scholarly journals Modeling Motivation for Alcohol in Humans using Traditional and Machine Learning Approaches

2020 ◽  
Author(s):  
Erica Grodin ◽  
Amanda Kay Montoya ◽  
Spencer Bujarski ◽  
Lara A. Ray
Keyword(s):  
Machine Learning ◽  
A Priori ◽  
Progressive Ratio ◽  
Data Driven ◽  
Learning Approaches ◽  
Self Administration ◽  
Breath Alcohol ◽  
Data Driven Approach ◽  
Preclinical And Clinical Studies ◽  
Alcohol Seeking

Given the significant cost of alcohol use disorder, identifying risk factors for alcohol seeking represents a research priority. Prominent addiction theories emphasize the role of motivation in the alcohol seeking process, which has largely been studied using preclinical models. In order to bridge the gap between preclinical and clinical studies, this study examined predictors of motivation for alcohol self-administration using a novel paradigm. Heavy drinkers (n=67) completed an alcohol infusion consisting of an alcohol challenge (target breath alcohol = 60mg%) and a progressive-ratio alcohol self-administration paradigm (maximum breath alcohol 120mg%; ratio requirements range = 20-3,139 response). Growth curve modeling was used to predict breath alcohol trajectories during alcohol self-administration. K-means clustering was used to identify motivated (n=41) and unmotivated (n=26) self-administration trajectories. The data was analyzed using two approaches: a theory-driven test of a-priori predictors and a data-driven, machine learning model. In both approaches, steeper delay discounting, indicating a preference for smaller, sooner rewards, predicted motivated alcohol seeking. The data-driven approach further identified phasic alcohol craving as a predictor of motivated alcohol self-administration. Additional application of this model to AUD translational science and treatment development appear warranted.

scholarly journals Predicting clinical outcomes in the Machine Learning era: The Piacenza score a purely data driven approach for mortality prediction in COVID-19 Pneumonia (Preprint)

2021 ◽  
Author(s):  
Geza Halasz ◽  
Michela Sperti ◽  
Matteo Villani ◽  
Umberto Michelucci ◽  
Piergiuseppe Agostoni ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Outcomes ◽  
Naive Bayes ◽  
Validation Cohort ◽  
A Priori ◽  
Naïve Bayes ◽  
Data Driven ◽  
Brier Score ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier

BACKGROUND Several models have been developed to predict mortality in patients with Covid-19 pneumonia, but only few have demonstrated enough discriminatory capacity. Machine-learning algorithms represent a novel approach for data-driven prediction of clinical outcomes with advantages over statistical modelling. OBJECTIVE To developed the Piacenza score, a Machine-learning based score, to predict 30-day mortality in patients with Covid-19 pneumonia METHODS The study comprised 852 patients with COVID-19 pneumonia, admitted to the Guglielmo da Saliceto Hospital (Italy) from February to November 2020. The patients’ medical history, demographic and clinical data were collected in an electronic health records. The overall patient dataset was randomly splitted into derivation and test cohort. The score was obtained through the Naïve Bayes classifier and externally validated on 86 patients admitted to Centro Cardiologico Monzino (Italy) in February 2020. Using a forward-search algorithm six features were identified: age; mean corpuscular haemoglobin concentration; PaO2/FiO2 ratio; temperature; previous stroke; gender. The Brier index was used to evaluate the ability of ML to stratify and predict observed outcomes. A user-friendly web site available at (https://covid.7hc.tech.) was designed and developed to enable a fast and easy use of the tool by the final user (i.e., the physician). Regarding the customization properties to the Piacenza score, we added a personalized version of the algorithm inside the website, which enables an optimized computation of the mortality risk score for a single patient, when some variables used by the Piacenza score are not available. In this case, the Naïve Bayes classifier is re-trained over the same derivation cohort but using a different set of patient’s characteristics. We also compared the Piacenza score with the 4C score and with a Naïve Bayes algorithm with 14 features chosen a-priori. RESULTS The Piacenza score showed an AUC of 0.78(95% CI 0.74-0.84 Brier-score 0.19) in the internal validation cohort and 0.79(95% CI 0.68-0.89, Brier-score 0.16) in the external validation cohort showing a comparable accuracy respect to the 4C score and to the Naïve Bayes model with a-priori chosen features, which achieved an AUC of 0.78(95% CI 0.73-0.83, Brier-score 0.26) and 0.80(95% CI 0.75-0.86, Brier-score 0.17) respectively. CONCLUSIONS A personalized Machine-learning based score with a purely data driven features selection is feasible and effective to predict mortality in patients with COVID-19 pneumonia.

