided for NDDVD at: http://bioinf.suda.edu.cn/NDDvarbase/LOVDv.3.0.
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| 2. |
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| 3. |
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| 4. |
- Yang, Yang, et al.
(författare)
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PON-Sol : Prediction of effects of amino acid substitutions on protein solubility
- 2016
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 32:13, s. 2032-2034
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Solubility is one of the fundamental protein properties. It is of great interest because of its relevance to protein expression. Reduced solubility and protein aggregation are also associated with many diseases. Results: We collected from literature the largest experimentally verified solubility affecting amino acid substitution (AAS) dataset and used it to train a predictor called PON-Sol. The predictor can distinguish both solubility decreasing and increasing variants from those not affecting solubility. PONSol has normalized correct prediction ratio of 0.491 on cross-validation and 0.432 for independent test set. The performance of the method was compared both to solubility and aggregation predictors and found to be superior. PON-Sol can be used for the prediction of effects of disease-related substitutions, effects on heterologous recombinant protein expression and enhanced crystallizability. One application is to investigate effects of all possible AASs in a protein to aid protein engineering.
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| 5. |
- Yang, Yang, et al.
(författare)
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Pon-tstab : Protein variant stability predictor. importance of training data quality
- 2018
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Ingår i: International Journal of Molecular Sciences. - : MDPI AG. - 1661-6596 .- 1422-0067. ; 19:4
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Tidskriftsartikel (refereegranskat)abstract
- Several methods have been developed to predict effects of amino acid substitutions on protein stability. Benchmark datasets are essential for method training and testing and have numerous requirements including that the data is representative for the investigated phenomenon. Available machine learning algorithms for variant stability have all been trained with ProTherm data. We noticed a number of issues with the contents, quality and relevance of the database. There were errors, but also features that had not been clearly communicated. Consequently, all machine learning variant stability predictors have been trained on biased and incorrect data. We obtained a corrected dataset and trained a random forests-based tool, PON-tstab, applicable to variants in any organism. Our results highlight the importance of the benchmark quality, suitability and appropriateness. Predictions are provided for three categories: stability decreasing, increasing and those not affecting stability.
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| 6. |
- Yang, Yang, et al.
(författare)
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Structure-based prediction of the effects of a missense variant on protein stability
- 2013
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Ingår i: Amino Acids. - : Springer Science and Business Media LLC. - 0939-4451 .- 1438-2199. ; 44:3, s. 847-855
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Tidskriftsartikel (refereegranskat)abstract
- Predicting the effects of amino acid substitutions on protein stability provides invaluable information for protein design, the assignment of biological function, and for understanding disease-associated variations. To understand the effects of substitutions, computational models are preferred to time-consuming and expensive experimental methods. Several methods have been proposed for this task including machine learning-based approaches. However, models trained using limited data have performance problems and many model parameters tend to be over-fitted. To decrease the number of model parameters and to improve the generalization potential, we calculated the amino acid contact energy change for point variations using a structure-based coarse-grained model. Based on the structural properties including contact energy (CE) and further physicochemical properties of the amino acids as input features, we developed two support vector machine classifiers. M47 predicted the stability of variant proteins with an accuracy of 87 % and a Matthews correlation coefficient of 0.68 for a large dataset of 1925 variants, whereas M8 performed better when a relatively small dataset of 388 variants was used for 20-fold cross-validation. The performance of the M47 classifier on all six tested contingency table evaluation parameters is better than that of existing machine learning-based models or energy function-based protein stability classifiers.
