scholarly journals HybridDTA: Hybrid Data Fusion through Pairwise Training for Drug-Target Affinity Prediction

2021 ◽  
Author(s):  
Hongyu Luo ◽  
Yingfei Xiang ◽  
Xiaomin Fang ◽  
Wei Lin ◽  
Fan Wang ◽  
...  
Keyword(s):  
Drug Target ◽  
Drug Repurposing ◽  
Machine Learning Techniques ◽  
Superior Performance ◽  
Experimental Conditions ◽  
Training Paradigm ◽  
Data Inconsistency ◽  
Learning Techniques ◽  
Hybrid Data ◽  
Target Binding

Estimating drug-target binding affinity (DTA) is crucial for various tasks, including drug design, drug repurposing, and lead optimization. Advanced works adopt machine learning techniques, especially deep learning, to DTA estimation by utilizing the existing assay data. These powerful techniques make it possible to screen a massive amount of potential drugs with limited computation cost. However, a typical DNN-based training paradigm directly minimizes the distances between the estimated scores and the ground truths, suffering from the issue of data inconsistency. The data inconsistency caused by various measurements, e.g., Kd, Ki, and IC50, as well as experimental conditions, e.g., reactant concentration and temperature, severely hinders the effective utilization of existing data, thus deteriorating the performance of DTA prediction. We propose a novel paradigm for effective training on hybrid DTA data to alleviate the data inconsistency issue. Since the ranking orders of the affinity scores with respect to measurements and experimental batches are more consistent, we adopt a pairwise paradigm to enable the DNNs to learn from ranking orders instead. We expect this paradigm can effectively blend datasets with various measurements and experimental batches to achieve better performances. For the sake of verifying the proposed paradigm, we compare it with the previous paradigm for various model backbones on multiple DTA datasets. The experimental results demonstrate the superior performance of our proposed paradigm. The ablation studies also show the effectiveness of the design of the proposed training paradigm.

scholarly journals Feasibility of Machine Learning Algorithms for Predicting the Deformation of Anodic Titanium Films by Modulating Anodization Processes

Materials ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1089
Author(s):  
Sung-Hee Kim ◽  
Chanyoung Jeong
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Multiclass Classification ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Smart Manufacturing ◽  
Gradient Boosting ◽  
Experimental Conditions ◽  
Learning Techniques ◽  
Tio2 Nanostructures

This study aims to demonstrate the feasibility of applying eight machine learning algorithms to predict the classification of the surface characteristics of titanium oxide (TiO2) nanostructures with different anodization processes. We produced a total of 100 samples, and we assessed changes in TiO2 nanostructures’ thicknesses by performing anodization. We successfully grew TiO2 films with different thicknesses by one-step anodization in ethylene glycol containing NH4F and H2O at applied voltage differences ranging from 10 V to 100 V at various anodization durations. We found that the thicknesses of TiO2 nanostructures are dependent on anodization voltages under time differences. Therefore, we tested the feasibility of applying machine learning algorithms to predict the deformation of TiO2. As the characteristics of TiO2 changed based on the different experimental conditions, we classified its surface pore structure into two categories and four groups. For the classification based on granularity, we assessed layer creation, roughness, pore creation, and pore height. We applied eight machine learning techniques to predict classification for binary and multiclass classification. For binary classification, random forest and gradient boosting algorithm had relatively high performance. However, all eight algorithms had scores higher than 0.93, which signifies high prediction on estimating the presence of pore. In contrast, decision tree and three ensemble methods had a relatively higher performance for multiclass classification, with an accuracy rate greater than 0.79. The weakest algorithm used was k-nearest neighbors for both binary and multiclass classifications. We believe that these results show that we can apply machine learning techniques to predict surface quality improvement, leading to smart manufacturing technology to better control color appearance, super-hydrophobicity, super-hydrophilicity or batter efficiency.

scholarly journals Hybrid Data-Driven and Physics-Based Modeling for Gas Turbine Prescriptive Analytics

2020 ◽  
Vol 5 (4) ◽  
pp. 29
Author(s):  
Sergei Belov ◽  
Sergei Nikolaev ◽  
Ighor Uzhinsky
Keyword(s):  
Gas Turbine ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Flame Tube ◽  
Learning Techniques ◽  
Prescriptive Analytics ◽  
Hybrid Data ◽  
Data Driven Modeling ◽  
Engine Power

