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Computational tools for exploring peptide-membrane interactions in gram-positive bacteria. Comput Struct Biotechnol J 2023; 21:1995-2008. [PMID: 36950221 PMCID: PMC10025024 DOI: 10.1016/j.csbj.2023.02.051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 02/27/2023] [Accepted: 02/27/2023] [Indexed: 03/05/2023] Open
Abstract
The vital cellular functions in Gram-positive bacteria are controlled by signaling molecules known as quorum sensing peptides (QSPs), considered promising therapeutic interventions for bacterial infections. In the bacterial system QSPs bind to membrane-coupled receptors, which then auto-phosphorylate and activate intracellular response regulators. These response regulators induce target gene expression in bacteria. One of the most reliable trends in drug discovery research for virulence-associated molecular targets is the use of peptide drugs or new functionalities. In this perspective, computational methods act as auxiliary aids for biologists, where methodologies based on machine learning and in silico analysis are developed as suitable tools for target peptide identification. Therefore, the development of quick and reliable computational resources to identify or predict these QSPs along with their receptors and inhibitors is receiving considerable attention. The databases such as Quorumpeps and Quorum Sensing of Human Gut Microbes (QSHGM) provide a detailed overview of the structures and functions of QSPs. The tools and algorithms such as QSPpred, QSPred-FL, iQSP, EnsembleQS and PEPred-Suite have been used for the generic prediction of QSPs and feature representation. The availability of compiled key resources for utilizing peptide features based on amino acid composition, positional preferences, and motifs as well as structural and physicochemical properties, including biofilm inhibitory peptides, can aid in elucidating the QSP and membrane receptor interactions in infectious Gram-positive pathogens. Herein, we present a comprehensive survey of diverse computational approaches that are suitable for detecting QSPs and QS interference molecules. This review highlights the utility of these methods for developing potential biomarkers against infectious Gram-positive pathogens.
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Key Words
- 3-HBA, 3–Hydroxybenzoic Acid
- AAC, Amino Acid Composition
- ABC, ATP-binding cassette
- ACD, Available Chemicals Database
- AIP, Autoinducing Peptide
- AMP, Anti-Microbial Peptide
- ATP, Adenosine Triphosphate
- Agr, Accessory gene regulator
- BFE, Binding Free Energy
- BIP Inhibitors
- BIP, Biofilm Inhibitory Peptides
- BLAST, Basic Local Alignment Search Tool
- BNB, Bernoulli Naïve-Bayes
- CADD, Computer-Aided Drug Design
- CSP, Competence Stimulating Peptide
- CTD, Composition-Transition-Distribution
- D, Aspartate
- DCH, 3,3′-(3,4-dichlorobenzylidene)-bis-(4-hydroxycoumarin)
- DT, Decision Tree
- FDA, Food and Drug Administration
- GBM, Gradient Boosting Machine
- GDC, g-gap Dipeptide
- GNB, Gaussian NB
- Gram-positive bacteria
- H, Histidine
- H-Kinase, Histidine Kinase
- H-phosphotransferase, Histidine Phosphotransferase
- HAM, Hamamelitannin
- HGM, Human Gut Microbiota
- HNP, Human Neutrophil Peptide
- IT, Information Theory Features
- In silico approaches
- KNN, K-Nearest Neighbors
- MCC, Mathew Co-relation Coefficient
- MD, Molecular Dynamics
- MDR, Multiple Drug Resistance
- ML, Machine Learning
- MRSA, Methicillin Resistant S. aureus
- MSL, Multiple Sequence Alignment
- OMR, Omargliptin
- OVP, Overlapping Property Features
- PCP, Physicochemical Properties
- PDB, Protein Data Bank
- PPIs, Protein-Protein Interactions
- PSM, Phenol-Soluble Modulin
- PTM, Post Translational Modification
- QS, Quorum Sensing
- QSCN, QS communication network
- QSHGM, Quorum Sensing of Human Gut Microbes
- QSI, QS Inhibitors
- QSIM, QS Interference Molecules
- QSP inhibitors
- QSP predictors
- QSP, QS Peptides
- QSPR, Quantitative Structure Property Relationship
- Quorum sensing peptides
- RAP, RNAIII-activating protein
- RF, Random Forest
- RIP, RNAIII-inhibiting peptide
- ROC, Receiver Operating Characteristic
- SAR, Structure-Activity Relationship
- SFS, Sequential Forward Search
- SIT, Sitagliptin
- SVM, Support Vector Machine
- TCS, Two-Component Sensory
- TRAP, Target of RAP
- TRG, Trelagliptin
- WHO, World Health Organization
- mRMR, minimum Redundancy and Maximum Relevance
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Artificial intelligence to predict outcomes of head and neck radiotherapy. Clin Transl Radiat Oncol 2023; 39:100590. [PMID: 36935854 PMCID: PMC10014342 DOI: 10.1016/j.ctro.2023.100590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 01/28/2023] [Accepted: 01/28/2023] [Indexed: 02/01/2023] Open
Abstract
Head and neck radiotherapy induces important toxicity, and its efficacy and tolerance vary widely across patients. Advancements in radiotherapy delivery techniques, along with the increased quality and frequency of image guidance, offer a unique opportunity to individualize radiotherapy based on imaging biomarkers, with the aim of improving radiation efficacy while reducing its toxicity. Various artificial intelligence models integrating clinical data and radiomics have shown encouraging results for toxicity and cancer control outcomes prediction in head and neck cancer radiotherapy. Clinical implementation of these models could lead to individualized risk-based therapeutic decision making, but the reliability of the current studies is limited. Understanding, validating and expanding these models to larger multi-institutional data sets and testing them in the context of clinical trials is needed to ensure safe clinical implementation. This review summarizes the current state of the art of machine learning models for prediction of head and neck cancer radiotherapy outcomes.
