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Guest Editorial: Advanced Machine-Learning Methods for Brain-Machine Interfacing or Brain-Computer Interfacing

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 1643-1644页

作者： Xia, Kaijian Jiang, Yizhang Zhang, Yudong Si, Wen China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China Soochow Univ Changshu peoples Hosp 1 Affiliated Changshu Hosp Changshu 215500 Jiangsu Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Univ Leicester Dept Informat Leicester LE1 7RH Leics England Univ S Florida Coll Engn Tampa FL 33620 USA

The seven papers in this special section focus on advanced machine learning methods for brain machine interfacing. Particular emphasis is on novel theories and methods using transfer learning and deep learning proposed for Brain-Machine Interfacing (BMI) or Brain-Computer Interfacing (BCI). Our purpose is to review the new progress and achievements on transfer learning, deep learning, and their applications in BMI or BCI in recent years.

关键词： Special issues and sections Machine learning Brain-computer interfaces Transfer learning Deep learning computational intelligence

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Artificial Neural Networks for the Rapid Prediction of Possible Ferroptosis Inducers Using the GPx4 Enzyme

Artificial Neural Networks for the Rapid Prediction of Possi...

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ieee Symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Jesus Antonio Alvarado-Huayhuaz Miquéias Amorim Santos Silva Elvis Mauricio Jimenez Peña Joel Claudio Rengifo Maraví Angella M. Cordova-Muñoz Karina dos Santos Machado Facultad de Ciencias Universidad Nacional de Ingeniería Lima Peru Centro de Ciências Computacionais Universidade Federal do Rio Grande Rio Grande Brazil Departamento de Fisicoquímica Universidad Nacional Mayor de San Marcos Lima Peru Departamento de Química Universidad Nacional Agraria La Molina Lima Peru Departamento de Farmacología Universidad Nacional de Trujillo Trujillo Peru

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Cancer affects 20 million people worldwide; therefore, accelerated and urgent pathways are needed for the search for new drugs. The reactivation of cell death induced by the accumulation of iron-dependent lipid peroxides (Ferroptosis) is a potential mechanism for cancer treatment. One pathway to trigger Ferroptosis involves the inactivation of the glutathione-dependent peroxidase (GPx4) protein. Molecules with high affinity for iron are also being studied against cancer through chelation therapy. Therefore, the rapid detection of such molecules in molecular databases is of interest. Catechols exhibit a high iron-chelating power and can be synthesized by microorganisms as a chemical strategy for acquiring Fe 3+ . They can also be found in natural products. Recently, the natural product database in Peru (PeruNPDB) has been published where we found catechols; however, these have not yet been studied in GPx4. To expedite the search for catechols with affinity for the active site of GPx4, in large databases, we have developed a prediction model for scoring based on artificial neural networks. Our initial database consisted of the 83 catechols and 1,024 molecular descriptors. The target is the docking score obtained by ligand-receptor GPx4 coupling with the Autodock Vina software. Emphasizing the application of recursive feature elimination to improve the model’s performance and provides both the model and the procedure to be applied and improved by the scientific community. Our program enables the experimenter to rapidly filter molecules with high affinity specifically for the active site of GPx4, in the search for potential inhibitors.

关键词： Training Databases computational modeling Cancer treatment Artificial neural networks Predictive models Software Compounds Cancer Software development management

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Efficient Methods to Generate NIPT Male Samples with Aberrations on Sex Chromosomes for Benchmarking NIPT Algorithms

Efficient Methods to Generate NIPT Male Samples with Aberrat...

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ieee International conference on bioinformatics and computational biology (ICBCB)

作者： Le Sy Vinh Next Generation Genome Technology and Bioinformatics University of Engineering and Technology Vietnam National University Hanoi Hanoi Vietnam

ISBN: (数字)9798350375749

ISBN: (纸本)9798350375756

Noninvasive prenatal test (NIPT) has become a routine screening method to detect autosomal chromosomal aberrations. Predicting sex chromosome aneuploidies (SCAs) is more difficult due to the complicated genomic characteristics of sex chromosomes. Benchmarking the sensitivity of NIPT algorithms is challenging due to the lack of positive samples. In this paper, we introduce simple and efficient computational methods to create aberrations on sex chromosomes for NIPT male samples (i.e., creating male samples with XXY or XYY aberrations) from negative NIPT samples. We applied the methods to create 513 positive samples with XXY aberrations and 200 positive samples with XYY aberrations to evaluate the sensitivity of three NIPT algorithms WisecondorX, its improvement VINIPT, and CNVkit. Experiements on both negative and positive samples showed that the algorithms have high sensitivity in detecting XXY and XYY aberrations (100% for WisecondorX and VINIPT; and 99.2% for CNVkit). The VINIPT algorithm has an overall specificity of 99.8% and better than WisecondorX (∼96.8%) and CNVkit (∼99%). The results indicate that VINIPT can play as a powerful tool for screening aberrations on sex chromosomes of male samples.

