Article
Multidisciplinary Sciences
Friederike Maite Siemers, Juergen Bajorath
Summary: This paper explores the application of random forest (RF) and support vector machine (SVM) in molecular machine learning (ML) and compound property prediction. By utilizing explainable artificial intelligence (XAI) methods, such as the Shapley value concept, the study reveals that RF and SVM models have different learning characteristics in their predictions and chemically intuitive explanations for accurate predictions originate from different sources.
SCIENTIFIC REPORTS
(2023)
Article
Ecology
Robin Singh Bhadoria, Manish Kumar Pandey, Pradeep Kundu
Summary: Human intervention causing forest fires hinders nature's ability to recover, leading to climate change consequences that we must take responsibility for and minimize. Mitigating fires by predicting and controlling their spread can be enhanced through machine learning models, like the proposed RVFR model, which achieves higher accuracy in predicting forest fires based on past data.
ECOLOGICAL INFORMATICS
(2021)
Article
Automation & Control Systems
Nanxiang Yang, Yongyan Pei, Yan Wang, Limin Zhao, Ping Zhao, Zhanchao Li
Summary: Oxidative stress can lead to various diseases, and achieving antioxidation is essential. However, the study of antioxidant mechanism is still in its early stage. Most current methods are time-consuming and laborious. Therefore, it is urgent to develop a theoretical method based on peptide sequence information for identifying antioxidant activity. This study utilized machine learning methods and sequence information of tripeptides to construct models for identifying their antioxidant activities.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2023)
Article
Neurosciences
Fengping Zhu, Zhiguang Pan, Ying Tang, Pengfei Fu, Sijie Cheng, Wenzhong Hou, Qi Zhang, Hong Huang, Yirui Sun
Summary: This study constructed efficient coagulopathy prediction models using data mining and machine learning algorithms, identifying important clinical parameters for acute coagulopathy occurrence. The random forest algorithm showed better performance compared to the support vector machine in predicting coagulopathy.
CNS NEUROSCIENCE & THERAPEUTICS
(2021)
Article
Agriculture, Dairy & Animal Science
Cem Tirink, Dariusz Piwczynski, Magdalena Kolenda, Hasan Onder
Summary: This study aimed to estimate body weight from various biometric measurements and features using data mining and machine learning algorithms. The results showed that the random forest algorithm can help improve important characteristics and breed an elite population in Poland.
Article
Computer Science, Information Systems
Gabriel J. Aguiar, Everton J. Santana, Andre C. P. F. L. de Carvalho, Sylvio Barbon Junior
Summary: The study examines the recommendation of method/base learner for multiple outputs and demonstrates the performance of meta-model through meta-learning experiments. The meta-models recommended high predictive performance solutions for multi-target regression tasks, including recommendations for real-world applications.
INFORMATION SCIENCES
(2022)
Article
Engineering, Aerospace
Muzaffer Can Iban, Erman Senturk
Summary: This research analyzes the prediction performance of three machine learning regression models for ionospheric parameters. The results show that random forest regression provides more accurate predictions than support vector machine and decision trees.
ADVANCES IN SPACE RESEARCH
(2022)
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
Engineering, Multidisciplinary
V Karthikeyan, S. Suja Priyadharsini
Summary: This paper proposes a new hybrid AdaBoost technique, combining Random Forest as the initial stage classifier and AdaBoost as subsequent stage classifiers to determine the class of the speaker sample. Experimental results show that the algorithm improves accuracy and stability in speaker recognition.
SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES
(2021)
Article
Psychiatry
Kerry L. Kinney, Yufeng Zheng, Matthew C. Morris, Julie A. Schumacher, Saurabh B. Bhardwaj, James K. Rowlett
Summary: The study develops algorithms for predicting benzodiazepine prescriptions using machine learning methods. The results show that support-vector machine and random forest algorithms can accurately classify individuals who receive a benzodiazepine prescription and can separate patients by the number of benzodiazepine prescriptions received. These predictive models could be used to reduce the public health burden of benzodiazepines.
FRONTIERS IN PSYCHIATRY
(2023)
Article
Education & Educational Research
Ricardo Costa-Mendes, Tiago Oliveira, Mauro Castelli, Frederico Cruz-Jesus
Summary: This article compares the predictive power between classic multilinear regression model and machine learning algorithms using a dataset from the Portuguese Ministry of Education. The results show that machine learning algorithms have higher predictive ability, with stacking appropriateness increasing. It is recommended to design an information system to support the nationwide education system and collect meaningful data related to academic achievement.
EDUCATION AND INFORMATION TECHNOLOGIES
(2021)
Article
Physics, Multidisciplinary
Athanasia Dimitriadou, Andros Gregoriou
Summary: In this paper, a machine-learning framework is used to predict Bitcoin movements. A dataset of 24 potential explanatory variables commonly used in finance literature is compiled. Forecasting models are built using past Bitcoin values, other cryptocurrencies, exchange rates, and other macroeconomic variables based on daily data from December 2, 2014 to July 8, 2019. Empirical results show that the traditional logistic regression model outperforms the linear support vector machine and random forest algorithm, achieving an accuracy of 66%. Furthermore, the results provide evidence rejecting weak form efficiency in the Bitcoin market.
Article
Environmental Sciences
Nitu Wu, Luis Guilherme Teixeira Crusiol, Guixiang Liu, Deji Wuyun, Guodong Han
Summary: Timely and accurate grassland classification is crucial for grassland resource management. However, there is a lack of comparative studies on commonly used methods for semi-arid grasslands in northern China. This study compared the performance of four machine learning algorithms for mapping semi-arid grassland using pixel-based and object-based classification methods. The findings showed that the object-based methods provided a more realistic land cover distribution and higher accuracy.
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
Agriculture, Dairy & Animal Science
Rafael N. Watanabe, Priscila A. Bernardes, Elieder P. Romanzini, Larissa G. Braga, Thais R. Brito, Ronyatta W. Teobaldo, Ricardo A. Reis, Danisio P. Munari
Summary: Monitoring animal activity in production systems is crucial for obtaining information on animal health, production, and reproduction. This study evaluated the use of accelerometers to predict grazing behavior of Nelore cattle and found that the Random Forest algorithm, combined with resampling techniques, achieved high accuracy in classifying various behaviors. Knowledge of animal behavior can provide insights into their well-being, health, and productivity, making accelerometers a valuable tool for continuous animal monitoring.
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)
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
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
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
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
Physics, Multidisciplinary
Aihua Zhang, Sun Choi
Summary: We have developed efficient techniques to solve the first-time problems of Brownian motion. Using a time-scale separation of recrossings, we have shown that Eyring's transmission coefficient (kappa) equals the one (kappa V) corresponding to an absorbing boundary consistent with the transition state theory, which is greater than the one (kappa K) derived by Kramers. We have also proposed methods for reaction rate determination by analyzing short-time trajectories from the barrier maximum, and discussed the relation to the reactive flux method and the significance of reaction coordinates.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(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)