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| 7. |
- Zhang, Xueli, 1991-
(författare)
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Biomarkers for Diagnosis, Therapy and Prognosis in Colorectal Cancer : a study from databases, machine learning predictions to laboratory confirmations
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Colorectal cancer (CRC) is one of the leading causes of cancer death worldwide. Early diagnosis and better therapy response have been believed to be associated with better prognosis. CRC biomarkers are considered as precise indicators for the early diagnosis and better therapy response. It is, therefore, of importance to find out, analyze and evaluate the CRC biomarkers to further provide the more precis evidence for predicting novel potential biomarkers and eventually to improve early diagnosis, personalized therapy and prognosis for CRC.In this study, we started with creating and establishing a CRC biomarker database. (CBD: http://sysbio.suda.edu.cn/CBD/index.html) In the CBD database, there were 870 reported CRC biomarkers collected from the published articles in PubMed. In this version of the CBD, CRC biomarker data was carefully collected, sorted, displayed, and analyzed. The major applications of the CBD are to provide 1) the records of CRC biomarkers (DNA, RNA, protein and others) concerning diagnosis, treatment and prognosis; 2) the basic and clinical research information concerning the CRC biomarkers; 3) the primary results for bioinformatics and biostatics analysis of the CRC biomarkers; 4) downloading/uploading the biomedicine information for CRC biomarkers.Based on our CBD and other public databases, we further analyzed the presented CRC biomarkers (DNAs, RNAs, proteins) and predicted novel potential multiple biomarkers (the combination of single biomarkers) with biological networks and pathways analysis for diagnosis, therapy response and prognosis in CRC. We found several hub biomarkers and key pathways for the diagnosis, treatment and prognosis in CRC. Receiver operating characteristic (ROC) test and survival analysis by microarray data revealed that multiple biomarkers could be better biomarkers than the single biomarkers for the diagnosis and prognosis of CRC.There are 62 diagnosis biomarkers for colon cancer in our CBD. In the previous studies, we found these present biomarkers were not enough to improve significantly the diagnosis of colon cancer. In order to find out novel biomarkers for the colon cancer diagnosis, we have performed /machine learning (ML) techniques such as support vector machine (SVM) and regression tree to predict candidate to discover diagnostic biomarkers for colon cancer. Based on the protein-protein interaction (PPI) network topology features of the identified biomarkers, we found 12 protein biomarkers which were considered as the candidate colon cancer diagnosis biomarkers. Among these protein biomarkers Chromogranin-A (CHGA) was the most powerful biomarker, which showed good performance in bioinformatics test and Immunohistochemistry(IHC). We are now expanding this study to CRC.Expression of CHGA protein in colon cancer was further verified with a novel logistic regressionbased meta-analysis, and convinced as a valuable diagnostic biomarker as compared with the typical diagnostic biomarkers, such as TP53, KRAS and MKI67.microRNAs (miRNAs/miRs) have been considered as potential biomarkers. A novel miRNA-mRNA interaction network-based model was used to predict miRNA biomarkers for CRC and found that miRNA-186-5p, miRNA-10b-5p and miRNA-30e-5p might be the novel biomarkers for CRC diagnosis. In conclusion, we have created a useful CBD database for CRC biomarkers and provided detailed information for how to use the CBD in CRC biomarker investigations. Our studies have been focusing on the biomarkers in diagnosis, therapy and prognosis. Based on our CBD and other powerful cancer associated databases, ML has been used to analyze the characteristics of the CRC biomarkers and predict novel potential CRC biomarkers. The predicted potential biomarkers were further confirmed at biomedical laboratory.
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| 8. |
- Zhang, Xueli, 1990-, et al.
(författare)
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CBD : a biomarker database for colorectal cancer
- 2018
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Ingår i: Database. - : Oxford University Press. - 1758-0463.
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Tidskriftsartikel (refereegranskat)abstract
- Colorectal cancer (CRC) biomarker database (CBD) was established based on 870 identified CRC biomarkers and their relevant information from 1115 original articles in PubMed published from 1986 to 2017. In this version of the CBD, CRC biomarker data were collected, sorted, displayed and analysed. The CBD with the credible contents as a powerful and time-saving tool provide more comprehensive and accurate information for further CRC biomarker research. The CBD was constructed under MySQL server. HTML, PHP and JavaScript languages have been used to implement the web interface. The Apache was selected as HTTP server. All of these web operations were implemented under the Windows system. The CBD could provide to users the multiple individual biomarker information and categorized into the biological category, source and application of biomarkers; the experiment methods, results, authors and publication resources; the research region, the average age of cohort, gender, race, the number of tumours, tumour location and stage. We only collect data from the articles with clear and credible results to prove the biomarkers are useful in the diagnosis, treatment or prognosis of CRC. The CBD can also provide a professional platform to researchers who are interested in CRC research to communicate, exchange their research ideas and further design high-quality research in CRC. They can submit their new findings to our database via the submission page and communicate with us in the CBD.