关键词： Sensitivity Software algorithms Genomics Benchmark testing Sensitivity and specificity Prediction algorithms Software computational efficiency bioinformatics Biological cells

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Transparent AI Models for Meningococcal Meningitis Diagnosis: Evaluating Interpretability and Performance Metrics 12

Transparent AI Models for Meningococcal Meningitis Diagnosis...

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12th ieee International conference on Intelligent Systems, IS 2024

作者： Messai, Aya Drif, Ahlem Ouyahia, Amel Guechi, Meriem Rais, Mounira Kaderali, Lars Cherifi, Hocine University of Ferhat Abbas Sétif 1 Lrsd Laboratory Sétif19000 Algeria University of Ferhat Abbas Sétif 1 Department of computer science Sétif19000 Algeria University of Ferhat Abbas Sétif 1 STDs Hiv Aids unit Department of Medicine Sétif19000 Algeria Chu Saadna Abdenour Hospital Sétif Hospital Sétif19000 Algeria University of Ferhat Abbas Sétif 1 Department of Medicine Sétif19000 Algeria Institute of Bioinformatics University Medicine Greifswald Felix-Hausdorf-Str. 8 Greifswald17475 Germany University of Burgundy Dijon Laboratoire Interdisciplinaire Carnot de Bourgogne Icb Umr 6303 Cnrs France

ISBN: (纸本)9798350350982

Meningococcal meningitis, a severe bacterial infection, demands precise diagnosis and immediate intervention. This paper explores the potential of Artificial intelligence (AI) in improving diagnostic accuracy and clinical decision-making for this condition. We assess interpretability and reliability by analyzing the decision-making process of Machine Learning (ML) models tailored to identify Meningococcal meningitis among different etiologies of meningitis. Initially, we train and test various ML models, including logistic regression, K-nearest neighbors, support vector machine, decision tree, gradient boosting, AdaBoost, random forest, and LightGBM classifier, on a dataset comprising 460 cases of Meningococcal meningitis and 474 cases of other types of meningitis. Among these models, the gradient-boosting model exhibits superior performance metrics, including accuracy (0.88), precision (0.92), recall (0.83), AUROC (0.93), and f1-score (0.87). Subsequently, we employ Explainable AI (XAI) tools (ELI5 and LIME) to elucidate the importance of features in the ML models. Our results highlight key factors contributing to model success, such as Neisseria meningitidis identified through cerebrospinal fluid (CSF) culture and latex agglutination, gram-negative diplococci in CSF smear examination, white cell counts in CSF, and patient age. Local explanations reveal the presence of neutrophils in CSF as a characteristic feature of Meningococcal meningitis. LIME analysis indicates the significance of low lymphocyte percentages and elevated white blood cell counts in predicting this condition. These findings underscore the effectiveness of integrating global and local interpretability techniques, aligning with expert knowledge, and emphasizing the importance of transparent AI models in clinical decision-making processes. © 2024 ieee.

关键词： Cerebrospinal fluid

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An Efficient computational Method to Create Positive NIPT Samples with Autosomal Trisomy

An Efficient Computational Method to Create Positive NIPT Sa...

