ArticleOncotarget2017
Pse-Analysis: a python package for DNA/RNA and protein/ peptide sequence analysis based on pseudo components and kernel methods.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- Classification of DNA Sequence Based on a Non-gradient Algorithm: Pseudoinverse Learners.Methods in molecular biology (Clifton, N.J.) · 2024Article
- A machine learning technique for identifying DNA enhancer regions utilizing CIS-regulatory element patterns.Scientific reports · 2022Article
- PSSMCOOL: a comprehensive R package for generating evolutionary-based descriptors of protein sequences from PSSM profiles.Biology methods & protocols · 2022Article
- The Potential Diagnostic and Prognostic Value of Circulating MicroRNAs in the Assessment of Patients With Prostate Cancer: Rational and Progress.Frontiers in oncology · 2021Review
- Review of Machine Learning Methods for the Prediction and Reconstruction of Metabolic Pathways.Frontiers in molecular biosciences · 2021Review
- PyFeat: a Python-based effective feature generation tool for DNA, RNA and protein sequences.Bioinformatics (Oxford, England) · 2019Article
- iMethylK_pseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAACCurrent genomics · 2019Article
- iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites.Briefings in bioinformatics · 2019Review
- iNuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou's PseAAC to pseudo-tri-nucleotide composition.Molecular genetics and genomics : MGG · 2019Article
- AOPs-SVM: A Sequence-Based Classifier of Antioxidant Proteins Using a Support Vector Machine.Frontiers in bioengineering and biotechnology · 2019Article
- Predicting Ion Channels Genes and Their Types With Machine Learning Techniques.Frontiers in genetics · 2019Article
- Predicting subcellular localization of multisite proteins using differently weighted multi-label k-nearest neighbors sets.Technology and health care : official journal of the European Society for Engineering and Medicine · 2019Article
- Hot spot prediction in protein-protein interactions by an ensemble system.BMC systems biology · 2018Article
- IDP⁻CRF: Intrinsically Disordered Protein/Region Identification Based on Conditional Random Fields.International journal of molecular sciences · 2018Article
- Structural insights of dipeptidyl peptidase-IV inhibitors through molecular dynamics-guided receptor-dependent 4D-QSAR studies.Molecular diversity · 2018Article
- Feature extraction method for proteins based on Markov tripeptide by compressive sensing.BMC bioinformatics · 2018Article
- A Novel Modeling in Mathematical Biology for Classification of Signal Peptides.Scientific reports · 2018Article
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- Sc-ncDNAPred: A Sequence-Based Predictor for Identifying Non-coding DNA inFrontiers in microbiology · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To expedite the pace in conducting genome/proteome analysis, we have developed a Python package called Pse-Analysis. The powerful package can automatically complete the following five procedures: (1) sample feature extraction, (2) optimal parameter selection, (3) model training, (4) cross validation, and (5) evaluating prediction quality. All the work a user needs to do is to input a benchmark dataset along with the query biological sequences concerned. Based on the benchmark dataset, Pse-Analysis will automatically construct an ideal predictor, followed by yielding the predicted results for the submitted query samples. All the aforementioned tedious jobs can be automatically done by the computer. Moreover, the multiprocessing technique was adopted to enhance computational speed by about 6 folds. The Pse-Analysis Python package is freely accessible to the public at http://bioinformatics.hitsz.edu.cn/Pse-Analysis/, and can be directly run on Windows, Linux, and Unix.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.