This paper presents a methodology for predictive and prescriptive analytics of a gas turbine. The methodology is based on a combination of physics-based and data-driven modeling using machine learning techniques. Combining these approaches results in a set of reliable, fast, and continuously updating models for prescriptive analytics. The methodology is demonstrated with a case study of a jet-engine power plant preventive maintenance and diagnosis of its flame tube. The developed approach allows not just to analyze and predict some problems in the combustion chamber, but also to identify a particular flame tube to be repaired or replaced and plan maintenance actions in advance.

scholarly journals Reaction Wheels Fault Isolation Onboard 3-Axis Controlled Satel-lite using Enhanced Random Forest with Multidomain Features

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Afshin Rahimi ◽  
Mofiyinoluwa O. Folami
Keyword(s):  
Machine Learning ◽  
Fault Isolation ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Superior Performance ◽  
Alternative Methods ◽  
Model Parameters ◽  
High Fidelity ◽  
Novel Approach ◽  
Learning Techniques

As the number of satellite launches increases each year, it is only natural that an interest in the safety and monitoring of these systems would increase as well. However, as a system becomes more complex, generating a high-fidelity model that accurately describes the system becomes complicated. Therefore, imploring a data-driven method can provide to be more beneficial for such applications. This research proposes a novel approach for data-driven machine learning techniques on the detection and isolation of nonlinear systems, with a case-study for an in-orbit closed loop-controlled satellite with reaction wheels as actuators. High-fidelity models of the 3-axis controlled satellite are employed to generate data for both nominal and faulty conditions of the reaction wheels. The generated simulation data is used as input for the isolation method, after which the data is pre-processed through feature extraction from a temporal, statistical, and spectral domain. The pre-processed features are then fed into various machine learning classifiers. Isolation results are validated with cross-validation, and model parameters are tuned using hyperparameter optimization. To validate the robustness of the proposed method, it is tested on three characterized datasets and three reaction wheel configurations, including standard four-wheel, three-orthogonal, and pyramid. The results prove superior performance isolation accuracy for the system under study compared to previous studies using alternative methods (Rahimi & Saadat, 2019, 2020).

scholarly journals Eye movements and human face perception: An holistic analysis and proficiency classification based on frontal 2D face images

2018 ◽  
Author(s):  
Victor P. L. Varela ◽  
Estela Ribeiro ◽  
Pedro A. S. S. Orona ◽  
Carlos E. Thomaz
Keyword(s):  
Eye Movements ◽  
Cognitive Task ◽  
Machine Learning Techniques ◽  
Superior Performance ◽  
Emotional States ◽  
Face Images ◽  
Benchmark Tests ◽  
Learning Techniques ◽  
Proficiency Classification ◽  
Human Faces

Human faces convey a collection of information, such as gender, identity, and emotional states. Therefore, understanding the differences between volunteers’ eye movements on benchmark tests of face recognition and perception can explicitly indicate the most discriminating regions to improve performance in this visual cognitive task. The aim of this work is to qualify and classify these eye strategies using multivariate statistics and machine learning techniques, achieving up to 94.8% accuracy. Our experimental results show that volunteers have focused their visual attention, on average, at the eyes, but those with superior performance in the tests carried out have looked at the nose region more closely.

Image Processing and Machine Learning Techniques for the Segmentation of cDNA Microarray Images

2012 ◽  
pp. 817-829
Author(s):  
Nikolaos Giannakeas ◽  
Dimitrios I. Fotiadis
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Expression Profiles ◽  
Machine Learning Techniques ◽  
Experimental Conditions ◽  
Microarray Image ◽  
Learning Techniques ◽  
Common Substrate ◽  
Processing Techniques ◽  
Comprehensive Measurement

Microarray technology allows the comprehensive measurement of the expression level of many genes simultaneously on a common substrate. Typical applications of microarrays include the quantification of expression profiles of a system under different experimental conditions, or expression profile comparisons of two systems for one or more conditions. Microarray image analysis is a crucial step in the analysis of microarray data. In this chapter an extensive overview of the segmentation of the microarray image is presented. Methods already presented in the literature are classified into two main categories:methods which are based on image processing techniques and those which are based on Machine learning techniques. A novel classification-based application for the segmentation is also presented to demonstrate efficiency.

