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Artificial intelligence prediction

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https://www.readbyqxmd.com/read/28620199/predicting-the-outcomes-of-organic-reactions-via-machine-learning-are-current-descriptors-sufficient
#1
G Skoraczyński, P Dittwald, B Miasojedow, S Szymkuć, E P Gajewska, B A Grzybowski, A Gambin
As machine learning/artificial intelligence algorithms are defeating chess masters and, most recently, GO champions, there is interest - and hope - that they will prove equally useful in assisting chemists in predicting outcomes of organic reactions. This paper demonstrates, however, that the applicability of machine learning to the problems of chemical reactivity over diverse types of chemistries remains limited - in particular, with the currently available chemical descriptors, fundamental mathematical theorems impose upper bounds on the accuracy with which raction yields and times can be predicted...
June 15, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28566328/somatic-mutations-drive-distinct-imaging-phenotypes-in-lung-cancer
#2
Emmanuel Rios Velazquez, Chintan Parmar, Ying Liu, Thibaud P Coroller, Gisele Cruz, Olya Stringfield, Zhaoxiang Ye, G Mike Makrigiorgos, Fiona M M Fennessy, Raymond H Mak, Robert J Gillies, John Quackenbush, Hugo Aerts
Tumors are characterized by somatic mutations that drive biological processes ultimately reflected in tumor phenotype. With regard to radiographic phenotypes, generally unconnected through present understanding to the presence of specific mutations, artificial intelligence (AI) methods can automatically quantify phenotypic characters by using predefined, engineered algorithms or automatic deep-learning methods, a process also known as radiomics. Here we demonstrate how imaging phenotypes can be connected to somatic mutations through an integrated analysis of independent datasets of 763 lung adenocarcinoma patients with somatic mutation testing and engineered computed tomography (CT) image analytics...
May 31, 2017: Cancer Research
https://www.readbyqxmd.com/read/28545640/artificial-intelligence-in-precision%C3%A2-cardiovascular-medicine
#3
REVIEW
Chayakrit Krittanawong, HongJu Zhang, Zhen Wang, Mehmet Aydar, Takeshi Kitai
Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes, learning capacity, and knowledge storage. AI techniques have been applied in cardiovascular medicine to explore novel genotypes and phenotypes in existing diseases, improve the quality of patient care, enable cost-effectiveness, and reduce readmission and mortality rates. Over the past decade, several machine-learning techniques have been used for cardiovascular disease diagnosis and prediction. Each problem requires some degree of understanding of the problem, in terms of cardiovascular medicine and statistics, to apply the optimal machine-learning algorithm...
May 30, 2017: Journal of the American College of Cardiology
https://www.readbyqxmd.com/read/28537025/prediction-of-dissolved-oxygen-concentration-in-hypoxic-river-systems-using-support-vector-machine-a-case-study-of-wen-rui-tang-river-china
#4
Xiaoliang Ji, Xu Shang, Randy A Dahlgren, Minghua Zhang
Accurate quantification of dissolved oxygen (DO) is critically important for managing water resources and controlling pollution. Artificial intelligence (AI) models have been successfully applied for modeling DO content in aquatic ecosystems with limited data. However, the efficacy of these AI models in predicting DO levels in the hypoxic river systems having multiple pollution sources and complicated pollutants behaviors is unclear. Given this dilemma, we developed a promising AI model, known as support vector machine (SVM), to predict the DO concentration in a hypoxic river in southeastern China...
May 23, 2017: Environmental Science and Pollution Research International
https://www.readbyqxmd.com/read/28522333/development-of-qsars-for-parameterizing-physiology-based-toxicokinetic-models
#5
Dimosthenis Α Sarigiannis, Krystalia Papadaki, Periklis Kontoroupis, Spyros P Karakitsios
A Quantitative Structure Activity Relationship (QSAR) model was developed in order to predict physicochemical and biochemical properties of industrial chemicals of various groups. This model was based on the solvation equation, originally proposed by Abraham. In this work Abraham's solvation model got parameterized using artificial intelligence techniques such as artificial neural networks (ANNs) for the prediction of partitioning into kidney, heart, adipose, liver, muscle, brain and lung for the estimation of the bodyweight-normalized maximal metabolic velocity (Vmax) and the Michaelis - Menten constant (Km)...
