scholarly journals Analyses of used engine oils via atomic spectroscopy – Influence of sample pre-treatment and machine learning for engine type classification and lifetime assessment

Talanta ◽  
2021 ◽  
pp. 122431
Author(s):  
Roman Grimmig ◽  
Simon Lindner ◽  
Philipp Gillemot ◽  
Markus Winkler ◽  
Steffen Witzleben
Keyword(s):  
Machine Learning ◽  
Atomic Spectroscopy ◽  
Engine Oils ◽  
Pre Treatment ◽  
Type Classification ◽  
Engine Type

scholarly journals MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrea Delli Pizzi ◽  
Antonio Maria Chiarelli ◽  
Piero Chiacchiaretta ◽  
Martina d’Annibale ◽  
Pierpaolo Croce ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rectal Cancer ◽  
Treatment Response ◽  
Randomized Clinical Trials ◽  
Locally Advanced ◽  
Neoadjuvant Treatment ◽  
Learning Model ◽  
Partial Least Square ◽  
Machine Learning Model ◽  
Pre Treatment

AbstractNeoadjuvant chemo-radiotherapy (CRT) followed by total mesorectal excision (TME) represents the standard treatment for patients with locally advanced (≥ T3 or N+) rectal cancer (LARC). Approximately 15% of patients with LARC shows a complete response after CRT. The use of pre-treatment MRI as predictive biomarker could help to increase the chance of organ preservation by tailoring the neoadjuvant treatment. We present a novel machine learning model combining pre-treatment MRI-based clinical and radiomic features for the early prediction of treatment response in LARC patients. MRI scans (3.0 T, T2-weighted) of 72 patients with LARC were included. Two readers independently segmented each tumor. Radiomic features were extracted from both the “tumor core” (TC) and the “tumor border” (TB). Partial least square (PLS) regression was used as the multivariate, machine learning, algorithm of choice and leave-one-out nested cross-validation was used to optimize hyperparameters of the PLS. The MRI-Based “clinical-radiomic” machine learning model properly predicted the treatment response (AUC = 0.793, p = 5.6 × 10–5). Importantly, the prediction improved when combining MRI-based clinical features and radiomic features, the latter extracted from both TC and TB. Prospective validation studies in randomized clinical trials are warranted to better define the role of radiomics in the development of rectal cancer precision medicine.

Machine Learning techniques for solar irradiation nowcasting: Cloud type classification forecast through satellite data and imagery

2022 ◽  
Vol 305 ◽  
pp. 117834
Author(s):  
Alfredo Nespoli ◽  
Alessandro Niccolai ◽  
Emanuele Ogliari ◽  
Giovanni Perego ◽  
Elena Collino ◽  
...  
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Machine Learning Techniques ◽  
Solar Irradiation ◽  
Cloud Type ◽  
Learning Techniques ◽  
Type Classification

scholarly journals Machine learning for single cell genomics data analysis

2021 ◽  
Author(s):  
Félix Raimundo ◽  
Laetitia Papaxanthos ◽  
Céline Vallot ◽  
Jean-Philippe Vert
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Network Inference ◽  
Method Development ◽  
Biological Knowledge ◽  
Omics Data ◽  
Gene Regulatory Network Inference ◽  
Multimodal Data ◽  
Low Dimensional ◽  
Type Classification

AbstractSingle-cell omics technologies produce large quantities of data describing the genomic, transcriptomic or epigenomic profiles of many individual cells in parallel. In order to infer biological knowledge and develop predictive models from these data, machine learning (ML)-based model are increasingly used due to their flexibility, scalability, and impressive success in other fields. In recent years, we have seen a surge of new ML-based method development for low-dimensional representations of single-cell omics data, batch normalization, cell type classification, trajectory inference, gene regulatory network inference or multimodal data integration. To help readers navigate this fast-moving literature, we survey in this review recent advances in ML approaches developed to analyze single-cell omics data, focusing mainly on peer-reviewed publications published in the last two years (2019-2020).

scholarly journals CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kanggeun Lee ◽  
Hyoung-oh Jeong ◽  
Semin Lee ◽  
Won-Ki Jeong
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Genomic Data ◽  
The Cancer Genome Atlas ◽  
Cancer Type ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Somatic Alterations ◽  
The Impact ◽  
Type Classification

AbstractWith recent advances in DNA sequencing technologies, fast acquisition of large-scale genomic data has become commonplace. For cancer studies, in particular, there is an increasing need for the classification of cancer type based on somatic alterations detected from sequencing analyses. However, the ever-increasing size and complexity of the data make the classification task extremely challenging. In this study, we evaluate the contributions of various input features, such as mutation profiles, mutation rates, mutation spectra and signatures, and somatic copy number alterations that can be derived from genomic data, and further utilize them for accurate cancer type classification. We introduce a novel ensemble of machine learning classifiers, called CPEM (Cancer Predictor using an Ensemble Model), which is tested on 7,002 samples representing over 31 different cancer types collected from The Cancer Genome Atlas (TCGA) database. We first systematically examined the impact of the input features. Features known to be associated with specific cancers had relatively high importance in our initial prediction model. We further investigated various machine learning classifiers and feature selection methods to derive the ensemble-based cancer type prediction model achieving up to 84% classification accuracy in the nested 10-fold cross-validation. Finally, we narrowed down the target cancers to the six most common types and achieved up to 94% accuracy.