scholarly journals Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques

2019 ◽  
Author(s):  
Reed D. Gurchiek ◽  
Nicholas Cheney ◽  
Ryan S. McGinnis
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Superior Performance ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Practical Implementation ◽  
Learning Techniques

Wearable sensors have the potential to enable comprehensive patient characterization and optimized clinical intervention. Critical to realizing this vision is accurate estimation of biomechanical time-series in daily-life, including joint, segment, and muscle kinetics and kinematics, from wearable sensor data. The use of physical models for estimation of these quantities often requires many wearable devices making practical implementation more difficult. However, regression techniques may provide a viable alternative by allowing the use of a reduced number of sensors for estimating biomechanical time-series. Herein, we review 46 articles that used regression algorithms to estimate joint, segment, and muscle kinematics and kinetics. We present a high-level comparison of the many different techniques identified and discuss the implications of our findings concerning practical implementation and further improving estimation accuracy. In particular, we found that several studies report the incorporation of domain knowledge often yielded superior performance. Further, most models were trained on small datasets in which case nonparametric regression often performed best. No models were open-sourced, and most were subject-specific and not validated on impaired populations. Future research should focus on developing open-source algorithms using complementary physics-based and machine learning techniques that are validated in clinically impaired populations. This approach may further improve estimation performance and reduce barriers to clinical adoption.

Single-Trial EEG Responses Classified Using Latency Features

2020 ◽  
Vol 30 (06) ◽  
pp. 2050033
Author(s):  
Irzam Hardiansyah ◽  
Valentina Pergher ◽  
Marc M. Van Hulle
Keyword(s):  
Classification Accuracy ◽  
Brain Plasticity ◽  
Covert Attention ◽  
Machine Learning Techniques ◽  
Attention Training ◽  
Single Trial ◽  
Experimental Conditions ◽  
Repeated Practice ◽  
Learning Techniques ◽  
Older Subjects

Covert attention has been repeatedly shown to impact on EEG responses after single and repeated practice sessions. Machine learning techniques are increasingly adopted to classify single-trial EEG responses thereby primarily relying on amplitude-based features instead of latency-based features. In this study, we investigated changes in EEG response signatures of nine healthy older subjects when performing 10 sessions of covert attention training. We show that, when we trained classifiers to distinguish recorded EEG patterns between the two experimental conditions (a target stimulus is “present” or “not present”), latency-based classifiers outperform the amplitude-based ones and that classification accuracy improved along with behavioral accuracy, providing supportive evidence of brain plasticity.

scholarly journals Transformation of Drug Discovery towards Artificial Intelligence: An in Silico Approach

2021 ◽  
Author(s):  
Ruby Srivastava
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Drug Discovery ◽  
In Silico ◽  
Drug Repurposing ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Modern Drug ◽  
Short Period

Computational methods play a key role in the design of therapeutically important molecules for modern drug development. With these “in silico” approaches, machines are learning and offering solutions to some of the most complex drug related problems and has well positioned them as a next frontier for potential breakthrough in drug discovery. Machine learning (ML) methods are used to predict compounds with pharmacological activity, specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties to evaluate the drugs and their various applications. Modern artificial intelligence (AI) has the capacity to significantly enhance the role of computational methodology in drug discovery. Use of AI in drug discovery and development, drug repurposing, improving pharmaceutical productivity, and clinical trials will certainly reduce the human workload as well as achieving targets in a short period of time. This chapter elaborates the crosstalk between the machine learning techniques, computational tools and the future of AI in the pharmaceutical industry.

scholarly journals Hybrid Data-Driven and Physics-Based Modelling for Gas-Turbine Prescriptive Analytics

2020 ◽  
Author(s):  
Sergei Belov ◽  
Sergei Nikolaev ◽  
Ighor Uzhinsky
Keyword(s):  
Data Driven ◽  
Machine Learning Techniques ◽  
Engineering Systems ◽  
Learning Techniques ◽  
Prescriptive Analytics ◽  
Hybrid Data ◽  
Complex Engineering ◽  
Data Driven Modeling ◽  
Complex Engineering Systems

This paper presents a methodology for predictive and prescriptive analytics of complex engineering systems. The methodology is based on a combination of physics-based and data-driven modeling using machine learning techniques. Combining these approaches results in a set of reliable, fast, and continuously updating models for prescriptive analytics. The methodology is demonstrated with a case study of a jet-engine power plant preventive maintenance and diagnostics of its flame tube.

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