Machine Learning For Identification Research Articles

ADGRE5-centered Tsurv model in T cells recognizes responders to neoadjuvant cancer immunotherapy.

Neoadjuvant immunotherapy with anti-programmed death-1 (neo-antiPD1) has revolutionized perioperative methods for improvement of overall survival (OS), while approaches for major pathologic response patients' (MPR) recognition along with methods for overcoming non-MPR resistance are still in urgent need. We utilized and integrated publicly-available immune checkpoint inhibitors regimens (ICIs) single-cell (sc) data as the discovery datasets, and innovatively developed a cell-communication analysis pipeline, along with a VIPER-based-SCENIC process, to thoroughly dissect MPR-responding subsets. Besides, we further employed our own non-small cell lung cancer (NSCLC) ICIs cohort's sc data for validation in-silico. Afterward, we resorted to ICIs-resistant murine models developed by us with multimodal investigation, including bulk-RNA-sequencing, Chip-sequencing and high-dimensional cytometry by time of flight (CYTOF) to consolidate our findings in-vivo. To comprehensively explore mechanisms, we adopted 3D ex-vivo hydrogel models for analysis. Furthermore, we constructed an ADGRE5-centered Tsurv model from our discovery dataset by machine learning (ML) algorithms for a wide range of tumor types (NSCLC, melanoma, urothelial cancer, etc.) and verified it in peripheral blood mononuclear cells (PBMCs) sc datasets. Through a meta-analysis of multimodal sequential sc sequencing data from pre-ICIs and post-ICIs, we identified an MPR-expanding T cells meta-cluster (MPR-E) in the tumor microenvironment (TME), characterized by a stem-like CD8+ T cluster (survT) with STAT5-ADGRE5 axis enhancement compared to non-MPR or pre-ICIs TME. Through multi-omics analysis of murine TME, we further confirmed the existence of survT with silenced function and immune checkpoints (ICs) in MPR-E. After verification of the STAT5-ADGRE5 axis of survT in independent ICIs cohorts, an ADGRE5-centered Tsurv model was then developed through ML for identification of MPR patients pre-ICIs and post-ICIs, both in TME and PBMCs, which was further verified in pan-cancer immunotherapy cohorts. Mechanistically, we unveiled ICIs stimulated ADGRE5 upregulation in a STAT5-IL32 dependent manner in a 3D ex-vivo system (3D-HYGTIC) developed by us previously, which marked Tsurv with better survival flexibility, enhanced stemness and potential cytotoxicity within TME. Our research provides insights into mechanisms underlying MPR in neo-antiPD1 and a well-performed model for the identification of non-MPR.

Application of machine learning in identification of pathogenic microbes

Application of machine learning in identification of pathogenic microbes

Application of mechanical product design parameter optimization based on machine learning in identification

The selection of mechanical product design parameters is a key factor in determining product quality. The defects caused by unreasonable product parameter design are one of the main reasons for the extension of the product development cycle and the impact on product market competitiveness. A slurry blade is one of the most common mechanical structures, which is widely used in wind power, ship, hydropower, and other fields. To identify the parameter defects of mechanical product design in the early stage of the design stage, this paper proposes the parameter design optimization identification model based on Stacking integrated learning and studies the parameter selection and results in the analysis of the slurry blade model. Space Claim software in Ansys software establishes three-dimensional slurry blade models with different scales. Combined with the NSGA II optimization algorithm, the multi-objective optimization design of the aerofoil structure is carried out. The CFD software is used to simulate and verify the slurry blade model after parameter design optimization. The results show that the optimization identification model for the design of slurry blade parameters based on the Stacking integrated learning algorithm can realize the influence of the unexpected change of design parameters on the design results. The prediction accuracy of cavitation proportion is high under different working conditions, and the absolute error is <0.02. The simulation of the optimized blade shows that the cavitation proportion e reaches a minimum of 0.005. The purpose of guiding the selection of design parameters, machining parameters, and assembly accuracy parameters is achieved, the structural defects of the slurry blade are reduced, and the working efficiency of the edge is improved.

