Article
Genetics & Heredity
Dongxu Zhao, Zhixia Teng, Yanjuan Li, Dong Chen
Summary: This study proposed a random forest-based model called iAIPs for identifying anti-inflammatory peptides, which constructed an identification model through feature extraction and feature selection, and experimental results showed that the model performed well. The study of peptide identification is helpful in understanding species diversity and evolutionary history.
FRONTIERS IN GENETICS
(2021)
Article
Oncology
Jiajia Liu, Zhihui Zhou, Shanshan Kong, Zezhong Ma
Summary: This paper focuses on optimizing the properties of anti-breast cancer drugs through machine learning algorithms and finds the most suitable algorithm for predicting IC50 and pIC50 values. By adjusting the algorithm parameters, the accuracy and stability of the Random Forest algorithm are improved.
FRONTIERS IN ONCOLOGY
(2022)
Article
Chemistry, Multidisciplinary
Xiaotong Wang, Xiaoyu Chen, Zong-Jie Wang, Mengwei Zhuang, Lin Zhong, Chengzhang Fu, Ronald Garcia, Rolf Mueller, Youming Zhang, Jie Yan, Dalei Wu, Liujie Huo
Summary: In this study, a novel lanthipeptide myxococin was identified from Myxococcus fulvus. Myxococins are the first example of lanthipeptides where multiple thioether rings are installed by a Class II lanthipeptide synthetase MfuM and a Class I lanthipeptide cyclase MfuC in a cascaded way. The first M61 family aminopeptidase MfuP involved in RiPP biosynthesis was also discovered, showing the activity of an endopeptidase. Additionally, myxococins demonstrated anti-inflammatory activity in lipopolysaccharide-induced macrophages without detectable cytotoxicity.
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
(2023)
Review
Nutrition & Dietetics
Ana G. Abril, Manuel Pazos, Tomas G. Villa, Pilar Calo-Mata, Jorge Barros-Velazquez, Monica Carrera
Summary: Bioactive peptides have various health benefits and can be identified and quantified through proteomics. They have the potential to be a source of novel drugs and ingredients in food and pharmaceuticals.
Article
Geosciences, Multidisciplinary
Pin Zhang, Zhen-Yu Yin, Yin-Fu Jin, Tommy H. T. Chan, Fu-Ping Gao
Summary: This study proposes a novel modeling approach using machine learning techniques to predict the compression index C c in geotechnical design, showing that machine learning models outperform traditional empirical prediction formulations. Among the tested machine learning algorithms, random forest and back-propagation neural network models are recommended for predicting C c under different conditions.
GEOSCIENCE FRONTIERS
(2021)
Article
Computer Science, Artificial Intelligence
Youness Manzali, Yassine Akhiat, Mohamed Chahhou, Mohammed Elmohajir, Ahmed Zinedine
Summary: Random Forest is a popular supervised machine learning algorithm that combines decision trees to discover more rules and ensure diversity. This paper proposes a new sub-forest selection method using a noisy variable technique to eliminate underperforming trees and obtain a high-performance and small-sized sub-forest.
Article
Biochemical Research Methods
Chunyan Ao, Quan Zou, Liang Yu
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.
Article
Chemistry, Physical
Ying Huang, Fachao Jiang, Haiming Xie
Summary: This study focuses on the energy optimization problem of concrete truck mixers and proposes a novel hybrid powertrain and an adaptive hierarchical energy management strategy to address the issue. Through two key efforts, the research aims to solve the energy optimization problem in concrete truck mixers.
JOURNAL OF POWER SOURCES
(2021)
Article
Geosciences, Multidisciplinary
Xinzhi Zhou, Haijia Wen, Yalan Zhang, Jiahui Xu, Wengang Zhang
Summary: This study aimed to develop two hybrid models to optimize factors and enhance predictive ability of landslide susceptibility models. By utilizing factor optimization methods, the hybrid models showed higher reliability and predictability compared to traditional models, confirming the effectiveness of factor optimization in improving model performance.