May 15, 2017: Food and Chemical Toxicology
https://www.readbyqxmd.com/read/28499165/preparing-diopside-nanoparticle-scaffolds-via-space-holder-method-simulation-of-the-compressive-strength-and-porosity
#6
Majid Abdellahi, Aliakbar Najafinezhad, Hamid Ghayour, Saeed Saber-Samandari, Amirsalar Khandan
In the present study, diopside nanopowders were prepared via mechanical milling with eggshell as the calcium source. The space holder method (compaction of ceramic powder and spacer) as one of the most important methods to produce ceramic/metal scaffolds was used to produce diopside scaffolds. For the first time, the effect of the spacer size on mechanical properties and porosity of the obtained scaffolds was experimentally discussed. According to the results obtained, the NaCl particles (as the spacer) with the size of 400-600µm maintained their original spherical shape during the compaction and sintering processes...
May 3, 2017: Journal of the Mechanical Behavior of Biomedical Materials
https://www.readbyqxmd.com/read/28494618/machine-learning-methods-to-predict-diabetes-complications
#7
Arianna Dagliati, Simone Marini, Lucia Sacchi, Giulia Cogni, Marsida Teliti, Valentina Tibollo, Pasquale De Cata, Luca Chiovato, Riccardo Bellazzi
One of the areas where Artificial Intelligence is having more impact is machine learning, which develops algorithms able to learn patterns and decision rules from data. Machine learning algorithms have been embedded into data mining pipelines, which can combine them with classical statistical strategies, to extract knowledge from data. Within the EU-funded MOSAIC project, a data mining pipeline has been used to derive a set of predictive models of type 2 diabetes mellitus (T2DM) complications based on electronic health record data of nearly one thousand patients...
May 1, 2017: Journal of Diabetes Science and Technology
https://www.readbyqxmd.com/read/28420077/recognition-of-the-duration-and-prediction-of-insect-prevalence-of-stored-rough-rice-infested-by-the-red-flour-beetle-tribolium-castaneum-herbst-using-an-electronic-nose
#8
Sai Xu, Zhiyan Zhou, Keliang Li, Sierra Mari Jamir, Xiwen Luo
The purpose of this research is to explore the feasibility of applying an electronic nose for the intelligent monitoring of injurious insects in a stored grain environment. In this study, we employed an electronic nose to sample rough rice that contained three degrees of red flour beetle (Tribolium castaneum Herbst) infestation for different durations-light degree (LD), middle degree (MD), and heavy degree (HD)-and manually investigated the insect situation at the same time. Manual insect situation investigation shows that, in all three rice treatments, the insect amounts gradually decreased after infestation...
April 14, 2017: Sensors
https://www.readbyqxmd.com/read/28418055/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
#9
H Mazaheri, M Ghaedi, M H Ahmadi Azqhandi, A Asfaram
Analytical chemists apply statistical methods for both the validation and prediction of proposed models. Methods are required that are adequate for finding the typical features of a dataset, such as nonlinearities and interactions. Boosted regression trees (BRTs), as an ensemble technique, are fundamentally different to other conventional techniques, with the aim to fit a single parsimonious model. In this work, BRT, artificial neural network (ANN) and response surface methodology (RSM) models have been used for the optimization and/or modeling of the stirring time (min), pH, adsorbent mass (mg) and concentrations of MB and Cd(2+) ions (mg L(-1)) in order to develop respective predictive equations for simulation of the efficiency of MB and Cd(2+) adsorption based on the experimental data set...