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Key Words
- ADASYN, adaptive synthetic sampling
- AI, artificial intelligence
- ANN, artificial neural network
- AUC, Area Under the ROC Curve
- Artificial intelligence
- BMI, body mass index
- C-Index, concordance index
- CART, Classification and Regression Tree
- CBCT, cone-beam computed tomography
- CIFE, conditional informax feature extraction
- CNN, convolutional neural network
- CRT, chemoradiation
- CT, computed tomography
- Cancer outcomes
- DL, deep learning
- DM, distant metastasis
- DSC, Dice Similarity Coefficient
- DSS, clinical decision support systems
- DT, Decision Tree
- DVH, Dose-volume histogram
- GANs, Generative Adversarial Networks
- GB, Gradient boosting
- GPU, graphical process units
- HNC, head and neck cancer
- HPV, human papillomavirus
- HR, hazard ratio
- Head and neck cancer
- IAMB, incremental association Markov blanket
- IBDM, image based data mining
- IBMs, image biomarkers
- IMRT, intensity-modulated RT
- KNN, k nearest neighbor
- LLR, Local linear forest
- LR, logistic regression
- LRR, loco-regional recurrence
- MIFS, mutual information based feature selection
- ML, machine learning
- MRI, Magnetic resonance imaging
- MRMR, Minimum redundancy feature selection
- Machine learning
- N-MLTR, Neural Multi-Task Logistic Regression
- NPC, nasopharynx
- NTCP, Normal Tissue Complication Probability
- OPC, oropharyngeal cancer
- ORN, osteoradionecrosis
- OS, overall survival
- PCA, Principal component analysis
- PET, Positron emission tomography
- PG, parotid glands
- PLR, Positive likelihood ratio
- PM, pharyngeal mucosa
- PTV, Planning target volumes
- PreSANet, deep preprocessor module and self-attention
- Predictive modeling
- QUANTEC, Quantitative Analyses of Normal Tissue Effects in the Clinic
- RF, random forest
- RFC, random forest classifier
- RFS, recurrence free survival
- RLR, Rigid logistic regression
- RRF, Regularized random forest
- RSF, random survival forest
- RT, radiotherapy
- RTLI, radiation-induced temporal lobe injury
- Radiomic
- SDM, shared decision making
- SMG, submandibular glands
- SMOTE, synthetic minority over-sampling technique
- STIC, sticky saliva
- SVC, support vector classifier
- SVM, support vector machine
- XGBoost, extreme gradient boosting
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Machine learning for normal tissue complication probability prediction: Predictive power with versatility and easy implementation. Clin Transl Radiat Oncol 2023; 39:100595. [PMID: 36880063 PMCID: PMC9984444 DOI: 10.1016/j.ctro.2023.100595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 02/05/2023] [Indexed: 02/11/2023] Open
Abstract
Background and purpose A popular Normal tissue Complication (NTCP) model deployed to predict radiotherapy (RT) toxicity is the Lyman-Burman Kutcher (LKB) model of tissue complication. Despite the LKB model's popularity, it can suffer from numerical instability and considers only the generalized mean dose (GMD) to an organ. Machine learning (ML) algorithms can potentially offer superior predictive power of the LKB model, and with fewer drawbacks. Here we examine the numerical characteristics and predictive power of the LKB model and compare these with those of ML. Materials and methods Both an LKB model and ML models were used to predict G2 Xerostomia on patients following RT for head and neck cancer, using the dose volume histogram of parotid glands as the input feature. Model speed, convergence characteristics and predictive power was evaluated on an independent training set. Results We found that only global optimization algorithms could guarantee a convergent and predictive LKB model. At the same time our results showed that ML models remained unconditionally convergent and predictive, while staying robust to gradient descent optimization. ML models outperform LKB in Brier score and accuracy but compare to LKB in ROC-AUC. Conclusion We have demonstrated that ML models can quantify NTCP better than or as well as LKB models, even for a toxicity that the LKB model is particularly well suited to predict. ML models can offer this performance while offering fundamental advantages in model convergence, speed, and flexibility, and so could offer an alternative to the LKB model that could potentially be used in clinical RT planning decisions.