GEOSCIENCE FRONTIERS
(2021)
Article
Computer Science, Information Systems
Myat Cho Mon Oo, Thandar Thein
Summary: In this paper, an efficient predictive analytics system for high dimensional big data is proposed by enhancing scalable random forest algorithm on the Apache Spark platform.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Review
Biochemistry & Molecular Biology
Julia Rivera-Jimenez, Carmen Berraquero-Garcia, Raul Perez-Galvez, Pedro J. Garcia-Moreno, F. Javier Espejo-Carpio, Antonio Guadix, Emilia M. Guadix
Summary: Inflammation is the immune system's response to harmful stimuli, with the aim of eliminating irritants and promoting tissue repair. Current anti-inflammatory drugs have limitations and side effects, so researchers are looking for alternative and more selective therapies from natural products. Small peptides with low molecular weight and short amino acid chains are found to be highly active, and the presence of hydrophobic and positively charged amino acids is common. Interestingly, a large percentage of anti-inflammatory peptides can be found in sustainable protein sources. However, not all peptides with in vitro anti-inflammatory potential achieve good scores in in silico bioactivity predictors, highlighting the need for bioinformatics tools to complement in vitro experiments with prediction of potential bioactive peptides.
Article
Forestry
Chen Chang, Zhongke Feng, Ziye Liu
Summary: With the optimization of random forest parameters, this study explores the impact of environmental factors on tree density. The results indicate that average temperature, soil thickness, and forest water consumption are the main factors limiting tree density, and the influence of each factor varies depending on the stage of tree growth. Based on forest resource data, tree density distribution grid maps were generated using models and interpolation methods, providing theoretical and data support for the development of appropriate forest management strategies.
Article
Computer Science, Artificial Intelligence
Munish Kumar, M. K. Jindal, R. K. Sharma, Simpel Rani Jindal, Harjeet Singh
Summary: This study enhances the recognition results of offline handwritten Gurumukhi characters by applying hybrid features and an adaptive boosting approach, achieving a recognition accuracy of 96.3%.
Article
Biochemistry & Molecular Biology
Hellen Daghero, Julio Raul Fernandez Masso, Soledad Astrada, Maribel Guerra Vallespi, Mariela Bollati-Fogolin
Summary: The study revealed the important role of COMMD1 in the mechanism of action of CIGB-552 by knocking out COMMD1 and analyzing the cell lines with microarray, validating signaling pathways to confirm the results. The findings showed that CIGB-552 inhibits the expression of related genes in lung cancer cell lines by modulating NF-kB and HIF-1 signaling pathways.
Article
Chemistry, Multidisciplinary
Ayanabha Jana, Shridevi S. Krishnakumar
Summary: The proposed research introduces a sign gesture recognition system for improved interaction between sign and non-sign users. Multiple types of features are utilized and trained on ensemble classifiers to accurately predict the label of input sign images. Additionally, a hybrid artificial neural network is designed to precisely locate and classify sign gestures. Experimental results demonstrate that the system is capable of handling various image types and achieves comparable accuracy to state-of-the-art literature.
APPLIED SCIENCES-BASEL
(2022)
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
Biochemistry & Molecular Biology
Adeel Malik, Watshara Shoombuatong, Chang-Bae Kim, Balachandran Manavalan
Summary: A machine learning-based predictor called GPApred was developed to identify LPXTG-like proteins from their primary sequences. This predictor can be utilized for functional characterization and drug targeting in further research.
INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES
(2023)
Review
Biochemistry & Molecular Biology
Arifa Un-Nisa, Amjad Khan, Muhammad Zakria, Sami Siraj, Shakir Ullah, Muhammad Khalid Tipu, Muhammad Ikram, Myeong Ok Kim
Summary: This review article discusses the beneficial effects of Lactobacillus against various diseases, with a special emphasis on its effects on neurological disorders such as depression, multiple sclerosis, Alzheimer's, and Parkinson's disease. Probiotics are live microbes found in fermented foods and beverages, which, when administered in adequate doses, provide health benefits to the host. Imbalance in gut microbiota can lead to the development of several diseases affecting the gastrointestinal tract, skin, immune system, inflammation, and gut-brain axis. Recent research has shown promising effects of probiotics in alleviating symptoms in different disease models, highlighting their potential for prevention and treatment. The review also covers current probiotics-based products, disease models, measured markers, and evidence from past studies.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Multidisciplinary Sciences
Nattanong Bupi, Vinoth Kumar Sangaraju, Le Thi Phan, Aamir Lal, Thuy Thi Bich Vo, Phuong Thi Ho, Muhammad Amir Qureshi, Marjia Tabassum, Sukchan Lee, Balachandran Manavalan
Summary: Tomato yellow leaf curl virus (TYLCV) has spread to different countries, particularly in subtropical regions, and is associated with more severe symptoms. This study developed an integrated computational framework to accurately identify symptoms (mild or severe) based on TYLCV sequences isolated in Korea. Blind predictions revealed that 2 groups had severe symptoms and 1 group had mild symptoms.
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
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.
Review
Biochemical Research Methods
Le Thi Phan, Changmin Oh, Tao He, Balachandran Manavalan
Summary: Enhancers are non-coding DNA elements that enhance the transcription rate of specific genes. Computational platforms have been developed to complement experimental methods in identifying enhancers. This review provides an overview of machine learning-based prediction methods and databases for enhancer identification and discusses the advantages and drawbacks of these methods, as well as guidelines for developing more efficient enhancer predictors.
Editorial Material
Medicine, Research & Experimental
Shaherin Basith, Balachandran Manavalan
MOLECULAR THERAPY-NUCLEIC ACIDS
(2023)
Article
Computer Science, Artificial Intelligence
K. Janani, S. S. Mohanrasu, Chee Peng Lim, Balachandran Manavalan, R. Rakkiyappan
Summary: Feature selection is necessary due to the rapid increase in digital technology, which allows for the generation of large quantities of high-dimensional data in a short amount of time. Ensemble feature selection has emerged as a potential approach to data mining, with the advantage of identifying multiple optimal features.
APPLIED SOFT COMPUTING
(2023)
Article
Toxicology
Tae Hwan Shin, Gwang Lee
Summary: Nanoparticles have been widely used in neurological research, but their potential toxicity remains a concern. This study investigated the effects of silica-coated magnetic nanoparticles on BV2 microglial cells and found that the nanoparticles induced amyloid beta accumulation and changes in lysosomal function. By employing triple-omics analysis, it was revealed that the nanoparticles caused a reduction in proteasome activity and lysosomal swelling. However, co-treatment with glutathione and citrate alleviated these effects.
ARCHIVES OF TOXICOLOGY
(2023)
Article
Biology
Jiayue Hu, Wang Yu, Chao Pang, Junru Jin, Nhat Truong Pham, Balachandran Manavalan, Leyi Wei
Summary: DrugormerDTI is a novel neural network architecture that predicts drug-target interactions by learning the relationship between molecule graphs and protein residues.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biochemistry & Molecular Biology
Jun Sung Park, Kyonghwan Choe, Amjad Khan, Myeung Hoon Jo, Hyun Young Park, Min Hwa Kang, Tae Ju Park, Myeong Ok Kim
Summary: The aim of this study was to establish a functional in vitro co-cultured BBB model to investigate BBB-related physiological conditions. A co-cultured model consisting of brain-derived endothelial and astrocyte cells was successfully established on transwell membranes. The co-cultured model showed effective barrier properties and enhanced expression of tight junction proteins. Under disease conditions, the co-cultured model mimicked BBB damages. This in vitro BBB model can be a useful tool for studying BBB-related pathological and physiological processes.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Oncology
Hye Jin Yun, Min Li, Dong Guo, So Mi Jeon, Su Hwan Park, Je Sun Lim, Su Bin Lee, Rui Liu, Linyong Du, Seok-Ho Kim, Tae Hwan Shin, Seong-il Eyun, Yun-Yong Park, Zhimin Lu, Jong-Ho Lee
Summary: This study reveals that enhanced glucose-derived de novo serine biosynthesis is a critical metabolic feature of GBM cells under metabolic stress, and highlights the potential to target SSP for treating human GBM.
JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH
(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)