scholarly journals Gene Expression Profiles in Sporadic ALS Fibroblasts Define Disease Subtypes and The Metabolic Effects of the Investigational Drug EH301

2021 ◽  
Author(s):  
Jasmine A. Fels ◽  
Gabriella Casalena ◽  
Csaba Konrad ◽  
Holly Holmes ◽  
Ryan W. Dellinger ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Disease Progression ◽  
Metabolic Stress ◽  
Learning Model ◽  
Metabolic Effects ◽  
Investigational Drug ◽  
Machine Learning Model ◽  
Sporadic Als ◽  
The Impact

Abstract Background: Majority of ALS cases are sporadic (sALS), as they lack defined genetic causes. Metabolic alterations shared between the nervous system and skin fibroblasts have emerged in ALS. Recently, we found that a subgroup of sALS fibroblasts (sALS1) is characterized by metabolic profiles (metabotype) distinct from other sALS cases (sALS2) and controls, suggesting that metabolic therapies could be effective in sALS. The metabolic modulators nicotinamide riboside and pterostilbene (EH301) are under clinical development for the treatment of ALS. Here, we studied the metabolome and transcriptome of sALS cells to understand the molecular bases of sALS metabotypes and the impact of EH301.Methods: Six fibroblast cell lines (3 male and 3 female subjects of similar ages) were used for each group (sALS1, sALS2, and controls). Metabolomics and transcriptomics were investigated at baseline and after EH301 treatment. Differential gene expression (DEGs) and metabolite abundance were assessed by a Wald Test and ANOVA, respectively, with FDR correction, and pathway analyses were performed. EH301 protection against metabolic stress was tested by thiol depletion. Weighted gene co-expression network analysis (WGCNA) was used to investigate the association of metabolic and clinical features and was also performed on the Answer ALS dataset from induced motor neurons (iMN). A machine learning model based on DEGs was tested as a sALS disease progression predictor. Results: We found that the sALS1 transcriptome is distinct from sALS2 and that EH301 modifies gene expression differently in sALS1, sALS2, and controls. Furthermore, EH301 had strong protective effects against metabolic stress, which is linked to anti-inflammatory and antioxidant pathways. WGCNA revealed that ALS functional rating scale and metabotypes are associated with gene modules enriched for cell cycle, immunity, autophagy, and metabolism terms, which are modified by EH301. Meta-analysis of publicly available transcriptomics data from iMNs confirmed functional associations of genes correlated with disease traits. A small subset of genes differentially expressed in sALS fibroblasts could be used in a machine learning model to predict disease progression.Conclusions: Multi-omics analyses of patient-derived fibroblasts highlighted differential metabolic and transcriptomic profiles in sALS metabotypes, which translate into differential responses to the investigational drug EH301.

scholarly journals Author Correction: Machine learning model to project the impact of COVID-19 on US motor gasoline demand

Nature Energy ◽  
2020 ◽  
Vol 5 (12) ◽  
pp. 1051-1052
Author(s):  
Shiqi Ou ◽  
Xin He ◽  
Weiqi Ji ◽  
Wei Chen ◽  
Lang Sui ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Gasoline Demand ◽  
Motor Gasoline ◽  
Machine Learning Model ◽  
The Impact

scholarly journals Machine learning model to project the impact of COVID-19 on US motor gasoline demand

Nature Energy ◽  
2020 ◽  
Vol 5 (9) ◽  
pp. 666-673 ◽  
Author(s):  
Shiqi Ou ◽  
Xin He ◽  
Weiqi Ji ◽  
Wei Chen ◽  
Lang Sui ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Gasoline Demand ◽  
Motor Gasoline ◽  
Machine Learning Model ◽  
The Impact

scholarly journals Development of a severity of disease score and classification model by machine learning for hospitalized COVID-19 patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0240200
Author(s):  
Miguel Marcos ◽  
Moncef Belhassen-García ◽  
Antonio Sánchez-Puente ◽  
Jesús Sampedro-Gomez ◽  
Raúl Azibeiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Ventilation ◽  
Disease Progression ◽  
Characteristic Curve ◽  
Severe Disease ◽  
Learning Model ◽  
Classification Model ◽  
Blood Oxygen Saturation ◽  
Severity Of Disease ◽  
Machine Learning Model

