scholarly journals A Deep-Learning Approach toward Rational Molecular Docking Protocol Selection

Molecules ◽  
2020 ◽  
Vol 25 (11) ◽  
pp. 2487 ◽  
Author(s):  
José Jiménez-Luna ◽  
Alberto Cuzzolin ◽  
Giovanni Bolcato ◽  
Mattia Sturlese ◽  
Stefano Moro
Keyword(s):  
Machine Learning ◽  
Ligand Docking ◽  
Learning Approach ◽  
Small Compound ◽  
The Past ◽  
Machine Learning Model ◽  
Docking Protocol ◽  
Different Types ◽  
Protocol Selection ◽  
Fully Connected

While a plethora of different protein–ligand docking protocols have been developed over the past twenty years, their performances greatly depend on the provided input protein–ligand pair. In this study, we developed a machine-learning model that uses a combination of convolutional and fully connected neural networks for the task of predicting the performance of several popular docking protocols given a protein structure and a small compound. We also rigorously evaluated the performance of our model using a widely available database of protein–ligand complexes and different types of data splits. We further open-source all code related to this study so that potential users can make informed selections on which protocol is best suited for their particular protein–ligand pair.

scholarly journals A Deep-Learning Approach Toward Rational Molecular Docking Protocol Selection

2020 ◽  
Author(s):  
Jose Jimenez-Luna ◽  
Alberto Cuzzolin ◽  
Giovanni Bolcato ◽  
Mattia Sturlese ◽  
Stefano Moro
Keyword(s):  
Machine Learning ◽  
Ligand Docking ◽  
Learning Approach ◽  
Small Compound ◽  
The Past ◽  
Machine Learning Model ◽  
Docking Protocol ◽  
Different Types ◽  
Protocol Selection ◽  
Fully Connected

While a plethora of different protein-ligand docking protocols have been developed over the past twenty years, their performances greatly depend on the provided input protein-ligand pair. In this work we have developed a machine-learning model that uses a combination of convolutional and fully-connected neural networks for the task of predicting the performance of several popular docking protocols given a protein structure and a small compound. We also rigorously evaluate the performance of our model using a widely available database of protein-ligand complexes and different types of data splits. We further open-source all code related to this study so that potential users can make informed guesses on which protocol is best suited for their particular protein-ligand pair.

scholarly journals A Deep-Learning Approach Toward Rational Molecular Docking Protocol Selection

2020 ◽  
Author(s):  
Jose Jimenez-Luna ◽  
Alberto Cuzzolin ◽  
Giovanni Bolcato ◽  
Mattia Sturlese ◽  
Stefano Moro
Keyword(s):  
Machine Learning ◽  
Ligand Docking ◽  
Learning Approach ◽  
Small Compound ◽  
The Past ◽  
Machine Learning Model ◽  
Docking Protocol ◽  
Different Types ◽  
Protocol Selection ◽  
Fully Connected

While a plethora of different protein-ligand docking protocols have been developed over the past twenty years, their performances greatly depend on the provided input protein-ligand pair. In this work we have developed a machine-learning model that uses a combination of convolutional and fully-connected neural networks for the task of predicting the performance of several popular docking protocols given a protein structure and a small compound. We also rigorously evaluate the performance of our model using a widely available database of protein-ligand complexes and different types of data splits. We further open-source all code related to this study so that potential users can make informed guesses on which protocol is best suited for their particular protein-ligand pair.

scholarly journals Predicting Fraud Victimization Using Classical Machine Learning

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 300
Author(s):  
Mark Lokanan ◽  
Susan Liu
Keyword(s):  
Machine Learning ◽  
Financial Literacy ◽  
Learning Algorithm ◽  
Demographic Characteristics ◽  
Financial Knowledge ◽  
The Past ◽  
Machine Learning Model ◽  
Long Time ◽  
Regulatory Organization ◽  
Investment Fraud

Protecting financial consumers from investment fraud has been a recurring problem in Canada. The purpose of this paper is to predict the demographic characteristics of investors who are likely to be victims of investment fraud. Data for this paper came from the Investment Industry Regulatory Organization of Canada’s (IIROC) database between January of 2009 and December of 2019. In total, 4575 investors were coded as victims of investment fraud. The study employed a machine-learning algorithm to predict the probability of fraud victimization. The machine learning model deployed in this paper predicted the typical demographic profile of fraud victims as investors who classify as female, have poor financial knowledge, know the advisor from the past, and are retired. Investors who are characterized as having limited financial literacy but a long-time relationship with their advisor have reduced probabilities of being victimized. However, male investors with low or moderate-level investment knowledge were more likely to be preyed upon by their investment advisors. While not statistically significant, older adults, in general, are at greater risk of being victimized. The findings from this paper can be used by Canadian self-regulatory organizations and securities commissions to inform their investors’ protection mandates.

