Empirical comparison and recent advances of computational prediction of hormone binding proteins using machine learning methods

Article Biochemistry & Molecular Biology

iRNA-ac4C: A novel computational method for effectively detecting N4-acetylcytidine sites in human mRNA

Wei Su et al.

Summary: A novel predictor, iRNA-ac4C, was developed to identify ac4C sites in human mRNA using three feature extraction methods. The results showed promising generalization capabilities.

INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES (2023)

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Article Biochemical Research Methods

Characterizing viral circRNAs and their application in identifying circRNAs in viruses

Mengting Niu et al.

Summary: Circular RNAs (circRNAs) are special non-coding RNAs with a circular structure, playing important roles in various biological activities. Viral circRNAs, encoded by viruses, have been found in different types of viruses. However, the characteristics and functions of viral circRNAs are still unknown. Comparative analysis reveals that viral circRNAs are less conserved compared to animal circRNAs, suggesting rapid evolution. Furthermore, viral circRNAs show similarities in nucleic acid composition but distinct differences in secondary structure and autocorrelation characteristics compared to animal circRNAs. Based on these characteristics, a machine learning model was developed to predict viral circRNAs. Additionally, analysis indicates potential interactions between viral circRNAs and human miRNAs, as well as their involvement in various KEGG pathways related to the nervous system and cancer.

BRIEFINGS IN BIOINFORMATICS (2022)

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Article Biochemistry & Molecular Biology

Deep-4mCGP: A Deep Learning Approach to Predict 4mC Sites in Geobacter pickeringii by Using Correlation-Based Feature Selection Technique

Hasan Zulfiqar et al.

Summary: The study aimed to establish a robust deep learning model to recognize 4mC sites in Geobacter pickeringii. By using different feature descriptors and optimization algorithms, the accuracy of the model was improved.

INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2022)

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Article Microbiology

iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy

Zahoor Ahmed et al.

Summary: Thermophilic proteins have important application value in biotechnology and industrial processes. A multi-layer perceptron (MLP) model based on a multi-feature fusion strategy was proposed to accurately identify thermophilic proteins. The developed software package iThermo provides a user-friendly platform for conveniently applying the model.

FRONTIERS IN MICROBIOLOGY (2022)

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Article Biochemical Research Methods

STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction

Shaherin Basith et al.

Summary: Protein post-translational modification (PTM) is a crucial regulatory mechanism. Identifying protein lysine acetylation (Kace) sites is challenging, but this study proposes a novel predictor STALLION that accurately identifies Kace sites in prokaryotic species. The predictor utilizes multiple models and feature encodings, outperforming existing predictors in benchmarking experiments.

BRIEFINGS IN BIOINFORMATICS (2022)

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Letter Clinical Neurology

Mendelian randomization highlights causal association between genetically increased C-reactive protein levels and reduced Alzheimer's disease risk

Yang Hu et al.

ALZHEIMERS & DEMENTIA (2022)

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Article Biochemical Research Methods

Deep-4mCW2V: A sequence-based predictor to identify N4-methylcytosine sites in Escherichia coli

Hasan Zulfiqar et al.

Summary: This study developed a deep learning-based model to predict 4mC sites in Escherichia coli. By encoding DNA sequences and utilizing convolutional neural networks for classification, the model can accurately identify 4mC sites, providing a convenient approach for studying 4mC modification.

METHODS (2022)

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Article Biochemical Research Methods

RFhy-m2G: Identification of RNA N2-methylguanosine modification sites based on random forest and hybrid features

Chunyan Ao et al.

Summary: A novel predictor, RFhy-m2G, was developed in this study to identify m2G modification sites using hybrid features and random forest. The predictor achieved high accuracies through feature fusion and optimal feature selection.

METHODS (2022)

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Article Biochemistry & Molecular Biology

Cognitive performance protects against Alzheimer's disease independently of educational attainment and intelligence

Yang Hu et al.

Summary: This study used large-scale genome-wide association studies (GWAS) to investigate the causal relationship between educational attainment, cognitive performance, intelligence, and Alzheimer's disease (AD) through univariable and multivariable Mendelian-randomization (MR) analyses. The findings highlight the protective role of cognitive performance in AD, independently of educational attainment and intelligence.

MOLECULAR PSYCHIATRY (2022)

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Article Engineering, Biomedical

Deep-GHBP: Improving prediction of Growth Hormone-binding proteins using deep learning model

Farman Ali et al.

Summary: This study presents an innovative approach for the prediction of Growth hormone-binding proteins (GHBPs) using PSSM, CTD, Geary, Moran, and PseAAC. Important features were selected using a novel MMC feature selection algorithm and the prediction was done using DNN as a classifier. The results showed that this method outperformed existing predictors in terms of accuracy.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2022)

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Review Oncology

Exosomal Non-Coding RNAs: New Insights into the Biology of Hepatocellular Carcinoma

Qian Zhang et al.

Summary: Exosomes are the smallest extracellular vesicles released by cells, carrying various non-coding RNAs that play critical roles in the occurrence and progression of hepatocellular carcinoma.

