Maximizing Efficiency of Artificial Intelligence‐Driven Drug Combination Optimization through Minimal Resolution Experimental Design

2019 ◽  
Vol 3 (4) ◽  
pp. 1900122 ◽  
Author(s):  
Jhin Jieh Lim ◽  
Jasmine Goh ◽  
Masturah Bte Mohd Abdul Rashid ◽  
Edward Kai‐Hua Chow
Keyword(s):  
Artificial Intelligence ◽  
Experimental Design ◽  
Drug Combination ◽  
Minimal Resolution ◽  
Combination Optimization

scholarly journals Preventive and Curative Personality Profiling Based on EEG, ERP, and Big Five Personality Traits: A Literature Review

2020 ◽  
Vol 17 (2) ◽  
pp. 531-545
Author(s):  
Cut Amalia Saffiera ◽  
Raini Hassan ◽  
Amelia Ritahani Ismail
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Experimental Design ◽  
Personality Traits ◽  
Big Five ◽  
Healthy Lifestyle ◽  
Big Five Personality Traits ◽  
Event Related Potential ◽  
Big Five Personality ◽  
Electrophysiological Signals

Healthy lifestyle is a significant factor that impacts on the budget for medicine. According to psychological studies, personality traits based on the Big Five personality traits especially the neuroticism and conscientiousness, have the ability to predict healthy lifestyle profiling. Electrophysiological signals have been used to explore the nature of individual differences and personality that are related to perception. In this paper, we reviewed studies examining healthy lifestyle profile i.e., preventive and curative using electroencephalography (EEG) and event-related potential (ERP) signals. This study proposed a general experimental model by reviewing the literature to build suitable experimental design for implementing artificial intelligence techniques based on the machine learning.

A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence

Chemosphere ◽  
2018 ◽  
Vol 200 ◽  
pp. 330-343 ◽  
Author(s):  
Mingyi Fan ◽  
Jiwei Hu ◽  
Rensheng Cao ◽  
Wenqian Ruan ◽  
Xionghui Wei
Keyword(s):  
Artificial Intelligence ◽  
Water Treatment ◽  
Experimental Design ◽  
Pollutants Removal

scholarly journals Domain-General Tutor Authoring with Apprentice Learner Models

2020 ◽  
Author(s):  
Christopher J. MacLellan ◽  
Kenneth R. Koedinger
Keyword(s):  
Artificial Intelligence ◽  
Experimental Design ◽  
Prior Knowledge ◽  
Computational Models ◽  
Tree Model ◽  
Tutoring Systems ◽  
Interactive Machine Learning ◽  
Expert Model ◽  
Wide Range ◽  
Artificial Intelligence In Education

Abstract Intelligent tutoring systems are effective for improving students’ learning outcomes (Pane et al. 2013; Koedinger and Anderson, International Journal of Artificial Intelligence in Education, 8, 1–14, 1997; Bowen et al. Journal of Policy Analysis and Management, 1, 94–111 2013). However, constructing tutoring systems that are pedagogically effective has been widely recognized as a challenging problem (Murray 2003; Murray, International Journal of Artificial Intelligence in Education, 10, 98–129, 1999). In this paper, we explore the use of computational models of apprentice learning, or computer models that learn interactively from examples and feedback, for authoring expert-models via demonstrations and feedback (Matsuda et al. International Journal of Artificial Intelligence in Education, 25(1), 1–34 2014) across a wide range of domains. To support these investigations, we present the Apprentice Learner Architecture, which posits the types of knowledge, performance, and learning components needed for apprentice learning. We use this architecture to create two models: the Decision Tree model, which non-incrementally learns skills, and the Trestle model, which instead learns incrementally. Both models draw on the same small set of prior knowledge (six operators and three types of relational knowledge) to support expert model authoring. Despite their limited prior knowledge, we demonstrate their use for efficiently authoring a novel experimental design tutor and show that they are capable of learning an expert model for seven additional tutoring systems that teach a wide range of knowledge types (associations, categories, and skills) across multiple domains (language, math, engineering, and science). This work shows that apprentice learner models are efficient for authoring tutors that would be difficult to build with existing non-programmer authoring approaches (e.g., experimental design or stoichiometry tutors). Further, we show that these models can be applied to author tutors across eight tutor domains even though they only have a small, fixed set of prior knowledge. This work lays the foundation for new interactive machine-learning based authoring paradigms that empower teachers and other non-programmers to build pedagogically effective educational technologies at scale.

scholarly journals Rekonfigurasi Jaringan Distribusi Radial Menggunakan Modified Firefly Algorithms (MFA) Pada Penyulang Tanjung Rayon Jombang