scholarly journals Machine Learning Based Behavioral Modification

2019 ◽  
Vol 8 (6S2) ◽  
pp. 1134-1138
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Behavior Analysis ◽  
Time Of Day ◽  
Computational Techniques ◽  
Behavioral Modification ◽  
Optimal Temperature ◽  
The Past ◽  
Different Types ◽  
Artificial Intelligence Technology

To build up a particular profile about a person, the study of examining the comportment is known as Behavior analysis. Initially the Behavior analysis is used in psychology and for suggesting and developing different types the application content for user then it developed in information technology. To make the applications for user's personal needs it becoming a new trends with the use of artificial intelligence (AI). in many applications like innovation to do everything from anticipating buy practices to altering a home's indoor regulator to the inhabitant's optimal temperature for a specific time of day use machine learning and artificial intelligence technology. The technique that is use to advance the rule proficiency that rely upon the past experience is known as machine learning. By utilizing the insights hypothesis it makes the numerical model, and its real work is to infer from the models gave. To take the information clearly from the data the methodology utilizes computational techniques.

scholarly journals Assessing the International Transferability of a Machine Learning Model for Detecting Medication Error in the General Internal Medicine Clinic: Multicenter Preliminary Validation Study

10.2196/23454 ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. e23454
Author(s):  
Yen Po Harvey Chin ◽  
Wenyu Song ◽  
Chia En Lien ◽  
Chang Ho Yoon ◽  
Wei-Chen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Medication Error ◽  
Model Performance ◽  
Learning Model ◽  
Study Cohort ◽  
Learning Approach ◽  
Hospital Data ◽  
General Internal ◽  
Machine Learning Model ◽  
Outpatient Prescriptions

Background Although most current medication error prevention systems are rule-based, these systems may result in alert fatigue because of poor accuracy. Previously, we had developed a machine learning (ML) model based on Taiwan’s local databases (TLD) to address this issue. However, the international transferability of this model is unclear. Objective This study examines the international transferability of a machine learning model for detecting medication errors and whether the federated learning approach could further improve the accuracy of the model. Methods The study cohort included 667,572 outpatient prescriptions from 2 large US academic medical centers. Our ML model was applied to build the original model (O model), the local model (L model), and the hybrid model (H model). The O model was built using the data of 1.34 billion outpatient prescriptions from TLD. A validation set with 8.98% (60,000/667,572) of the prescriptions was first randomly sampled, and the remaining 91.02% (607,572/667,572) of the prescriptions served as the local training set for the L model. With a federated learning approach, the H model used the association values with a higher frequency of co-occurrence among the O and L models. A testing set with 600 prescriptions was classified as substantiated and unsubstantiated by 2 independent physician reviewers and was then used to assess model performance. Results The interrater agreement was significant in terms of classifying prescriptions as substantiated and unsubstantiated (κ=0.91; 95% CI 0.88 to 0.95). With thresholds ranging from 0.5 to 1.5, the alert accuracy ranged from 75%-78% for the O model, 76%-78% for the L model, and 79%-85% for the H model. Conclusions Our ML model has good international transferability among US hospital data. Using the federated learning approach with local hospital data could further improve the accuracy of the model.

scholarly journals Redewiedergabe – Schritte zur automatischen Erkennung

2019 ◽  
Vol 47 (1) ◽  
pp. 216-248
Author(s):  
Annelen Brunner
Keyword(s):  
Machine Learning ◽  
Automatic Detection ◽  
Learning Approach ◽  
Manual Annotation ◽  
Narrative Texts ◽  
Rule Based ◽  
Machine Learning Approach ◽  
Different Types ◽  
Short Narrative ◽  
Over Time

Abstract This contribution presents a quantitative approach to speech, thought and writing representation (ST&WR) and steps towards its automatic detection. Automatic detection is necessary for studying ST&WR in a large number of texts and thus identifying developments in form and usage over time and in different types of texts. The contribution summarizes results of a pilot study: First, it describes the manual annotation of a corpus of short narrative texts in relation to linguistic descriptions of ST&WR. Then, two different techniques of automatic detection – a rule-based and a machine learning approach – are described and compared. Evaluation of the results shows success with automatic detection, especially for direct and indirect ST&WR.