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ieee International conference on bioinformatics and computational biology (ICBCB)

作者： Tinh H. Nguyen Huynh V. Nguyen Minh D. Pham Vinh S. Le VNU University of Engineering and Technology Hanoi Vietnam GENTIS Hanoi Vietnam Next Generation Genome Technology and Bioinformatics VNU University of Engineering and Technology Hanoi Vietnam

ISBN: (数字)9798350375749

ISBN: (纸本)9798350375756

Noninvasive prenatal test (NIPT) has been widely used for screening trisomy on chromosomes 13 (T13), 18 (T18), and 21 (T21). However, the false negative rate of NIPT algorithms has not been thoroughly evaluated due to the lack of positive samples. In this study, we present an efficient computational approach to create positive samples with autosomal trisomy from negative samples. We applied the approach to establish a low coverage dataset of 1440 positive samples with T13, T18, and T21 aberrations for both mosaic and non-mosaic conditions. We examined the performance of WisecondorX and its improvement, called VINIPT, on both negative and positive datasets. Experiments showed that WisecondorX and VINIPT were able to detect all non-mosaic samples with T13, T18, and T21 aberrations (i.e., the sensitivity of 100%). When analyzing mosaic samples, both WisecondorX and VINIPT have the overall sensitivity of 99.7% on detecting T13, T18, and T21 aberrations from mosaic samples. WisecondorX has the specificity of 98.5% for the nonmosaic analysis, but a considerably lower specificity of 95% for the mosaic analysis. VINIPT has a much better specificity than WisecondorX, i.e., 99.9% for the non-mosaic analysis, and 98.2% for the mosaic analysis. The results suggest that VINIPT can play as a powerful tool for detecting autosomal trisomy from the low coverage data.

关键词： Sensitivity Software algorithms Sensitivity and specificity Software computational efficiency Software reliability bioinformatics Biological cells computational biology

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A dual-channel deep learning approach to continuous prediction of acute kidney injury in the intensive care unit

A dual-channel deep learning approach to continuous predicti...

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2023 ieee International conference on bioinformatics and Biomedicine, BIBM 2023

作者： Kong, Xuetong Zhang, Peng Ling, Yunchao Lv, Daqing Xu, Jun Zhang, Guoqing University of Chinese Academy of Sciences Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study Hangzhou China Shanghai Institute of Nutrition and Health University of Chinese Academy of Sciences Chinese Academy of Sciences Cas Key Laboratory of Computational Biology Bio-Med Big Data Center Shanghai China Shanghai Southgene Technology Co. Ltd Shanghai China Shanghai Sixth People's Hospital Shanghai China Shanghai Institute of Nutrition and Health University of Chinese Academy of Sciences Chinese Academy of Sciences Shanghai Sixth People's Hospital Cas Key Laboratory of Computational Biology Bio-Med Big Data Center Shanghai China

ISBN: (纸本)9798350337488

Acute Kidney Injury (AKI) often occurs in the intensive care units (ICU), where it is associated with high morbidity and mortality. Early and continuous prediction of AKI plays a crucial role in preventing AKI events, providing timely treatment, and reducing mortality. Continuously monitoring the high dimensional vital signs and lab measurements of the patients has always been a challenging task. In this study, we proposed a novel dual-channel deep learning approach named DC-AKI to address this challenge. DC-AKI extracts feature of temporal variables at different granularity, comprehensively and accurately obtains effective information, and finally continuous predicting risks of the patients. One channel of the model calculates corresponding local interactions of the variables with convolution kernels. The other channel uses the gated recurrent units (GRU) network and attention mechanism to obtains global interactions information among the variables. Finally, the model uses full connection layer to combine the representation vectors of the two channels, and uses Sigmoid classifier for prediction. We trained and tested our model on the MIMIC IV dataset, and the AUC ROC value of DC-AKI for predicting the risk of AKI in ICU patients 48 hours in advance was 0.9527, outperforming other methods. And our model was externally validated on the eICU dataset with an AUC ROC of 0.9054. These results demonstrate the robustness and accuracy of the model on different datasets. This finding provides clinicians with an opportunity to identify and manage patients with AKI in a timely manner, thereby improving patient prognosis. The implemented code is available online at https://***/BioMedBigDataCenter/DC-AKI. © 2023 ieee.

关键词： acute kidney injury AKI deep learning dual-channel ICU intensive care unit

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20th International conference on computational Methods in Systems biology, CMSB 2022

20th International Conference on Computational Methods in Sy...