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| 9. |
- Zhang, Xueli, 1990-, et al.
(författare)
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Chromogranin-A Expression as a Novel Biomarker for Early Diagnosis of Colon Cancer Patients
- 2019
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Ingår i: International Journal of Molecular Sciences. - : MDPI. - 1661-6596 .- 1422-0067. ; 20:12
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Tidskriftsartikel (refereegranskat)abstract
- Colon cancer is one of the major causes of cancer death worldwide. The five-year survival rate for the early-stage patients is more than 90%, and only around 10% for the later stages. Moreover, half of the colon cancer patients have been clinically diagnosed at the later stages. It is; therefore, of importance to enhance the ability for the early diagnosis of colon cancer. Taking advantages from our previous studies, there are several potential biomarkers which have been associated with the early diagnosis of the colon cancer. In order to investigate these early diagnostic biomarkers for colon cancer, human chromogranin-A (CHGA) was further analyzed among the most powerful diagnostic biomarkers. In this study, we used a logistic regression-based meta-analysis to clarify associations of CHGA expression with colon cancer diagnosis. Both healthy populations and the normal mucosa from the colon cancer patients were selected as the double normal controls. The results showed decreased expression of CHGA in the early stages of colon cancer as compared to the normal controls. The decline of CHGA expression in the early stages of colon cancer is probably a new diagnostic biomarker for colon cancer diagnosis with high predicting possibility and verification performance. We have also compared the diagnostic powers of CHGA expression with the typical oncogene KRAS, classic tumor suppressor TP53, and well-known cellular proliferation index MKI67, and the CHGA showed stronger ability to predict early diagnosis for colon cancer than these other cancer biomarkers. In the protein-protein interaction (PPI) network, CHGA was revealed to share some common pathways with KRAS and TP53. CHGA might be considered as a novel, promising, and powerful biomarker for early diagnosis of colon cancer.
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| 10. |
- Zhang, Xueli, et al.
(författare)
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Loss of CHGA Protein as a Potential Biomarker for Colon Cancer Diagnosis : A Study on Biomarker Discovery by Machine Learning and Confirmation by Immunohistochemistry in Colorectal Cancer Tissue Microarrays
- 2022
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Ingår i: Cancers. - Basel, Switzerland : MDPI. - 2072-6694. ; 14:11
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: The incidence of colorectal cancers has been constantly increasing. Although the mortality has slightly decreased, it is far from satisfaction. Precise early diagnosis for colorectal cancer has been a great challenge in order to improve patient survival.PATIENTS AND METHODS: We started with searching for protein biomarkers based on our colorectal cancer biomarker database (CBD), finding differential expressed genes (GEGs) and non-DEGs from RNA sequencing (RNA-seq) data, and further predicted new biomarkers of protein-protein interaction (PPI) networks by machine learning (ML) methods. The best-selected biomarker was further verified by a receiver operating characteristic (ROC) test from microarray and RNA-seq data, biological network, and functional analysis, and immunohistochemistry in the tissue arrays from 198 specimens.RESULTS: There were twelve proteins (MYO5A, CHGA, MAPK13, VDAC1, CCNA2, YWHAZ, CDK5, GNB3, CAMK2G, MAPK10, SDC2, and ADCY5) which were predicted by ML as colon cancer candidate diagnosis biomarkers. These predicted biomarkers showed close relationships with reported biomarkers of the PPI network and shared some pathways. An ROC test showed the CHGA protein with the best diagnostic accuracy (AUC = 0.9 in microarray data and 0.995 in RNA-seq data) among these candidate protein biomarkers. Furthermore, immunohistochemistry examination on our colon cancer tissue microarray samples further confirmed our bioinformatical prediction, indicating that CHGA may be used as a potential biomarker for early diagnosis of colon cancer patients.CONCLUSIONS: CHGA could be a potential candidate biomarker for diagnosing earlier colon cancer in the patients.
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