Application of machine learning in the identification of fluvial-lacustrine lithofacies from well logs: A case study from Sichuan Basin, China

The lithofacies identification is critical for forecasting sweet spots of hydrocarbon explorations. Well logs are widely used in lithofacies identifications because they are petrophysical measurements of subsurface stratigraphy which reflect lithological successions and depositional processes. The traditional lithofacies identification from well logs is a manual work that is time-consuming and bias-prone. An automated and bias-free method is in demand. To this end, we created a lithofacies dataset of eleven wells with well log records and lithofacies descriptions that were interpreted manually based on facies analysis of drilling cutting descriptions and well logs. Then we developed machine learning models that were trained using the lithofacies dataset of the fluvial-lacustrine Upper Triassic Xujiahe and Lower Jurassic Ziliujing formations in Yuanba Area, northern Sichuan Basin of southwestern China. By employing extreme gradient boosting and resampling algorithms, this machine learning model is efficient and outperforms support vector machine and multiple-layer perceptron, as indicated by its highest accuracy and F1-score of 0.90, the highest AUC of 0.94, as well as the shortest training time. Moreover, the result suggests that resampling is necessary for lithofacies identification with the imbalanced dataset. A combined method of oversampling and undersampling is better than a single resampling method. This study presents a successful application of machine learning in fluvial-lacustrine lithofacies identification from well logs and suggests the great potentiality of machine learning in subsurface hydrocarbon explorations.

Accuracy of machine learning in identification of dental implant systems in radiographs – A systematic review and meta-analysis

Machine learning has played a promising role in medical diagnosis. The aim of this systematic review was to evaluate the accuracy of machine learning in identification of dental implant systems from radiographs. This systematic review was conducted by searching four electronic databases, PubMed, SCOPUS, Cochrane Library, and Google Scholar. Inclusion criteria were studies that used machine learning for implant identification. Our search yielded 87,189 studies, of which a total of eight studies were found which used machine learning for implant identification. Of the included studies, three studies provided the required data to conduct meta-analysis. The overall pooled estimate of accuracy of the three included studies was 95.43%. Machine learning appears to be practically efficient in implant recognition. The findings of this review suggested an inadequate reporting of studies due to a lack of standardized guidelines for reporting and conducting the studies that investigate machine learning in implant identification. This could limit the reliable interpretation of the reported accuracy.

Machine Learning in Identification and Control of Biological Disasters

Machine Learning in Identification and Control of Biological Disasters

The role of machine learning in identification of early gestational diabetes mellitus prediction models

The role of machine learning in identification of early gestational diabetes mellitus prediction models

Evaluation of radiomics and machine learning in identification of aggressive tumor features in renal cell carcinoma (RCC).

The purpose of this study was to evaluate the use of CT radiomics features and machine learning analysis to identify aggressive tumor features, including high nuclear grade (NG) and sarcomatoid (sarc) features, in large renal cell carcinomas (RCCs). CT-based volumetric radiomics analysis was performed on non-contrast (NC) and portal venous (PV) phase multidetector computed tomography images of large (> 7cm) untreated RCCs in 141 patients (46W/95M, mean age 60years). Machine learning analysis was applied to the extracted radiomics data to evaluate for association with high NG (grade 3-4), with multichannel analysis for NG performed in a subset of patients (n = 80). A similar analysis was performed in a sarcomatoid rich cohort (n = 43, 31M/12F, mean age 63.7 years) using size-matched non-sarcomatoid controls (n = 49) for identification of sarcomatoid change. The XG Boost Model performed best on the tested data. After manual and machine feature extraction, models consisted of 3, 7, 5, 10 radiomics features for NC sarc, PV sarc, NC NG and PV NG, respectively. The area under the receiver operating characteristic curve (AUC) for these models was 0.59, 0.65, 0.69 and 0.58 respectively. The multichannel NG model extracted 6 radiomic features using the feature selection strategy and showed an AUC of 0.67. Statistically significant but weak associations between aggressive tumor features (high nuclear grade, sarcomatoid features) in large RCC were identified using 3D radiomics and machine learning analysis.

Machine learning for identification of dental implant systems based on shape - A descriptive study.

Aim:To evaluate the efficacy of machine learning in identification of dental implant systems from panoramic radiographs based on the shape.Settings and Design:In vitro–Descriptive studyMaterials and Methods:A Dataset of digital panoramic radiographs of three dental implant systems were obtained. The images were divided into two datasets: one for training and another for testing of the machine learning models. Machine learning algorithms namely, support vector machine, logistic regression, K Nearest neighbor and X boost classifiers were trained to classify implant systems from radiographs, based on the shape using Hu and Eigen values. Performance of algorithms was evaluated by its classification accuracy using the test dataset.Statistical Analysis Used:Accuracy and recover operating characteristic (ROC) curve were calculated to analyze the performance of the model.Results:The classifiers tested in the study were able to identify the implant systems with an average accuracy of 0.67. Of the classifiers trained, logistic regression showed best overall performance followed by SVM, KNN and X boost classifiers.Conclusions:Machine learning models tested in the study are proficient enough to identify dental implant systems; hence we are proposing machine learning as a method for implant identification and can be generalized with a larger dataset and more cross sectional studies.