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Key Words
- AB, AdaBooost (aka Adaptive Boosting)
- Clinical radiobiology
- DA, Dual Annealing
- DE, Differential Evolution
- DT, Decision Tree
- DVH, Dose Volume Histogram
- GB, Gradient Boost
- GD, Gradient Descent
- GMD, Generalized Mean Dose
- Head and Neck Cancer
- LKB, Lyman Kutcher Burman
- LR, Logistic Regression
- ML, Machine Learning
- Machine Learning
- NTCP, Normal Tissue Complication Probability
- Normal Tissue Complication Probability
- OAR, Organ(s) at Risk
- RT, Radiotherapy
- Radiotherapy
- Treatment Planning
- Xerostomia
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Machine learning and deep learning methods that use omics data for metastasis prediction. Comput Struct Biotechnol J 2021; 19:5008-5018. [PMID: 34589181 PMCID: PMC8450182 DOI: 10.1016/j.csbj.2021.09.001] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 08/16/2021] [Accepted: 09/02/2021] [Indexed: 12/14/2022] Open
Abstract
Knowing metastasis is the primary cause of cancer-related deaths, incentivized research directed towards unraveling the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications, and is now being used to predict the onset of metastasis to improve diagnostics and disease therapies. In this regard, predicting metastasis onset has also been explored using artificial intelligence approaches that are machine learning, and more recently, deep learning-based. This review summarizes the different machine learning and deep learning-based metastasis prediction methods developed to date. We also detail the different types of molecular data used to build the models and the critical signatures derived from the different methods. We further highlight the challenges associated with using machine learning and deep learning methods, and provide suggestions to improve the predictive performance of such methods.
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Key Words
- AE, autoencoder
- ANN, Artificial Neural Network
- AUC, area under the curve
- Acc, Accuracy
- Artificial intelligence
- BC, Betweenness centrality
- BH, Benjamini-Hochberg
- BioGRID, Biological General Repository for Interaction Datasets
- CCP, compound covariate predictor
- CEA, Carcinoembryonic antigen
- CNN, convolution neural networks
- CV, cross-validation
- Cancer
- DBN, deep belief network
- DDBN, discriminative deep belief network
- DEGs, differentially expressed genes
- DIP, Database of Interacting Proteins
- DNN, Deep neural network
- DT, Decision Tree
- Deep learning
- EMT, epithelial-mesenchymal transition
- FC, fully connected
- GA, Genetic Algorithm
- GANs, generative adversarial networks
- GEO, Gene Expression Omnibus
- HCC, hepatocellular carcinoma
- HPRD, Human Protein Reference Database
- KNN, K-nearest neighbor
- L-SVM, linear SVM
- LIMMA, linear models for microarray data
- LOOCV, Leave-one-out cross-validation
- LR, Logistic Regression
- MCCV, Monte Carlo cross-validation
- MLP, multilayer perceptron
- Machine learning
- Metastasis
- NPV, negative predictive value
- PCA, Principal component analysis
- PPI, protein-protein interaction
- PPV, positive predictive value
- RC, ridge classifier
- RF, Random Forest
- RFE, recursive feature elimination
- RMA, robust multi‐array average
- RNN, recurrent neural networks
- SGD, stochastic gradient descent
- SMOTE, synthetic minority over-sampling technique
- SVM, Support Vector Machine
- Se, sensitivity
- Sp, specificity
- TCGA, The Cancer Genome Atlas
- k-CV, k-fold cross validation
- mRMR, minimum redundancy maximum relevance
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Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review. Comput Struct Biotechnol J 2021; 19:2833-2850. [PMID: 34025952 PMCID: PMC8123783 DOI: 10.1016/j.csbj.2021.05.010] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/01/2021] [Accepted: 05/02/2021] [Indexed: 12/23/2022] Open
Abstract
The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.
