Article
Biochemical Research Methods
Shaherin Basith, Md Mehedi Hasan, Gwang Lee, Leyi Wei, Balachandran Manavalan
Summary: Enhancers are DNA fragments that enhance the transcription of related genes when bound by transcription factors. Identifying enhancers from the human genome is challenging due to their sporadic distribution and similar fractions. The proposed integrative machine learning framework, Enhancer-IF, showed excellent prediction performance in identifying cell-specific enhancers across different cell types, highlighting its superiority in enhancer identification.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Mir Tanveerul Hassan, Hilal Tayara, Kil To Chong
Summary: The cytokine interleukin-4 (IL-4) guides the differentiation of naive T-helper 0 cells (Th0) to T-helper 2 cells (Th2) and is involved in various immune responses. We propose an ensemble model for predicting IL-4 inducing peptides and achieved high accuracy with the Meta-IL4 model. These predictions could assist in developing peptides that elicit the appropriate Th2 response.
Article
Chemistry, Multidisciplinary
Jiayu Li, Jici Jiang, Hongdi Pei, Zhibin Lv
Summary: A new IL-10-induced peptide recognition method called IL10-Stack was introduced in this research, which utilized unified deep representation learning and a stacking algorithm. Feature extraction from peptide sequences was done using two approaches, Amino Acid Index (AAindex) and sequence-based unified representation (UniRep). The IL10-Stack model, constructed using a 1900-dimensional UniRep feature vector, demonstrated excellent performance in IL-10-induced peptide recognition with an accuracy of 0.910 and MCC of 0.820. Compared to existing methods, IL-10Pred and ILeukin10Pred, the IL10-Stack approach showed improved accuracy by 12.1% and 2.4% respectively. The IL10-Stack method has the potential to identify IL-10-induced peptides, aiding in the development of immunosuppressive drugs.
APPLIED SCIENCES-BASEL
(2023)
Article
Biochemistry & Molecular Biology
Fuyi Li, Xudong Guo, Dongxu Xiang, Miranda E. Pitt, Arnold Bainomugisa, Lachlan J. M. Coin
Summary: This study developed a machine learning-based bioinformatics approach called PEPPER to rapidly and accurately identify PE_PGRS proteins, which is crucial for functional elucidation.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2022)
Article
Biochemistry & Molecular Biology
Fuyi Li, Xudong Guo, Dongxu Xiang, Miranda E. Pitt, Arnold Bainomugisa, Lachlan J. M. Coin
Summary: In this study, a machine learning-based bioinformatics approach called PEPPER was developed to rapidly and accurately identify PE_PGRS proteins. Experimental results showed that PEPPER outperformed existing methods in terms of prediction accuracy and speed.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2022)
Article
Biochemical Research Methods
Anjali Dhall, Sumeet Patiyal, Neelam Sharma, Salman Sadullah Usmani, Gajendra P. S. Raghava
Summary: IL-6 plays a crucial role in the progression of COVID-19, and a method for predicting IL-6 inducing peptides has been developed using machine learning techniques. By identifying IL-6 inducing peptides in different proteins of SARS-CoV-2, the study offers new insights for designing vaccines against COVID-19.
BRIEFINGS IN BIOINFORMATICS
(2021)
Review
Cell Biology
Maxim N. Shokhirev, Adiv A. Johnson
Summary: Alzheimer's disease, an incurable age-related brain disorder, was studied through the analysis of genes, proteins, and microRNAs. The results showed that factors such as cell death, cellular senescence, energy metabolism, genomic integrity, and glia were associated with the disease. These factors exhibited unique characteristics in different age groups.
AGEING RESEARCH REVIEWS
(2022)
Article
Biology
Phasit Charoenkwan, Nalini Schaduangrat, Pietro Lio, Mohammad Ali Moni, Balachandran Manavalan, Watshara Shoombuatong
Summary: This study proposes a novel computational approach, NEPTUNE, for the accurate and large-scale identification of Tumor Homing Peptides (THPs) from sequence information. The results demonstrate that NEPTUNE achieves superior performance in THP prediction and improves interpretability using the SHapley additive explanations method.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Review
Biology
Watshara Shoombuatong, Nalini Schaduangrat, Jaru Nikom
Summary: Efficient and precise identification of drug targets is crucial for drug development. Computational approaches, particularly those utilizing machine learning, offer an efficient means to accelerate the prediction of drugable proteins. This study provides a comprehensive assessment of the strengths and weaknesses of various computational methods for predicting and analyzing druggable proteins, and offers guidance for designing and developing novel prediction models.