A note on no-free-lunch theorem

2021 ◽  
Author(s):  
Lidong Wu
Keyword(s):  
Machine Learning ◽  
Theoretical Result ◽  
Data Driven ◽  
Np Hard ◽  
Free Lunch ◽  
No Free Lunch Theorem ◽  
Data Driven Approach ◽  
Hard Problems ◽  
No Free Lunch ◽  
Np Hard Problems

The No-Free-Lunch theorem is an interesting and important theoretical result in machine learning. Based on philosophy of No-Free-Lunch theorem, we discuss extensively on the limitation of a data-driven approach in solving NP-hard problems.

scholarly journals Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
M. Withnall ◽  
E. Lindelöf ◽  
O. Engkvist ◽  
H. Chen
Keyword(s):  
Machine Learning ◽  
Message Passing ◽  
A Priori ◽  
Molecular Graph ◽  
Model Performance ◽  
Learning Approaches ◽  
Property Prediction ◽  
Physical Chemical ◽  
Chemical Descriptor ◽  
Hyperparameter Selection

AbstractNeural Message Passing for graphs is a promising and relatively recent approach for applying Machine Learning to networked data. As molecules can be described intrinsically as a molecular graph, it makes sense to apply these techniques to improve molecular property prediction in the field of cheminformatics. We introduce Attention and Edge Memory schemes to the existing message passing neural network framework, and benchmark our approaches against eight different physical–chemical and bioactivity datasets from the literature. We remove the need to introduce a priori knowledge of the task and chemical descriptor calculation by using only fundamental graph-derived properties. Our results consistently perform on-par with other state-of-the-art machine learning approaches, and set a new standard on sparse multi-task virtual screening targets. We also investigate model performance as a function of dataset preprocessing, and make some suggestions regarding hyperparameter selection.

scholarly journals Data-driven studies of magnetic two-dimensional materials

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Trevor David Rhone ◽  
Wei Chen ◽  
Shaan Desai ◽  
Steven B. Torrisi ◽  
Daniel T. Larson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dft Calculations ◽  
Density Functional ◽  
Magnetic Coupling ◽  
Magnetic Ordering ◽  
Magnetic Behavior ◽  
Data Driven ◽  
Two Dimensional ◽  
Computationally Efficient ◽  
Data Driven Approach

Abstract We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form $$\hbox {A}_2\hbox {B}_2\hbox {X}_6$$ A 2 B 2 X 6 , based on the known material $$\hbox {Cr}_2\hbox {Ge}_2\hbox {Te}_6$$ Cr 2 Ge 2 Te 6 , using density functional theory (DFT) calculations and machine learning methods to determine their magnetic properties, such as magnetic order and magnetic moment. We also examine formation energies and use them as a proxy for chemical stability. We show that machine learning tools, combined with DFT calculations, can provide a computationally efficient means to predict properties of such two-dimensional (2D) magnetic materials. Our data analytics approach provides insights into the microscopic origins of magnetic ordering in these systems. For instance, we find that the X site strongly affects the magnetic coupling between neighboring A sites, which drives the magnetic ordering. Our approach opens new ways for rapid discovery of chemically stable vdW materials that exhibit magnetic behavior.

A Data-Driven Approach using Machine Learning to Enable Real-Time Flight Path Planning

2020 ◽  
Author(s):  
Jung-Hyun Kim ◽  
Simon I. Briceno ◽  
Cedric Y. Justin ◽  
Dimitri Mavris
Keyword(s):  
Machine Learning ◽  
Path Planning ◽  
Real Time ◽  
Flight Path ◽  
Data Driven ◽  
Data Driven Approach

scholarly journals Text Classification and Tagging of United States Army Ground Vehicle Fault Descriptions in Support of Data-Driven Prognostics

2020 ◽  
Vol 12 (1) ◽  
pp. 8
Author(s):  
Brandon Hansen ◽  
Cody Coleman ◽  
Yi Zhang ◽  
Maria Seale
Keyword(s):  
Machine Learning ◽  
Natural Language ◽  
A Priori ◽  
Supervised Machine Learning ◽  
Data Driven ◽  
A Priori Knowledge ◽  
Data Sets ◽  
Ground Vehicle ◽  
Us Army ◽  
Priori Knowledge