Background Efficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management. Methods We trained, validated, and externally tested a machine-learning model to early identify patients who will die or require mechanical ventilation during hospitalization from clinical and laboratory features obtained at admission. A development cohort with 918 Covid-19 patients was used for training and internal validation, and 352 patients from another hospital were used for external testing. Performance of the model was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity and specificity. Results A total of 363 of 918 (39.5%) and 128 of 352 (36.4%) Covid-19 patients from the development and external testing cohort, respectively, required mechanical ventilation or died during hospitalization. In the development cohort, the model obtained an AUC of 0.85 (95% confidence interval [CI], 0.82 to 0.87) for predicting severity of disease progression. Variables ranked according to their contribution to the model were the peripheral blood oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) ratio, age, estimated glomerular filtration rate, procalcitonin, C-reactive protein, updated Charlson comorbidity index and lymphocytes. In the external testing cohort, the model performed an AUC of 0.83 (95% CI, 0.81 to 0.85). This model is deployed in an open source calculator, in which Covid-19 patients at admission are individually stratified as being at high or non-high risk for severe disease progression. Conclusions This machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients.

scholarly journals Development of a severity of disease score and classification model by machine learning for hospitalized COVID-19 patients

2020 ◽  
Author(s):  
Miguel Marcos ◽  
Moncef Belhassen-Garcia ◽  
Antonio Sanchez- Puente ◽  
Jesus Sampedro-Gomez ◽  
Raul Azibeiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Ventilation ◽  
Disease Progression ◽  
Characteristic Curve ◽  
Severe Disease ◽  
Learning Model ◽  
Classification Model ◽  
Blood Oxygen Saturation ◽  
Severity Of Disease ◽  
Machine Learning Model

BACKGROUND: Efficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management. METHODS: We trained, validated, and externally tested a machine-learning model to early identify patients who will die or require mechanical ventilation during hospitalization from clinical and laboratory features obtained at admission. A development cohort with 918 Covid-19 patients was used for training and internal validation, and 352 patients from another hospital were used for external testing. Performance of the model was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity and specificity. RESULTS: A total of 363 of 918 (39.5%) and 128 of 352 (36.4%) Covid-19 patients from the development and external testing cohort, respectively, required mechanical ventilation or died during hospitalization. In the development cohort, the model obtained an AUC of 0.85 (95% confidence interval [CI], 0.82 to 0.87) for predicting severity of disease progression. Variables ranked according to their contribution to the model were the peripheral blood oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) ratio, age, estimated glomerular filtration rate, procalcitonin, C-reactive protein, updated Charlson comorbidity index and lymphocytes. In the external testing cohort, the model performed an AUC of 0.83 (95% CI, 0.81 to 0.85). This model is deployed in an open source calculator, in which Covid-19 patients at admission are individually stratified as being at high or non-high risk for severe disease progression. CONCLUSIONS: This machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients.

scholarly journals Forecasting Amazon Rain-Forest Deforestation Using a Hybrid Machine Learning Model

2022 ◽  
Vol 14 (2) ◽  
pp. 691
Author(s):  
David Dominguez ◽  
Luis de Juan del Villar ◽  
Odette Pantoja ◽  
Mario González-Rodríguez
Keyword(s):  
Machine Learning ◽  
Regression Model ◽  
Rain Forest ◽  
Time Series Data ◽  
Learning Model ◽  
Series Data ◽  
Amazon Rain Forest ◽  
Machine Learning Model ◽  
Hybrid Machine ◽  
The Impact

The present work aims to carry out an analysis of the Amazon rain-forest deforestation, which can be analyzed from actual data and predicted by means of artificial intelligence algorithms. A hybrid machine learning model was implemented, using a dataset consisting of 760 Brazilian Amazon municipalities, with static data, namely geographical, forest, and watershed, among others, together with a time series data of annual deforestation area for the last 20 years (1999–2019). The designed learning model combines dense neural networks for the static variables and a recurrent Long Short Term Memory neural network for the temporal data. Many iterations were performed on augmented data, testing different configurations of the regression model, for adjusting the model hyper-parameters, and generating a battery of tests to obtain the optimal model, achieving a R-squared score of 87.82%. The final regression model predicts the increase in annual deforestation area (square kilometers), for a decade, from 2020 to 2030, predicting that deforestation will reach 1 million square kilometers by 2030, accounting for around 15% compared with the present 1%, of the between 5.5 and 6.7 millions of square kilometers of the rain-forest. The obtained results will help to understand the impact of man’s footprint on the Amazon rain-forest.