Article
Biology
Fei Li, Shuai Liu, Kewei Li, Yaqi Zhang, Meiyu Duan, Zhaomin Yao, Gancheng Zhu, Yutong Guo, Ying Wang, Lan Huang, Fengfeng Zhou
Summary: DNA methylation is a major epigenetic modification that regulates biological processes without altering the DNA sequence. This study proposes a feature representation framework called EpiTEAmDNA, which integrates convolutional neural network and conventional machine learning methods. It shows improved performances compared to existing deep learning methods on small datasets across multiple DNA methylation types.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biochemical Research Methods
Yan Zhu, Fuyi Li, Dongxu Xiang, Tatsuya Akutsu, Jiangning Song, Cangzhi Jia
Summary: A promoter is a region in the DNA sequence that defines where gene transcription begins, and identifying promoters is crucial for understanding gene transcriptional regulation. Computational techniques are effective tools for annotating promoters, and Depicter, a deep learning-based method, helps identify different types of promoter sequences. Extensive testing shows that Depicter outperforms other state-of-the-art methods in predictive performance.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Geosciences, Multidisciplinary
Khanh Pham, Dongku Kim, Sangyeong Park, Hangseok Choi
Summary: This study utilized ensemble learning to develop a classification model for accurately estimating slope stability and demonstrated the superiority of ensemble classifiers over single-learning models. The performance of ensemble classifiers varied slightly depending on the learning techniques employed, with extreme gradient boosting framework showing the best performance.
Article
Biochemistry & Molecular Biology
Andre da Costa, Ricardo Franco-Duarte, Raul Machado, Andreia C. Gomes
Summary: This study analyzes the sequence diversity of IL-6 cytokine family and reveals their diversity and characteristics in different organisms. The study finds that conserved residues have an impact on the structural features of the proteins. Parameters such as GRAVY, isoelectric point, and molecular weight are important for differentiating protein classes. Additionally, OSM sequences in primates show unique changes in the BC loop, which may affect its binding to the receptor and signaling pathways. This study highlights the importance of sequence diversity analysis in understanding the evolution of IL-6 cytokine family.
Article
Biochemistry & Molecular Biology
Andre da Costa, Ricardo Franco-Duarte, Raul Machado, Andreia C. Gomes
Summary: This study explores the sequence diversity of the IL-6 cytokine family among different organisms, revealing conserved binding sites and protein-dependent characteristics. Using machine learning approaches, the prediction of organism class and protein type was achieved with high fidelity. Additionally, primates showed distinct features in the OSM sequences compared to other mammals, potentially influencing signaling pathways.
Editorial Material
Immunology
Travis E. Faust, Dorothy P. Schafer
Summary: The study reveals that the pro-inflammatory cytokine IL-6 specifically increases synaptogenesis in immature excitatory neurons through downstream neuronal STAT3-dependent transcriptional regulation of Rgs4.
Article
Biochemical Research Methods
Xin Zhang, Lesong Wei, Xiucai Ye, Kai Zhang, Saisai Teng, Zhongshen Li, Junru Jin, Minjae Kim, Tetsuya Sakurai, Lizhen Cui, Balachandran Manavalan, Leyi Wei
Summary: A novel deep learning framework SiameseCPP is proposed for automated prediction of cell-penetrating peptides (CPPs). SiameseCPP learns discriminative representations of CPPs based on a well-pretrained model and a Siamese neural network comprising a transformer and gated recurrent units. Comprehensive experiments demonstrate that SiameseCPP outperforms existing baseline models for CPP prediction and exhibits satisfactory generalization ability on other functional peptide datasets.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biology
Phasit Charoenkwan, Chonlatip Pipattanaboon, Chanin Nantasenamat, Md Mehedi Hasan, Mohammad Ali Moni, Pietro Lio, Watshara Shoombuatong
Summary: Despite existing cancer therapies, the development of new and effective treatments is necessary to address the ongoing cancer recurrence and new cases. This study proposes a new machine learning-based approach, PSRTTCA, for improving the identification and characterization of tumor T cell antigens (TTCAs) based on their primary sequences.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biochemical Research Methods
Kijin Kim, Kyungmin Park, Seonghyeon Lee, Seung-Hwan Baek, Tae-Hun Lim, Jongwoo Kim, Balachandran Manavalan, Jin-Won Song, Won-Keun Kim
Summary: VirPipe is a new pipeline for detecting viral genomes from Nanopore or Illumina sequencing, with streamlined installation and customization.