The manner in which a prognostics problem is framed is critical for enabling its solution by the proper method. Recently, data-driven prognostics techniques have demonstrated enormous potential when used alone, or as part of a hybrid solution in conjunction with physics-based models. Historical maintenance data constitutes a critical element for the use of a data-driven approach to prognostics, such as supervised machine learning. The historical data is used to create training and testing data sets to develop the machine learning model. Categorical classes for prediction are required for machine learning methods; however, faults of interest in US Army Ground Vehicle Maintenance Records appear as natural language text descriptions rather than a finite set of discrete labels. Transforming linguistically complex data into a set of prognostics classes is necessary for utilizing supervised machine learning approaches for prognostics. Manually labeling fault description instances is effective, but extremely time-consuming; thus, an automated approach to labelling is preferred. The approach described in this paper examines key aspects of the fault text relevant to enabling automatic labeling. A method was developed based on the hypothesis that a given fault description could be generalized into a category. This method uses various natural language processing (NLP) techniques and a priori knowledge of ground vehicle faults to assign classes to the maintenance fault descriptions. The core component of the method used in this paper is a Word2Vec word-embedding model. Word embeddings are used in conjunction with a token-oriented rule-based data structure for document classification. This methodology tags text with user-provided classes using a corpus of similar text fields as its training set. With classes of faults reliably assigned to a given description, supervised machine learning with these classes can be applied using related maintenance information that preceded the fault. This method was developed for labeling US Army Ground Vehicle Maintenance Records, but is general enough to be applied to any natural language data sets accompanied with a priori knowledge of its contents for consistent labeling. In addition to applications in machine learning, generated labels are also conducive to general summarization and case-by-case analysis of faults. The maintenance components of interest for this current application are alternators and gaskets, with future development directed towards determining the RUL of these components based on the labeled data.

scholarly journals Reconfiguring primase DNA-recognition sequences by using a data-driven approach

2020 ◽  
Author(s):  
Adam Soffer ◽  
Morya Ifrach ◽  
Stefan Ilic ◽  
Ariel Afek ◽  
Dan Vilenchik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protein Interactions ◽  
Dna Sequences ◽  
Metabolic Pathways ◽  
Dna Recognition ◽  
Model System ◽  
Data Driven ◽  
Specific Interactions ◽  
Data Driven Approach ◽  
Dna Protein Interactions

AbstractDNA–protein interactions are essential in all aspects of every living cell. Understanding of how features embedded in the DNA sequence affect specific interactions with proteins is challenging but important, since it may contribute to finding the means to regulate metabolic pathways involving DNA–protein interactions. Using a massive experimental benchmark dataset of binding scores for DNA sequences and a machine learning workflow, we describe the binding to DNA of T7 primase, as a model system for specific DNA–protein interactions. Effective binding of T7 primase to its specific DNA recognition se-quences triggers the formation of RNA primers that serve as Okazaki fragment start sites during DNA replication.

scholarly journals Predicting clinical outcomes in the Machine Learning era: The Piacenza score a purely data driven approach for mortality prediction in COVID-19 Pneumonia

2021 ◽  
Author(s):  
geza halasz ◽  
Michela Sperti ◽  
Matteo Villani ◽  
Umberto Michelucci ◽  
Piergiuseppe Agostoni ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Outcomes ◽  
Naive Bayes ◽  
Validation Cohort ◽  
A Priori ◽  
Haemoglobin Concentration ◽  
Naïve Bayes ◽  
Data Driven ◽  
Brier Score ◽  
Features Selection

Background Several models have been developed to predict mortality in patients with COVID-19 pneumonia, but only few have demonstrated enough discriminatory capacity. Machine-learning(ML) algorithms represent a novel approach for data-driven prediction of clinical outcomes with advantages over statistical modelling. We developed the Piacenza score, a ML-based score, to predict 30-day mortality in patients with COVID-19 pneumonia. Methods 852 patients (mean age 70years, 70%males) were enrolled from February to November 2020. The dataset was randomly splitted into derivation and test. The Piacenza score was obtained through the Naive Bayes classifier and externally validated on 86 patients. Using a forward-search algorithm the following six features were identified: age; mean corpuscular haemoglobin concentration; PaO2/FiO2 ratio; temperature; previous stroke; gender. In case one or more of the features are not available for a patient, the model can be re-trained using only the provided features. We also compared the Piacenza score with the 4C score and with a Naive Bayes algorithm with 14 variables chosen a-priori. Results The Piacenza score showed an AUC of 0.78(95% CI 0.74-0.84, Brier-score 0.19) in the internal validation cohort and 0.79(95% CI 0.68-0.89, Brier-score 0.16) in the external validation cohort showing a comparable accuracy respect to the 4C score and to the Naive Bayes model with a-priori chosen features, which achieved an AUC of 0.78(95% CI 0.73-0.83, Brier-score 0.26) and 0.80(95% CI 0.75-0.86, Brier-score 0.17) respectively. Conclusion A personalized ML-based score with a purely data driven features selection is feasible and effective to predict mortality in patients with COVID-19 pneumonia.

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