ArticleComputational and structural biotechnology journal2021
Identification of cyclin protein using gradient boost decision tree algorithm.
Article in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
32 citing papers in PubMed, 73 citations in OpenAlex.
- ac4C modification sites prediction in human mRNA: a complete review.Briefings in bioinformatics · 2026Review
- Enhancing breast cancer diagnosis: non-invasive prediction of MKI-67 (Ki67) expression using ultrasound images.BMC cancer · 2025Article
- Development of robust machine learning models to estimate hydrochar higher heating value and yield based upon biomass proximate analysis.Bioresources and bioprocessing · 2025Article
- Predictive modeling of oil rate for wells under gas lift using machine learning.Scientific reports · 2025Article
- Machine learning models based on routine blood and biochemical test data for diagnosis of neurological diseases.Scientific reports · 2025Article
- Developing a cost-effective tool for choke flow rate prediction in sub-critical oil wells using wellhead data.Scientific reports · 2025Article
- Predicting CO2 adsorption in KOH-activated biochar using advanced machine learning techniques.Scientific reports · 2025Article
- Machine learning frameworks to accurately estimate the adsorption of organic materials onto resin and biochar.Scientific reports · 2025Article
- Human essential gene identification based on feature fusion and feature screening.IET systems biology · 2024Article
- Promoter Prediction in Agrobacterium tumefaciens Strain C58 by Using Artificial Intelligence Strategies.Methods in molecular biology (Clifton, N.J.) · 2024Article
- A First Computational Frame for Recognizing Heparin-Binding Protein.Diagnostics (Basel, Switzerland) · 2023Article
- Computational identification of promoters inFrontiers in microbiology · 2023Article
- Computational prediction of promotors inFrontiers in microbiology · 2023Article
- Deep-STP: a deep learning-based approach to predict snake toxin proteins by using word embeddings.Frontiers in medicine · 2023Article
- Empirical comparison and recent advances of computational prediction of hormone binding proteins using machine learning methods.Computational and structural biotechnology journal · 2023Review
- Altered Functional Connectivity and Complexity in Major Depressive Disorder after Musical Stimulation.Brain sciences · 2022Article
- SCMRSA: a New Approach for Identifying and Analyzing Anti-MRSA Peptides Using Estimated Propensity Scores of Dipeptides.ACS omega · 2022Article
- A Statistical Analysis of the Sequence and Structure of Thermophilic and Non-Thermophilic Proteins.International journal of molecular sciences · 2022Article
- Article
- Deep-4mCGP: A Deep Learning Approach to Predict 4mC Sites inInternational journal of molecular sciences · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cyclin proteins are capable to regulate the cell cycle by forming a complex with cyclin-dependent kinases to activate cell cycle. Correct recognition of cyclin proteins could provide key clues for studying their functions. However, their sequences share low similarity, which results in poor prediction for sequence similarity-based methods. Thus, it is urgent to construct a machine learning model to identify cyclin proteins. This study aimed to develop a computational model to discriminate cyclin proteins from non-cyclin proteins. In our model, protein sequences were encoded by seven kinds of features that are amino acid composition, composition of k-spaced amino acid pairs, tri peptide composition, pseudo amino acid composition, geary correlation, normalized moreau-broto autocorrelation and composition/transition/distribution. Afterward, these features were optimized by using analysis of variance (ANOVA) and minimum redundancy maximum relevance (mRMR) with incremental feature selection (IFS) technique. A gradient boost decision tree (GBDT) classifier was trained on the optimal features. Five-fold cross-validated results showed that our model would identify cyclins with an accuracy of 93.06% and AUC value of 0.971, which are higher than the two recent studies on the same data.
Indexed as
Identifiers
What Socratic 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 Socratic graph.