scholarly journals Exploring the Impact of Data Poisoning Attacks on Machine Learning Model Reliability

2021 ◽  
Vol 192 ◽  
pp. 2624-2632
Author(s):  
Laura Verde ◽  
Fiammetta Marulli ◽  
Stefano Marrone
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Machine Learning Model ◽  
Model Reliability ◽  
The Impact

Evaluating the Effectiveness of Marketing Campaigns for Malls Using a Novel Interpretable Machine Learning Model

2021 ◽  
Author(s):  
Tong Wang ◽  
Cheng He ◽  
Fujie Jin ◽  
Yu Jeffrey Hu
Keyword(s):  
Machine Learning ◽  
Recent Trend ◽  
Learning Model ◽  
Response Curves ◽  
Interpretable Machine Learning ◽  
Machine Learning Model ◽  
Different Types ◽  
Sales Incentives ◽  
Marketing Spending ◽  
The Impact

We develop a novel interpretable machine learning model, GANNM, and use newly available data to evaluate how different types of marketing campaigns and budget allocations influence malls’ customer traffic. We observe that the response curves that measure the impact of campaign budget on customer traffic differ for different categories of campaigns, with sales incentives or experience incentives, during peak periods, off-peak periods, or online promotion periods. Based on such accurate response curves from GANNM, the optimized budget allocation is estimated to yield a 11.2% increase in customer traffic compared with the original allocation. Our findings provide novel insights on managing mall campaigns. Mall managers should increase marketing spending to areas that were likely overlooked before and avoid over-crowding budget to campaigns during times with high levels of competition and are likely already over-marketed. We provide empirical evidence showing that the recent trend of employing novel approaches for enhancing customer experience in physical stores can effectively encourage customers to visit malls. Furthermore, we show that online promotions could also create opportunities for offline businesses—investing in campaigns in the major online promotion periods could significantly increase customer traffic for malls, given sufficient investment in the campaigns to raise customer awareness.

scholarly journals ECMarker: Interpretable machine learning model identifies gene expression biomarkers predicting clinical outcomes and reveals molecular mechanisms of human disease in early stages

2019 ◽  
Author(s):  
Ting Jin ◽  
Nam D. Nguyen ◽  
Flaminia Talos ◽  
Daifeng Wang
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Lung Cancer ◽  
Human Disease ◽  
Gene Networks ◽  
Learning Model ◽  
Regulatory Mechanisms ◽  
Disease Development ◽  
Early Stages ◽  
Machine Learning Model

AbstractGene expression and regulation, a key molecular mechanism driving human disease development, remains elusive, especially at early stages. Integrating the increasing amount of population-level genomic data and understanding gene regulatory mechanisms in disease development are still challenging. Machine learning has emerged to solve this, but many machine learning methods were typically limited to building an accurate prediction model as a “black box”, barely providing biological and clinical interpretability from the box. To address these challenges, we developed an interpretable and scalable machine learning model, ECMarker, to predict gene expression biomarkers for disease phenotypes and simultaneously reveal underlying regulatory mechanisms. Particularly, ECMarker is built on the integration of semi- and discriminative- restricted Boltzmann machines, a neural network model for classification allowing lateral connections at the input gene layer. This interpretable model is scalable without needing any prior feature selection and enables directly modeling and prioritizing genes and revealing potential gene networks (from lateral connections) for the phenotypes. With application to the gene expression data of non-small cell lung cancer (NSCLC) patients, we found that ECMarker not only achieved a relatively high accuracy for predicting cancer stages but also identified the biomarker genes and gene networks implying the regulatory mechanisms in the lung cancer development. Additionally, ECMarker demonstrates clinical interpretability as its prioritized biomarker genes can predict survival rates of early lung cancer patients (p-value < 0.005). Finally, we identified a number of drugs currently in clinical use for late stages or other cancers with effects on these early lung cancer biomarkers, suggesting potential novel candidates on early cancer medicine. ECMarker is open source as a general-purpose tool at https://github.com/daifengwanglab/ECMarker.

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