Article
Biology
Saraswathy Nithiyanandam, Vinoth Kumar Sangaraju, Balachandran Manavalan, Gwang Lee
Summary: Protein folding is a complex process where a polymer of amino acids transitions from an unfolded state to a unique three-dimensional structure. Previous studies have identified structural parameters and examined their relationship with protein folding rate, but these parameters are only applicable to a limited set of proteins. Machine learning models have been proposed, but they fail to explain plausible folding mechanisms. In this study, ten different machine learning algorithms were evaluated using various structural parameters and network centrality measures, with support vector machine showing the best predictive capability.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biology
Phasit Charoenkwan, Pramote Chumnanpuen, Nalini Schaduangrat, Changmin Oh, Balachandran Manavalan, Watshara Shoombuatong
Summary: In this study, a novel computational approach called PSRQSP was developed to improve the prediction and analysis of QSPs. Experimental results showed that PSRQSP outperformed conventional methods in identifying QSPs and demonstrated its predictive capability and effectiveness. PSRQSP also constructed an easy-to-use web server for accelerating the discovery of potential QSPs for drug development.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biochemistry & Molecular Biology
Phasit Charoenkwan, Nalini Schaduangrat, Nhat Truong Pham, Balachandran Manavalan, Watshara Shoombuatong
Summary: Proposed the first stack-based approach, Pretoria, for accurate and large-scale identification of CD8+ T-cell epitopes (TCEs) of eukaryotic pathogens. Constructed a pool of 144 different machine learning (ML)-based classifiers based on 12 popular ML algorithms and used feature selection method to determine important ML classifiers for building the stacked model. Experimental results demonstrated that Pretoria outperformed several conventional ML classifiers and the existing method, with an accuracy of 0.866, MCC of 0.732, and AUC of 0.921 in the independent test.
INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES
(2023)
Article
Biochemistry & Molecular Biology
Ahmad Firoz, Adeel Malik, Hani Mohammed Ali, Yusuf Akhter, Balachandran Manavalan, Chang-Bae Kim
Summary: In this study, a new two-layer hybrid framework called PRR-HyPred was constructed to simultaneously predict and classify PRRs. Using support vector machine and random forest-based classifier, PRR-HyPred achieved accuracies of 83.4% and 95% in the first and second layers respectively. This is the first study that can predict and classify PRRs into specific families, and it can be a valuable tool for large-scale PRR prediction and classification, facilitating future studies.
INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES
(2023)
Article
Biochemistry & Molecular Biology
Tianshi Yu, Tianyang Huang, Leiye Yu, Chanin Nantasenamat, Nuttapat Anuwongcharoen, Theeraphon Piacham, Ruobing Ren, Ying-Chih Chiang
Summary: Researchers studied Cytochrome P450 17A1 (CYP17A1), a key enzyme in steroidogenesis, and its potential as a druggable target for anti-cancer molecule development. They used cheminformatic analyses and quantitative structure-activity relationship (QSAR) modeling on a dataset of CYP17A1 inhibitors. Different models were built for steroidal and nonsteroidal inhibitors, achieving good accuracy. The findings provide valuable insights for further drug discovery efforts targeting CYP17A1 inhibitors.
Article
Computer Science, Artificial Intelligence
Diponkor Bala, Md. Shamim Hossain, Mohammad Alamgir Hossain, Md. Ibrahim Abdullah, Md. Mizanur Rahman, Balachandran Manavalan, Naijie Gu, Mohammad S. Islam, Zhangjin Huang
Summary: The monkeypox virus poses a new pandemic threat. However, there is currently no reliable monkeypox database available for training and testing deep learning models. The MSID dataset has been developed for this purpose, providing a collection of monkeypox patient images for building confident deep learning models. The proposed MonkeyNet model can accurately identify monkeypox disease and assist doctors in making early diagnoses.
Article
Biology
Duanzhi Wu, Xin Fang, Kai Luan, Qijin Xu, Shiqi Lin, Shiying Sun, Jiaying Yang, Bingying Dong, Balachandran Manavalan, Zhijun Liao
Summary: In this study, SH2 domain-containing proteins and non-SH2 domain-containing proteins were successfully identified using deep learning technology. The best performing 288-dimensional features were obtained. Additionally, a new motif, YKIR, in the SH2 domain was discovered and its function in signal transduction was analyzed.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Nhat Truong Pham, Duc Ngoc Minh Dang, Ngoc Duy Nguyen, Thanh Thi Nguyen, Hai Nguyen, Balachandran Manavalan, Chee Peng Lim, Sy Dzung Nguyen
Summary: This paper proposes a deep learning framework for speech emotion recognition, which combines a hybrid data augmentation method and deep attention-based dilated convolutional-recurrent neural networks. The framework is able to extract high-level representations from three-dimensional log Mel spectrogram features. Experimental results show that the proposed framework outperforms other state-of-the-art methods on the EmoDB and ERC datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Biology
Shaherin Basith, Balachandran Manavalan, Gwang Lee
Summary: This study combined microsecond-scale unbiased molecular dynamics simulation with network analysis to elucidate the local and global conformational changes and allosteric communications in SOD1 systems. Structural analyses revealed significant variations in catalytic sites and stability due to unmetallated SOD1 systems and cysteine mutations. Dynamic motion analysis showed balanced atomic displacement and highly correlated motions in the Holo system.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Multidisciplinary Sciences
Phasit Charoenkwan, Sajee Waramit, Pramote Chumnanpuen, Nalini Schaduangrat, Watshara Shoombuatong
Summary: HCV infection causes chronic liver diseases, and there is no effective vaccine available. This study proposes a novel approach called TROLLOPE to accurately identify TCE-HCVs from sequence information, with superior predictive performance.