2017 ◽  
Vol 1 (2) ◽  
pp. 13
Author(s):  
Hidayatul Nurohmah ◽  
Agus Raikhani ◽  
Machrus Ali
Keyword(s):  
Artificial Intelligence ◽  
Power Loss ◽  
Electrical Energy ◽  
Network Reconfiguration ◽  
Power Losses ◽  
Large Power ◽  
Artificial Intelligent ◽  
Combination Optimization ◽  
Long Time ◽  
Opening And Closing

Abstract -  Reconfiguring a distribution network is necessary to reduce power loss and increase system reliability.Different distribution forms will affect the large power losses so that it is necessary to reset the network configuration.Reconfiguration is done by opening and closing switches on the best distribution network.The amount of feeder and bus on the network will be difficult and require a very long time if calculated manually.The repeater of Tanjung Rayon Jombang consists of 41 Buses and 44 feeders.Therefore it is necessary to solve the problem by using artificial intelligence or Artificial Intelligent (AI).Firefly Algorithms (FA) widely used research in solving the optimization problem.Modified Firefly Algorithms (MFA) is an FA modification designed to solve discrete combination optimization problems.MFAs can search for the best network reconfiguration so that it can reduce 12,0866 kWatt or 12,6881% in Cape repeater.With the end voltage before reconfiguration 0.92959 pu to 0.94072 pu.This method can later use other artificial intelligence or can be applied to other repeater, thus reducing the losses of electrical energy.

Application of machine/statistical learning, artificial intelligence and statistical experimental design for the modeling and optimization of methylene blue and Cd(ii) removal from a binary aqueous solution by natural walnut carbon

2017 ◽  
Vol 19 (18) ◽  
pp. 11299-11317 ◽  
Author(s):  
H. Mazaheri ◽  
M. Ghaedi ◽  
M. H. Ahmadi Azqhandi ◽  
A. Asfaram
Keyword(s):  
Artificial Intelligence ◽  
Aqueous Solution ◽  
Methylene Blue ◽  
Experimental Design ◽  
Aqueous Medium ◽  
Statistical Learning ◽  
Blue Dye ◽  
Statistical Experimental Design ◽  
Methylene Blue Dye ◽  
Novel Model

We developed and constructed a novel model that could make reliable predictions on the adsorption of methylene blue dye and Cd2+ions from an aqueous medium.

Experimental design and statistical analysis for three-drug combination studies

2015 ◽  
Vol 26 (3) ◽  
pp. 1261-1280 ◽  
Author(s):  
Hong-Bin Fang ◽  
Xuerong Chen ◽  
Xin-Yan Pei ◽  
Steven Grant ◽  
Ming Tan
Keyword(s):  
Experimental Design ◽  
Dose Response ◽  
Drug Combination ◽  
Mathematical Reasoning ◽  
Experimental Studies ◽  
Drug Combinations ◽  
Dose Finding ◽  
Nonparametric Model ◽  
Response Curves ◽  
Interaction Index

Drug combination is a critically important therapeutic approach for complex diseases such as cancer and HIV due to its potential for efficacy at lower, less toxic doses and the need to move new therapies rapidly into clinical trials. One of the key issues is to identify which combinations are additive, synergistic, or antagonistic. While the value of multidrug combinations has been well recognized in the cancer research community, to our best knowledge, all existing experimental studies rely on fixing the dose of one drug to reduce the dimensionality, e.g. looking at pairwise two-drug combinations, a suboptimal design. Hence, there is an urgent need to develop experimental design and analysis methods for studying multidrug combinations directly. Because the complexity of the problem increases exponentially with the number of constituent drugs, there has been little progress in the development of methods for the design and analysis of high-dimensional drug combinations. In fact, contrary to common mathematical reasoning, the case of three-drug combinations is fundamentally more difficult than two-drug combinations. Apparently, finding doses of the combination, number of combinations, and replicates needed to detect departures from additivity depends on dose–response shapes of individual constituent drugs. Thus, different classes of drugs of different dose–response shapes need to be treated as a separate case. Our application and case studies develop dose finding and sample size method for detecting departures from additivity with several common (linear and log-linear) classes of single dose–response curves. Furthermore, utilizing the geometric features of the interaction index, we propose a nonparametric model to estimate the interaction index surface by B-spine approximation and derive its asymptotic properties. Utilizing the method, we designed and analyzed a combination study of three anticancer drugs, PD184, HA14-1, and CEP3891 inhibiting myeloma H929 cell line. To our best knowledge, this is the first ever three drug combinations study performed based on the original 4D dose–response surface formed by dose ranges of three drugs.

scholarly journals Experimental design for multi-drug combination studies using signaling networks

Biometrics ◽  
2017 ◽  
Vol 74 (2) ◽  
pp. 538-547 ◽  
Author(s):  
Hengzhen Huang ◽  
Hong-Bin Fang ◽  
Ming T. Tan
Keyword(s):  
Experimental Design ◽  
Drug Combination ◽  
Signaling Networks ◽  
Combination Studies
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