CURRENT ONCOLOGY (2022)

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Article Biochemistry & Molecular Biology

Comprehensive Prediction of Lipocalin Proteins Using Artificial Intelligence Strategy

Hasan Zulfiqar et al.

Summary: In this study, an advanced computational model was developed to identify lipocalins using six features and feature selection techniques. The results demonstrated high accuracy and performance of the model in classification.

FRONTIERS IN BIOSCIENCE-LANDMARK (2022)

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Article Biochemical Research Methods

Analysis of Codon Usage Patterns in the Human Papillomavirus Oncogenes

Myeongji Cho et al.

Summary: This study analyzed the codon usage patterns in the E6 and E7 genes of high-risk HPV subtypes and found a bias for 'A' nucleotides. The results indicate that selection pressure plays a significant role in influencing the codon usage patterns and composition changes in the HPV genes.

CURRENT BIOINFORMATICS (2021)

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Article Biochemical Research Methods

Deep-BSC: Predicting Raw DNA Binding Pattern in Arabidopsis thaliana

Syed Adnan Shah Bukhari et al.

Summary: The study introduces a new deep learning architecture, Deep-BSC, to predict binding sites in DNA sequences, achieving good results.

CURRENT BIOINFORMATICS (2021)

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Article Biochemical Research Methods

iCarPS: a computational tool for identifying protein carbonylation sites by novel encoded features

Dan Zhang et al.

Summary: In this study, a predictor called iCarPS was developed to identify carbonylation sites based on sequence information, utilizing a novel feature encoding scheme and feature selection technique. Experiments demonstrated the accuracy and robustness of iCarPS, showing its powerful performance in carbonylation sites identification compared to other published methods.

BIOINFORMATICS (2021)

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Article Biochemical Research Methods

RaacLogo: a new sequence logo generator by using reduced amino acid clusters

Lei Zheng et al.

Summary: RaacLogo is a new sequence logo generator that can easily generate customized simplified logos for users by using reduced amino acid alphabets. The website provides 74 types of reduced amino acid alphabets, and a two-dimensional selector is proposed for easily selecting desired reduced amino acid clusters. It is anticipated that RaacLogo web server will play a more high-potential roles in protein sequence alignment, topological estimation and protein design experiments.

BRIEFINGS IN BIOINFORMATICS (2021)

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Article Biochemical Research Methods

Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework

Md Mehedi Hasan et al.

Summary: DNA N-6-methyladenine (6mA) is a crucial epigenetic modification responsible for various cellular processes. Accurate identification of 6mA sites is challenging in genome analysis, and existing machine learning models have limited practical application across different plant species. In this study, Meta-i6mA, combining 30 baseline models, performed exceptionally well in independent tests on Rosaceae, rice, and Arabidopsis thaliana, showing higher Matthews correlation coefficient values compared to existing predictors.

BRIEFINGS IN BIOINFORMATICS (2021)

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Article Medicine, General & Internal

rs1990622 variant associates with Alzheimer's disease and regulates TMEM106B expression in human brain tissues

Yang Hu et al.

Summary: The study revealed that the TMEM106B gene rs1990622 variant T allele increased the risk of Alzheimer's disease (AD), particularly in females; TMEM106B showed differential expression in different human brain tissues with high expression in the cerebellum; the rs1990622 variant could regulate TMEM106B expression in human brain tissues, with colocalization analysis supporting its impact on AD risk and TMEM106B expression.

BMC MEDICINE (2021)

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Article Biochemical Research Methods

DeepYY1: a deep learning approach to identify YY1-mediated chromatin loops

Fu-Ying Dao et al.

Summary: YY1 protein forms dimers that enhance enhancer-promoter interactions, a general feature of mammalian gene control. A deep learning algorithm named DeepYY1 has been developed to efficiently identify YY1-mediated chromatin loops. Sequences play a crucial role in the formation of YY1-mediated chromatin loops.

BRIEFINGS IN BIOINFORMATICS (2021)

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Article Biochemical Research Methods

Computational prediction of species-specific yeast DNA replication origin via iterative feature representation

Balachandran Manavalan et al.

Summary: DNA replication is a crucial task in cells that requires precise regulation. A novel species-specific yeast predictor called yORIpred has been developed to accurately identify replication origins (ORIs) in yeast genomes, showing superior and stable performance compared to existing predictors.

BRIEFINGS IN BIOINFORMATICS (2021)

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Article Mathematical & Computational Biology

Screening of Prospective Plant Compounds as H1R and CL1R Inhibitors and Its Antiallergic Efficacy through Molecular Docking Approach

Hasan Zulfiqar et al.

Summary: This research utilized molecular docking to identify the best inhibitors against H1R and CL1R, and evaluate their anti-allergic efficacy. This in silico study will aid in the design of novel, safe, and cost-effective drugs in the future to enhance the quality of human life.

COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE (2021)

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Article Biochemistry & Molecular Biology

sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks

Mengting Niu et al.