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20th International conference on computational Methods in Systems biology, CMSB 2022

ISBN: (纸本)9783031150333

The proceedings contain 17 papers. The special focus in this conference is on computational Methods in Systems biology. The topics include: Limit Cycle Analysis of a Class of Hybrid Gene Regulatory Networks;bayesian Learning of Effective Chemical Master Equations in Crowded Intracellular Conditions;probabilistic Multivariate Early Warning Signals;mobsPy: A Meta-species Language for Chemical Reaction Networks;automated Generation of Conditional Moment Equations for Stochastic Reaction Networks;An Extension of ERODE to Reduce Boolean Networks By Backward Boolean Equivalence;eBCSgen 2.0: Modelling and Analysis of Regulated Rule-Based Systems;abstract Simulation of Reaction Networks via Boolean Networks;abstraction-Based Segmental Simulation of Chemical Reaction Networks;qualitative Dynamics of Chemical Reaction Networks: An Investigation Using Partial Tropical Equilibrations;prioritization of Candidate Genes Through Boolean Networks;variable Stabilisation in Boolean Monotonic Model Pools;variable-Depth Simulation of Most Permissive Boolean Networks;minimal Trap Spaces of Logical Models are Maximal Siphons of Their Petri Net Encoding;stability Versus Meta-stability in a Skin Microbiome Model.

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Bridging the gaps in statistical models of protein alignment

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bioinformatics 2022年第SUPPL 1期38卷 229-237页

作者： Sumanaweera, Dinithi Allison, Lloyd Konagurthu, Arun S. Monash Univ Fac Informat Technol Dept Data Sci & Artificial Intelligence Clayton Vic 3800 Australia

Sequences of proteins evolve by accumulating substitutions together with insertions and deletions (indels) of amino acids. However, it remains a common practice to disconnect substitutions and indels, and infer approximate models for each of them separately, to quantify sequence relationships. Although this approach brings with it computational convenience (which remains its primary motivation), there is a dearth of attempts to unify and model them systematically and together. To overcome this gap, this article demonstrates how a complete statistical model quantifying the evolution of pairs of aligned proteins can be constructed using a time-parameterized substitution matrix and a time-parameterized alignment state machine. Methods to derive all parameters of such a model from any benchmark collection of aligned protein sequences are described here. This has not only allowed us to generate a unified statistical model for each of the nine widely used substitution matrices (PAM, JTT, BLOSUM, JO, WAG, VTML, LG, MIQS and PFASUM), but also resulted in a new unified model, MMLSUM. Our underlying methodology measures the Shannon information content using each model to explain losslessly any given collection of alignments, which has allowed us to quantify the performance of all the above models on six comprehensive alignment benchmarks. Our results show that MMLSUM results in a new and clear overall best performance, followed by PFASUM, VTML, BLOSUM and MIQS, respectively, amongst the top five. We further analyze the statistical properties of MMLSUM model and contrast it with others. Supplementary information are available at bioinformatics online.

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iEnhancer-KL: A Novel Two-Layer Predictor for Identifying Enhancers by Position Specific of Nucleotide Composition

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第6期18卷 2809-2815页

作者： Lyu, Yinuo Zhang, Zhen Li, Jiawei He, Wenying Ding, Yijie Guo, Fei Tianjin Univ Coll Intelligence & Comp Sch Comp Sci & Technol Tianjin 300350 Peoples R China Tsinghua Univ Dept Chem Engn Beijing 100084 Peoples R China Suzhou Univ Sci & Technol Sch Elect & Informat Engn Suzhou 215009 Jiangsu Peoples R China

An enhancer is a short region of DNA with the ability to recruit transcription factors and their complexes, increasing the likelihood of the transcription of a particular gene. Considering the importance of enhancers, enhancer identification is a prevailing problem in computational biology. In this paper, we propose a novel two-layer enhancer predictor called iEnhancer-KL, using computational biology algorithms to identify enhancers and then classify these enhancers into strong or weak types. Kullback-Leibler (KL) divergence is creatively taken into consideration to improve the feature extraction method PSTNPss. Then, LASSO is used to reduce the dimension of features and finally helps to get better prediction performance. Furthermore, the selected features are tested on several machine learning models, and the SVM algorithm achieves the best performance. The rigorous cross-validation indicates that our predictor is remarkably superior to the existing state-of-the-art methods with an Acc of 84.23 percent and the MCC of 0.6849 for identifying enhancers. Our code and results can be freely downloaded from https://***/Not-so-middle/***.

关键词： Feature extraction Support vector machines Prediction algorithms DNA Machine learning Benchmark testing Probability distribution Enhancer KL divergence PSTNPss LASSO SVM

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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2023 ieee International conference on bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 ieee.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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