scholarly journals Design and Development of Lubricating Material Database and Research on Performance Prediction Method of Machine Learning

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Dan Jia ◽  
Haitao Duan ◽  
Shengpeng Zhan ◽  
Yongliang Jin ◽  
Bingxue Cheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Prediction Method ◽  
Lubricating Oil ◽  
Physical Parameters ◽  
First Principle ◽  
Vast Number ◽  
First Principle Calculation ◽  
Lubricating Material ◽  
Machine Learning Model

AbstractLong developing period and cumbersome evaluation for the lubricating materials performance seriously jeopardize the successful development and application of any database system in tribological field. Such major setback can be solved effectively by implementing approaches with high throughput calculation. However, it often involves with vast number of output files, which are computed on the basis of first principle computation, having different data format from that of their experimental counterparts. Commonly, the input, storage and management of first principle calculation files and their individually test counterparts, implementing fast query and display in the database, adding to the use of physical parameters, as predicted with the performance estimated by first principle approach, may solve such setbacks. Investigation is thus performed for establishing database website specifically for lubricating materials, which satisfies both data: (i) as calculated on the basis of first principles and (ii) as obtained by practical experiment. It further explores preliminarily the likely relationship between calculated physical parameters of lubricating oil and its respectively tribological and anti-oxidative performance as predicted by lubricant machine learning model. Success of the method facilitates in instructing the obtainment of optimal design, preparation and application for any new lubricating material so that accomplishment of high performance is possible.

scholarly journals DeepGOZero: Improving protein function prediction from sequence and zero-shot learning based on ontology axioms

2022 ◽  
Author(s):  
Maxat Kulmanov ◽  
Robert Hoehndorf
Keyword(s):  
Machine Learning ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Prediction Method ◽  
Function Prediction ◽  
Training Data ◽  
Large Set ◽  
Theoretic Approach ◽  
Machine Learning Model ◽  
Protein Functions

Motivation: Protein functions are often described using the Gene Ontology (GO) which is an ontology consisting of over 50,000 classes and a large set of formal axioms. Predicting the functions of proteins is one of the key challenges in computational biology and a variety of machine learning methods have been developed for this purpose. However, these methods usually require significant amount of training data and cannot make predictions for GO classes which have only few or no experimental annotations. Results: We developed DeepGOZero, a machine learning model which improves predictions for functions with no or only a small number of annotations. To achieve this goal, we rely on a model-theoretic approach for learning ontology embeddings and combine it with neural networks for protein function prediction. DeepGOZero can exploit formal axioms in the GO to make zero-shot predictions, i.e., predict protein functions even if not a single protein in the training phase was associated with that function. Furthermore, the zero-shot prediction method employed by DeepGOZero is generic and can be applied whenever associations with ontology classes need to be predicted. Availability: http://github.com/bio-ontology-research-group/deepgozero

scholarly journals A Geno-Clinical Decision Model for the Diagnosis of Myelodysplastic Syndromes

2021 ◽  
Author(s):  
Nathan Radakovich ◽  
Manja Meggendorfer ◽  
Luca Malcovati ◽  
C Beau Hilton ◽  
Mikkael A Sekeres ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Myeloid Malignancies ◽  
Machine Learning Model ◽  
Inter Observer Variability ◽  
Genomic Relationships ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data

The differential diagnosis of myeloid malignancies is challenging and subject to inter-observer variability. We used clinical and next-generation sequencing (NGS) data to develop a machine learning model for the diagnosis of myeloid malignancies independent of bone marrow biopsy data based on a three institution, international cohort of patients. The model achieves high performance, with model interpretations indicating that it relies on factors similar to those used by clinicians. Additionally, we describe associations between NGS findings and clinically important phenotypes, and introduce the use of machine learning algorithms to elucidate clinico-genomic relationships.

scholarly journals Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers

2021 ◽  
Vol 11 (6) ◽  
Author(s):  
Agastya P. Bhati ◽  
Shunzhou Wan ◽  
Dario Alfè ◽  
Austin R. Clyde ◽  
Mathis Bode ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
High Performance ◽  
Large Scale ◽  
Antiviral Drug ◽  
Linear Accelerators ◽  
Vast Number ◽  
Global Pandemic ◽  
Major Bottleneck ◽  
High Performance Computers

The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case, developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers.

Single tungsten atom supported on N-doped graphyne as a high-performance electrocatalyst for nitrogen fixation under ambient conditions

2019 ◽  
Vol 21 (3) ◽  
pp. 1546-1551 ◽  
Author(s):  
Tianwei He ◽  
Sri Kasi Matta ◽  
Aijun Du
Keyword(s):  
Nitrogen Fixation ◽  
High Performance ◽  
First Principle ◽  
Ambient Conditions ◽  
Tungsten Atom ◽  
Principle Calculation ◽  
First Principle Calculation ◽  
Highly Efficient

A promising highly efficient and inexpensive W@N-doped graphyne electrocatalyst for N2 fixation has been predicted by first-principle calculation.

scholarly journals Improving Accuracy for Diabetes Mellitus Prediction by Using Deepnet

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Riyad Alshammari ◽  
Noorah Atiyah ◽  
Tahani Daghistani ◽  
Abdulwahhab Alshammari
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
National Guard ◽  
Learning Algorithms ◽  
Evaluation Criteria ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Diabetic Patients ◽  
Data Sets ◽  
Machine Learning Model

Diabetes is a salient issue and a significant health care concern for many nations. The forecast for the prevalence of diabetes is on the rise. Hence, building a prediction machine learning model to assist in the identification of diabetic patients is of great interest. This study aims to create a machine learning model that is capable of predicting diabetes with high performance. The following study used the BigML platform to train four machine learning algorithms, namely, Deepnet, Models (decision tree), Ensemble and Logistic Regression, on data sets collected from the Ministry of National Guard Hospital Affairs (MNGHA) in Saudi Arabia between the years of 2013 and 2015. The comparative evaluation criteria for the four algorithms examined included; Accuracy, Precision, Recall, F-measure and PhiCoefficient. Results show that the Deepnet algorithm achieved higher performance compared to other machine learning algorithms based on various evaluation matrices.

scholarly journals Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Hao Li ◽  
Zhijian Liu ◽  
Kejun Liu ◽  
Zhien Zhang
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Potential Application ◽  
High Throughput Screening ◽  
High Performance ◽  
Predictive Power ◽  
Prediction Method ◽  
Solar Water Heater ◽  
Water Heater ◽  
Solar Water

Predicting the performance of solar water heater (SWH) is challenging due to the complexity of the system. Fortunately, knowledge-based machine learning can provide a fast and precise prediction method for SWH performance. With the predictive power of machine learning models, we can further solve a more challenging question: how to cost-effectively design a high-performance SWH? Here, we summarize our recent studies and propose a general framework of SWH design using a machine learning-based high-throughput screening (HTS) method. Design of water-in-glass evacuated tube solar water heater (WGET-SWH) is selected as a case study to show the potential application of machine learning-based HTS to the design and optimization of solar energy systems.

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