Summary: The study introduced an ensemble convolutional neural network model to accurately predict high on-target sgRNA activity in four crops, demonstrating the importance of machine learning methods in guiding crop gene editing and academic research.

PLANT MOLECULAR BIOLOGY (2021)

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Article Biochemical Research Methods

eHSCPr discriminating the cell identity involved in endothelial to hematopoietic transition

Hao Wang et al.

Summary: The study introduced a predictor named eHSCPr for predicting the early stages of HSCs development. By comparing different gene selection methods, F-score captured critical surface markers and SVM analysis achieved an accuracy of over 94%, confirming important markers for HSCs development stages.

BIOINFORMATICS (2021)

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Article Biochemical Research Methods

A sequence-based deep learning approach to predict CTCF-mediated chromatin loop

Hao Lv et al.

Summary: The three-dimensional architecture of chromosomes is crucial for transcription regulation and DNA replication, with Deep-loop, a convolutional neural network model, showing excellent performance in predicting chromatin loops from different cell types based on high-throughput data. The source code of Deep-loop is freely available for use.

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Article Medicine, Research & Experimental

iTTCA-RF: a random forest predictor for tumor T cell antigens

Shihu Jiao et al.

Summary: The study developed a predictor called iTTCA-RF for identifying tumor T cell antigens, which outperformed other latest models. The predictor showed satisfactory performance with balanced accuracy, specificity, and sensitivity values over cross-validation and independent tests. The online prediction server for iTTCA-RF is freely accessible online.

JOURNAL OF TRANSLATIONAL MEDICINE (2021)

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Article Biochemical Research Methods

Prediction of Protein-protein Interactions in Arabidopsis thaliana Using Partial Training Samples in a Machine Learning Framework

Fee Faysal Ahmed et al.

Summary: The study developed an effective PPI prediction model for Arabidopsis thaliana using partial training samples in a machine learning framework, combining random forest classifier and autocorrelation sequence encoding features. The proposed model showed higher performance scores in both training and test datasets compared to other candidate predictors, suggesting its potential utility in elucidating the biological function of unseen PPIs in Arabidopsis thaliana.

CURRENT BIOINFORMATICS (2021)

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Article Computer Science, Artificial Intelligence

Risk Prediction of Diabetes: Big data mining with fusion of multifarious physical examination indicators

Hui Yang et al.

Summary: The study designed a computational system to predict diabetes risk by combining various types of physical examination data. Statistical analysis was conducted on different physical examination indexes to develop a model that can distinguish diabetes patients from healthy individuals. A diabetes risk scorecard was established to improve the convenience and flexibility of the model. Lastly, an online diabetes risk assessment system was set up to enhance diabetes cascade screening and personal lifestyle management.

INFORMATION FUSION (2021)

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Article Biochemistry & Molecular Biology

PPD: A Manually Curated Database for Experimentally Verified Prokaryotic Promoters

Wei Su et al.

Summary: This study established a Prokaryotic Promoter Database (PPD) which manually extracted 129,148 experimentally validated promoters across 63 prokaryotic species from published papers, providing users with a user-friendly interface for browsing, searching, visualizing, submitting, and downloading data.

JOURNAL OF MOLECULAR BIOLOGY (2021)

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Article Biochemical Research Methods

NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning

Md Mehedi Hasan et al.

Summary: Neuropeptides play a crucial role in regulating immune systems, and rapid and accurate identification of them is essential for basic research and drug development. Using machine learning and feature representation learning, the developed NeuroPred-FRL predictor demonstrates superior prediction performance, serving as a powerful tool for large-scale identification of neuropeptides.

BRIEFINGS IN BIOINFORMATICS (2021)

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Article Biochemical Research Methods

StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides

Phasit Charoenkwan et al.

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BRIEFINGS IN BIOINFORMATICS (2021)

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Article Genetics & Heredity

Prediction of Hormone-Binding Proteins Based on K-mer Feature Representation and Naive Bayes

Yuxin Guo et al.

Summary: The study focuses on the characteristics and identification methods of hormone binding proteins, successfully establishing a prediction model HBP_NB, using high-quality dataset and feature selection algorithm to accurately identify HBPs.

FRONTIERS IN GENETICS (2021)

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Article Biochemical Research Methods

DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor

Zhaochun Xu et al.

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Integrated biomarker profiling of the metabolome associated with impaired fasting glucose and type 2 diabetes mellitus in large-scale Chinese patients

Jianglan Long et al.

CLINICAL AND TRANSLATIONAL MEDICINE (2021)

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Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design

Hao Lv et al.

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BRIEFINGS IN BIOINFORMATICS (2021)

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Article Biochemical Research Methods

DeepIPs: comprehensive assessment and computational identification of phosphorylation sites of SARS-CoV-2 infection using a deep learning-based approach

Hao Lv et al.

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Article Biochemistry & Molecular Biology

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Hasan Zulfiqar et al.

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COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL (2021)

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Article Mathematical & Computational Biology

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Empirical comparison and recent advances of computational prediction of hormone binding proteins using machine learning methods

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