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Key Words
- ABG, Arterial Blood Gas
- ADA, Adenosine Deaminase
- AI, Artificial Intelligence
- ANN, Artificial Neural Networks
- APTT, Activated Partial Thromboplastin Time
- ARMED, Attribute Reduction with Multi-objective Decomposition Ensemble optimizer
- AUC, Area Under the Curve
- Acc, Accuracy
- Adaboost, Adaptive Boosting
- Apol AI, Apolipoprotein AI
- Apol B, Apolipoprotein B
- Artificial intelligence
- BNB, Bernoulli Naïve Bayes
- BUN, Blood Urea Nitrogen
- CI, Confidence Interval
- CK-MB, Creatine Kinase isoenzyme
- CNN, Convolutional Neural Networks
- COVID-19
- CPP, COVID-19 Positive Patients
- CRP, C-Reactive Protein
- CRT, Classification and Regression Decision Tree
- CoxPH, Cox Proportional Hazards
- DCNN, Deep Convolutional Neural Networks
- DL, Deep Learning
- DLC, Density Lipoprotein Cholesterol
- DNN, Deep Neural Networks
- DT, Decision Tree
- Diagnosis
- ED, Emergency Department
- ESR, Erythrocyte Sedimentation Rate
- ET, Extra Trees
- FCV, Fold Cross Validation
- FL, Federated Learning
- FiO2, Fraction of Inspiration O2
- GBDT, Gradient Boost Decision Tree
- GBM light, Gradient Boosting Machine light
- GDCNN, Genetic Deep Learning Convolutional Neural Network
- GFR, Glomerular Filtration Rate
- GFS, Gradient boosted feature selection
- GGT, Glutamyl Transpeptidase
- GNB, Gaussian Naïve Bayes
- HDLC, High Density Lipoprotein Cholesterol
- INR, International Normalized Ratio
- Inception Resnet, Inception Residual Neural Network
- L1LR, L1 Regularized Logistic Regression
- LASSO, Least Absolute Shrinkage and Selection Operator
- LDA, Linear Discriminant Analysis
- LDH, Lactate Dehydrogenase
- LDLC, Low Density Lipoprotein Cholesterol
- LR, Logistic Regression
- LSTM, Long-Short Term Memory
- MCHC, Mean Corpuscular Hemoglobin Concentration
- MCV, Mean corpuscular volume
- ML, Machine Learning
- MLP, MultiLayer Perceptron
- MPV, Mean Platelet Volume
- MRMR, Maximum Relevance Minimum Redundancy
- Multimodal data
- NB, Naïve Bayes
- NLP, Natural Language Processing
- NPV, Negative Predictive Values
- Nadam optimizer, Nesterov Accelerated Adaptive Moment optimizer
- OB, Occult Blood test
- PCT, Thrombocytocrit
- PPV, Positive Predictive Values
- PWD, Platelet Distribution Width
- PaO2, Arterial Oxygen Tension
- Paco2, Arterial Carbondioxide Tension
- Prognosis
- RBC, Red Blood Cell
- RBF, Radial Basis Function
- RBP, Retinol Binding Protein
- RDW, Red blood cell Distribution Width
- RF, Random Forest
- RFE, Recursive Feature Elimination
- RSV, Respiratory Syncytial Virus
- SEN, Sensitivity
- SG, Specific Gravity
- SMOTE, Synthetic Minority Oversampling Technique
- SPE, Specificity
- SRLSR, Sparse Rescaled Linear Square Regression
- SVM, Support Vector Machine
- SaO2, Arterial Oxygen saturation
- Screening
- TBA, Total Bile Acid
- TTS, Training Test Split
- WBC, White Blood Cell count
- XGB, eXtreme Gradient Boost
- k-NN, K-Nearest Neighbor
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Abstract
Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.
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Key Words
- ANN, Artificial Neural Network
- AUC, Area Under Curve
- BCRSVM, Breast Cancer Support Vector Machine
- BN, Bayesian Network
- CFS, Correlation based Feature Selection
- Cancer recurrence
- Cancer survival
- Cancer susceptibility
- DT, Decision Tree
- ES, Early Stopping algorithm
- GEO, Gene Expression Omnibus
- HTT, High-throughput Technologies
- LCS, Learning Classifying Systems
- ML, Machine Learning
- Machine learning
- NCI caArray, National Cancer Institute Array Data Management System
- NSCLC, Non-small Cell Lung Cancer
- OSCC, Oral Squamous Cell Carcinoma
- PPI, Protein–Protein Interaction
- Predictive models
- ROC, Receiver Operating Characteristic
- SEER, Surveillance, Epidemiology and End results Database
- SSL, Semi-supervised Learning
- SVM, Support Vector Machine
- TCGA, The Cancer Genome Atlas Research Network
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