ReviewThe Journal of membrane biology2017
A Treatise to Computational Approaches Towards Prediction of Membrane Protein and Its Subtypes.
Review in The Journal of membrane biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 74 citations in OpenAlex.
- Machine learning driven dashboard for chronic myeloid leukemia prediction using protein sequences.PloS one · 2025Article
- A Framework for Prediction of Oncogenomic Progression Aiding Personalized Treatment of Gastric Cancer.Diagnostics (Basel, Switzerland) · 2023Article
- Ensemble Learning for Hormone Binding Protein Prediction: A Promising Approach for Early Diagnosis of Thyroid Hormone Disorders in Serum.Diagnostics (Basel, Switzerland) · 2023Article
- A machine learning technique for identifying DNA enhancer regions utilizing CIS-regulatory element patterns.Scientific reports · 2022Article
- iAcety-SmRF: Identification of Acetylation Protein by Using Statistical Moments and Random Forest.Membranes · 2022Article
- An analytical study on the identification of N-linked glycosylation sites using machine learning model.PeerJ. Computer science · 2022Article
- Identification of stress response proteins through fusion of machine learning models and statistical paradigms.Scientific reports · 2021Article
- iSUMOK-PseAAC: prediction of lysine sumoylation sites using statistical moments and Chou's PseAAC.PeerJ · 2021Article
- Integrative approach for detecting membrane proteins.BMC bioinformatics · 2020Article
- Identification of 4-carboxyglutamate residue sites based on position based statistical feature and multiple classification.Scientific reports · 2020Article
- Article
- Multiscale Simulations of Biological Membranes: The Challenge To Understand Biological Phenomena in a Living Substance.Chemical reviews · 2019Article
- iMethylK_pseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAACCurrent genomics · 2019Article
- iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical MomentsCurrent genomics · 2019Article
- Article
- iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC.Molecular biology reports · 2018Article
- Predicting membrane proteins and their types by extracting various sequence features into Chou's general PseAAC.Molecular biology reports · 2018Article
- Detection of Weakly Expressed Trypanosoma cruzi Membrane Proteins Using High-Performance Probes.The Journal of eukaryotic microbiology · 2018Article
- A Novel Modeling in Mathematical Biology for Classification of Signal Peptides.Scientific reports · 2018Article
- Prediction of N-linked glycosylation sites using position relative features and statistical moments.PloS one · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Membrane proteins are vital mediating molecules responsible for the interaction of a cell with its surroundings. These proteins are involved in different functionalities such as ferrying of molecules and nutrients across membrane, recognizing foreign bodies, receiving outside signals and translating them into the cell. Membrane proteins play significant role in drug interaction as nearly 50% of the drug targets are membrane proteins. Due to the momentous role of membrane protein in cell activity, computational models able to predict membrane protein with accurate measures bears indispensable importance. The conventional experimental methods used for annotating membrane proteins are time-consuming and costly and in some cases impossible. Computationally intelligent techniques have emerged to be as a useful resource in the automation of prediction and hence the annotation process. In this study, various techniques have been reviewed that are based on different computational intelligence models used for prediction process. These techniques were formulated by different researchers and were further evaluated to provide a comparative analysis. Analysis shows that the usage of support vector machine-based prediction techniques bears more assiduous results.
Indexed as
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What OpenQuestion holds
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.