scholarly journals Novel Estimation of Penumbra Zone Based on Infarct Growth Using Machine Learning Techniques in Acute Ischemic Stroke

2020 ◽  
Vol 9 (6) ◽  
pp. 1977
Author(s):  
Yoon-Chul Kim ◽  
Hyung Jun Kim ◽  
Jong-Won Chung ◽  
In Gyeong Kim ◽  
Min Jung Seong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diffusion Weighted Imaging ◽  
Infarct Volume ◽  
Intraclass Correlation ◽  
Artery Occlusion ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Pre Treatment ◽  
Cerebral Artery Occlusion ◽  
Growth Distribution

While the penumbra zone is traditionally assessed based on perfusion–diffusion mismatch, it can be assessed based on machine learning (ML) prediction of infarct growth. The purpose of this work was to develop and validate an ML method for the prediction of infarct growth distribution and volume, in cases of successful (SR) and unsuccessful recanalization (UR). Pre-treatment perfusion-weighted, diffusion-weighted imaging (DWI) data, and final infarct lesions annotated from day-7 DWI from patients with middle cerebral artery occlusion were utilized to develop and validate two ML models for prediction of tissue fate. SR and UR models were developed from data in patients with modified treatment in cerebral infarction (mTICI) scores of 2b–3 and 0–2a, respectively. When compared to manual infarct annotation, ML-based infarct volume predictions resulted in an intraclass correlation coefficient (ICC) of 0.73 (95% CI = 0.31–0.91, p < 0.01) for UR, and an ICC of 0.87 (95% CI = 0.73–0.94, p < 0.001) for SR. Favorable outcomes for mismatch presence and absence in SR were 50% and 36%, respectively, while they were 61%, 56%, and 25%, respectively, for the low, intermediate, and high infarct growth groups. The presented method can offer novel and alternative insights into selecting patients for recanalization therapy and predicting functional outcome.

Machine Learning to Improve the Prioritization and Effectiveness of Pre-Treatment Physics Chart Checks

2020 ◽  
Vol 108 (3) ◽  
pp. S54-S55
Author(s):  
J.W. Shumway ◽  
M. Pillai ◽  
J. Dooley ◽  
S.K. Das ◽  
B.S. Chera
Keyword(s):  
Machine Learning ◽  
Pre Treatment

Machine Learning Models for Cancer Type Classification with Unstructured Data

2020 ◽  
Vol 24 (2) ◽  
Author(s):  
Erick E. Montelongo González ◽  
José A. Reyes Ortiz ◽  
Beatriz A. González Beltrán
Keyword(s):  
Machine Learning ◽  
Unstructured Data ◽  
Cancer Type ◽  
Learning Models ◽  
Type Classification ◽  
Machine Learning Models

Analysis of malware behavior: Type classification using machine learning

2015 ◽  
Author(s):  
Radu S. Pirscoveanu ◽  
Steven S. Hansen ◽  
Thor M. T. Larsen ◽  
Matija Stevanovic ◽  
Jens Myrup Pedersen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Behavior Type ◽  
Type Classification

scholarly journals Predicting lithium treatment response in bipolar patients using gender-specific gene expression biomarkers and machine learning

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 474
Author(s):  
Andy R. Eugene ◽  
Jolanta Masiak ◽  
Beata Eugene
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Linear Models ◽  
Learning Algorithm ◽  
Lithium Treatment ◽  
Study Data ◽  
Differentially Expressed ◽  
Specific Gene ◽  
Pre Treatment ◽  
Bipolar Patients

Background: We sought to test the hypothesis that transcriptiome-level genes signatures are differentially expressed between male and female bipolar patients, prior to lithium treatment, in a patient cohort who later were clinically classified as lithium treatment responders. Methods: Gene expression study data was obtained from the Lithium Treatment-Moderate dose Use Study data accessed from the National Center for Biotechnology Information’s Gene Expression Omnibus via accession number GSE4548. Differential gene expression analysis was conducted using the Linear Models for Microarray and RNA-Seq (limma) package and the Random Forests machine learning algorithm in R. Results: In pre-treatment lithium responders, the following genes were found having a greater than 0.5 fold-change, and differentially expressed indicating a male bias: RBPMS2, SIDT2, CDH23, LILRA5, and KIR2DS5; while the female-biased genes were: HLA-H, RPS23, FHL3, RPL10A, NBPF14, PSTPIP2, FAM117B, CHST7, and ABRACL. Conclusions: Using machine learning, we developed a pre-treatment gender- and gene-expression-based predictive model selective for lithium responders with an ROC AUC of 0.92 for men and an ROC AUC of 1 for women.

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