OptimalFlow: optimal transport approach to flow cytometry gating and population matching

BackgroundData obtained from flow cytometry present pronounced variability due to biological and technical reasons. Biological variability is a well-known phenomenon produced by measurements on different individuals, with different characteristics such as illness, age, sex, etc. The use of different settings for measurement, the variation of the conditions during experiments and the different types of flow cytometers are some of the technical causes of variability. This mixture of sources of variability makes the use of supervised machine learning for identification of cell populations difficult. The present work is conceived as a combination of strategies to facilitate the task of supervised gating.ResultsWe propose optimalFlowTemplates, based on a similarity distance and Wasserstein barycenters, which clusters cytometries and produces prototype cytometries for the different groups. We show that supervised learning, restricted to the new groups, performs better than the same techniques applied to the whole collection. We also present optimalFlowClassification, which uses a database of gated cytometries and optimalFlowTemplates to assign cell types to a new cytometry. We show that this procedure can outperform state of the art techniques in the proposed datasets. Our code is freely available as optimalFlow, a Bioconductor R package at https://bioconductor.org/packages/optimalFlow.ConclusionsoptimalFlowTemplates + optimalFlowClassification addresses the problem of using supervised learning while accounting for biological and technical variability. Our methodology provides a robust automated gating workflow that handles the intrinsic variability of flow cytometry data well. Our main innovation is the methodology itself and the optimal transport techniques that we apply to flow cytometry analysis.

Use of Steroid Profiling Combined With Machine Learning for Identification and Subtype Classification in Primary Aldosteronism

Most patients with primary aldosteronism, a major cause of secondary hypertension, are not identified or appropriately treated because of difficulties in diagnosis and subtype classification. Applications of artificial intelligence combined with mass spectrometry-based steroid profiling could address this problem. To assess whether plasma steroid profiling combined with machine learning might facilitate diagnosis and treatment stratification of primary aldosteronism, particularly for patients with unilateral adenomas due to pathogenic KCNJ5 sequence variants. This diagnostic study was conducted at multiple tertiary care referral centers. Steroid profiles were measured from June 2013 to March 2017 in 462 patients tested for primary aldosteronism and 201 patients with hypertension. Data analyses were performed from September 2018 to August 2019. The aldosterone to renin ratio and saline infusion tests were used to diagnose primary aldosteronism. Subtyping was done by adrenal venous sampling and follow-up of patients who underwent adrenalectomy. Statistical tests and machine-learning algorithms were applied to plasma steroid profiles. Areas under receiver operating characteristic curves, sensitivity, specificity, and other diagnostic performance measures were calculated. Primary aldosteronism was confirmed in 273 patients (165 men [60%]; mean [SD] age, 51 [10] years), including 134 with bilateral disease and 139 with unilateral adenomas (58 with and 81 without somatic KCNJ5 sequence variants). Plasma steroid profiles varied according to disease subtype and were particularly distinctive in patients with adenomas due to KCNJ5 variants, who showed better rates of biochemical cure after adrenalectomy than other patients. Among patients tested for primary aldosteronism, a selection of 8 steroids in combination with the aldosterone to renin ratio showed improved effectiveness for diagnosis over either strategy alone. In contrast, the steroid profile alone showed superior performance over the aldosterone to renin ratio for identifying unilateral disease, particularly adenomas due to KCNJ5 variants. Among 632 patients included in the analysis, machine learning-designed combinatorial marker profiles of 7 steroids alone both predicted primary aldosteronism in 1 step and subtyped patients with unilateral adenomas due to KCNJ5 variants at diagnostic sensitivities of 69% (95% CI, 68%-71%) and 85% (95% CI, 81%-88%), respectively, and at specificities of 94% (95% CI, 93%-94%) and 97% (95% CI, 97%-98%), respectively. The validation series yielded comparable diagnostic performance. Machine learning-designed combinatorial plasma steroid profiles may facilitate both screening for primary aldosteronism and identification of patients with unilateral adenomas due to pathogenic KCNJ5 variants, who are most likely to show benefit from surgical intervention.

Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1.

The human carboxylesterase 1 (CES1), responsible for the biotransformation of many diverse therapeutic agents, may contribute to the occurrence of adverse drug reactions and therapeutic failure through drug interactions. The present study is designed to address the issue of potential drug interactions resulting from the inhibition of CES1. Based on an ensemble of 10 crystal structures complexed with different ligands and a set of 294 known CES1 ligands, we used docking (Autodock Vina) and machine learning methodologies (LDA, QDA and multilayer perceptron), considering the different energy terms from the scoring function to assess the best combination to enable the identification of CES1 inhibitors. The protocol was then applied on a library of 1114 FDA-approved drugs and eight drugs were selected for in vitro CES1 inhibition. An inhibition effect was observed for diltiazem (IC50 = 13.9 µM). Three others drugs (benztropine, iloprost and treprostinil), exhibited a weak CES1 inhibitory effects with IC50 values of 298.2 µM, 366.8 µM and 391.6 µM respectively. In conclusion, the binding site of CES1 is relatively flexible and can adapt its conformation to different types of ligands. Combining ensemble docking and machine learning approaches improves the prediction of CES1 inhibitors compared to a docking study using only one crystal structure.

A novel joint HCPMMP method for automatically classifying Alzheimer’s and different stage MCI patients

A 360-area surface-based cortical parcellation was recently generated using multimodal data in a group average of 210 healthy young adults from the Human Connectome Project (HCP). In order to automatically and accurately identify mild cognitive impairment (MCI) at its two levels (early MCI and late MCI), Alzheimer's disease (AD) and healthy control (HC), a novel joint HCP MMP method was first proposed to delineate the cortical architecture and function connectivity in a group of non healthy adults. The proposed method was applied to register a dataset of 96 resting-state functional connectomes from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to Connectivity Informatics Technology Initiative (CIFTI) space and parcellated brain into human connectome project multi-modal parcellation (HCPMMP) with 360 areas. Various network features in each node of the connectivity network were considered as the candidate features for classification.The fine-grained multi-modal based on HCP-MMP combined with machine learning in identification for EMCI, LMCI, AD and HC. Applying various network features, including strength, betweenness centrality, clustering coefficient, local efficiency, eigenvector centrality, etc, we trained and tested several machine learning models. Thousands of features were processed by filter and wrapper feature selection procedures, and finally there were thirty features to be selected to achieve classification accuracies of 93.8% for EMCI vs. HC, 95.8% for LMCI vs. HC, 95.8% for AD vs. HC, and 91.7% for LMCI vs. AD, respectively by using support vector machine (SVM) algorithm. Most of the selected features locate in the region of temporal or cingulate cortex. Compared with previous studies, our results demonstrate the superiority of the proposed method over existing techniques.

The Utility of Machine Learning in Identification of Key Geophysical and Geochemical Datasets: A Case Study in Lithological Mapping in the Central African Copper Belt

Random Forests, a supervised machine learning algorithm, provides a robust, data driven means of predicting lithology from geophysical, geochemical and remote sensing data. As an essential part of input selection, datasets are ranked in order of importance to the classification outcome. Those ranked most important provide, on average, the most decisive split between lithological classes. These rankings provide explorers with an additional line of reasoning to complement conventional, geophysical and geochemical interpretation workflows. The approach shows potential to aid in identifying important criteria for distinguishing geological map units during early stage exploration. This can assist in directing subsequent expenditure towards the acquisition and further development of datasets which will be the most productive for mapping. In this case study, we use Random Forests to classify the lithology of a project in the Central African Copper-Belt, Zambia. The project area boasts extensive magnetic, radiometric, electromagnetic and multi-element geochemical coverage but only sparse geological observations. Under various training data paradigms, Random Forests produced a series of varying but closely related lithological maps. In this study, training data were restricted to outcrop, simulating the data available at the early stages of the project. Variable ranking highlighted those datasets which were of greatest importance to the result. Both geophysical and geochemical datasets were well represented in the highest ranking variables, reinforcing the importance of access to both data types. Further analysis showed that in many cases, the importance of high ranking datasets had a plausible geological explanation, often consistent with conventional interpretation. In other cases the method provides new insights, identifying datasets which may not have been considered from the outset of a new project.