scholarly journals Modeling of apartment prices in a Colombian context from a machine learning approach with stable-important attributes

DYNA ◽  
2020 ◽  
Vol 87 (212) ◽  
pp. 63-72
Author(s):  
Jorge Iván Pérez Rave ◽  
Favián González Echavarría ◽  
Juan Carlos Correa Morales
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Approach ◽  
Predictive Capability ◽  
Predictive Capacity ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Property Price ◽  
Object Of Study ◽  
Online Pricing

The objective of this work is to develop a machine learning model for online pricing of apartments in a Colombian context. This article addresses three aspects: i) it compares the predictive capacity of linear regression, regression trees, random forest and bagging; ii) it studies the effect of a group of text attributes on the predictive capability of the models; and iii) it identifies the more stable-important attributes and interprets them from an inferential perspective to better understand the object of study. The sample consists of 15,177 observations of real estate. The methods of assembly (random forest and bagging) show predictive superiority with respect to others. The attributes derived from the text had a significant relationship with the property price (on a log scale). However, their contribution to the predictive capacity was almost nil, since four different attributes achieved highly accurate predictions and remained stable when the sample change.

scholarly journals Retail Giant Sales Forecasting using Machine Learning

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2408-2411
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Making ◽  
Learning Model ◽  
Accurate Prediction ◽  
Machine Learning Model ◽  
Different Types ◽  
Using Data ◽  
Near Future ◽  
Sales Trend

Sales forecasting is widely recognized and plays a major role in an organization’s decision making. It is an integral part in business execution of retail giants, so that they can change their strategy to improve sales in the near future. This helps in better management of their resources like machine, money and manpower. Forecasting the sales will help in managing the revenue and inventory accordingly. This paper proposes a model that can forecast most profitable segments at granular level. As most retail giants have many branches in different locations, consolidation of sales are hard using data mining. Instead using machine learning model helps in getting reliable and accurate results. This paper helps in understanding the sales trend to monitor or predict future applicable on different types of sales patterns and products to produce accurate prediction results.

scholarly journals A model for estimating the value of the applied pressure based on the analysis of tactile sensor signals using machine learning methods

2021 ◽  
pp. 119-130
Author(s):  
П.С. Козырь ◽  
Р.Н. Яковлев
Keyword(s):  
Machine Learning ◽  
Polynomial Regression ◽  
Applied Pressure ◽  
Tactile Sensor ◽  
Least Squares Regression ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Machine Learning Model ◽  
Sensor Signals ◽  
Fully Connected

В рамках настоящего исследования был проведен анализ существующих работ, посвященных интерпретации показаний тактильных сенсорных устройств, по результатам которого была предложена модель машинного обучения, позволяющая осуществлять оценку величины приложенного давления к поверхности тактильного сенсора давления емкостного типа. В качестве опорных моделей обработки и интерпретации сигналов данного устройства в работе рассматривались несколько методов машинного обучения: линейная регрессия, полиномиальная регрессия, регрессия дерева решений, частичная регрессия наименьших квадратов и полносвязная нейронная сеть прямого распространения. Обучение опорных моделей и апробация конечного решения проводилась на авторском наборе данных, включающем в себя более 3000 экземпляров данных. Согласно полученным результатам, наилучшее качество определения величины приложенного давления продемонстрирован решением на основе полносвязной нейронной сети прямого распространения. Коэффициент детерминации и средний модуль отклонения для данного решения на тестовой выборке составили 0,93 и 13,14 кПа соответственно. Currently, in the field of developing sensing systems for robotic means, one of the urgent tasks is the problem of interpreting the data of tactile pressure and proximity sensors. As a rule, the solution to this problem is complicated both by the dependence of the indicators of tactile sensors on the type of object’s material and by the design features of each individual device. In this study, an analysis of existing works devoted to the interpretation of the readings of tactile sensor devices was carried out. According to the analysis results a machine learning model was proposed that allows estimating the amount of pressure applied to the surface of a tactile pressure sensor of a capacitive type. The architecture of the proposed model includes two key blocks of data analysis, the first one is aimed at recognizing the type of interaction object’s material and the second is devoted to the direct assessment of the magnitude of the pressure applied to the sensor. Several machine learning methods were considered as supporting models for processing and interpreting the signals of this device: linear regression, polynomial regression, decision tree regression, partial least squares regression and a fully connected feedforward neural network.

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