April 18, 2017: Physical Chemistry Chemical Physics: PCCP
https://www.readbyqxmd.com/read/28412664/new-model-for-prediction-binary-mixture-of-antihistamine-decongestant-using-artificial-neural-networks-and-least-squares-support-vector-machine-by-spectrophotometry-method
#10
Shirin Mofavvaz, Mahmoud Reza Sohrabi, Alireza Nezamzadeh-Ejhieh
In the present study, artificial neural networks (ANNs) and least squares support vector machines (LS-SVM) as intelligent methods based on absorption spectra in the range of 230-300nm have been used for determination of antihistamine decongestant contents. In the first step, one type of network (feed-forward back-propagation) from the artificial neural network with two different training algorithms, Levenberg-Marquardt (LM) and gradient descent with momentum and adaptive learning rate back-propagation (GDX) algorithm, were employed and their performance was evaluated...
July 5, 2017: Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
https://www.readbyqxmd.com/read/28384614/how-much-information-about-embryo-implantation-potential-is-included-in-morphokinetic-data-a-prediction-model-based-on-artificial-neural-networks-and-principal-component-analysis
#11
Robert Milewski, Agnieszka Kuczyńska, Bożena Stankiewicz, Waldemar Kuczyński
PURPOSE: The aim of this study was to answer the question of how much information about embryo implantation potential can be obtained from morphokinetic parameters through the creation a predictive model based on morphokinetic information and using advanced data-mining and artificial intelligence methods. MATERIALS AND METHODS: Time-lapse recordings of 610 embryos were included in the analysis. For each embryo, absolute (t2, t3, t4, t5) and relative (cc2 and s2) morphokinetic parameters were collected...
March 2017: Advances in Medical Sciences
https://www.readbyqxmd.com/read/28382857/prediction-of-human-intestinal-absorption-of-compounds-using-artificial-intelligence-techniques
#12
Rajnish Kumar, Anju Sharma, Mohammed Haris Siddiqui, Rajesh Kumar Tiwari
Information about Pharmacokinetics of compounds is an essential component of drug design and development. Modeling the pharmacokinetic properties require identification of the factors effecting absorption, distribution, metabolism and excretion of compounds. There have been continuous attempts in the prediction of absorption of compounds using various Artificial intelligence methods in the effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are large numbers of individual predictive models available for absorption using machine learning approaches...
April 4, 2017: Current Drug Discovery Technologies
https://www.readbyqxmd.com/read/28353110/heavy-metal-monitoring-analysis-and-prediction-in-lakes-and-rivers-state-of-the-art
#13
REVIEW
Adnan Elzwayie, Haitham Abdulmohsin Afan, Mohammed Falah Allawi, Ahmed El-Shafie
Several research efforts have been conducted to monitor and analyze the impact of environmental factors on the heavy metal concentrations and physicochemical properties of water bodies (lakes and rivers) in different countries worldwide. This article provides a general overview of the previous works that have been completed in monitoring and analyzing heavy metals. The intention of this review is to introduce the historical studies to distinguish and understand the previous challenges faced by researchers in analyzing heavy metal accumulation...
May 2017: Environmental Science and Pollution Research International
https://www.readbyqxmd.com/read/28344110/toward-a-systematic-exploration-of-nano-bio-interactions
#14
REVIEW
Xue Bai, Fang Liu, Yin Liu, Cong Li, Shenqing Wang, Hongyu Zhou, Wenyi Wang, Hao Zhu, David A Winkler, Bing Yan
Many studies of nanomaterials make non-systematic alterations of nanoparticle physicochemical properties. Given the immense size of the property space for nanomaterials, such approaches are not very useful in elucidating fundamental relationships between inherent physicochemical properties of these materials and their interactions with, and effects on, biological systems. Data driven artificial intelligence methods such as machine learning algorithms have proven highly effective in generating models with good predictivity and some degree of interpretability...
May 15, 2017: Toxicology and Applied Pharmacology
https://www.readbyqxmd.com/read/28333051/artificial-intelligence-based-model-for-optimization-of-cod-removal-efficiency-of-an-up-flow-anaerobic-sludge-blanket-reactor-in-the-saline-wastewater-treatment
#15
Alain R Picos-Benítez, Juan D López-Hincapié, Abraham U Chávez-Ramírez, Adrián Rodríguez-García
The complex non-linear behavior presented in the biological treatment of wastewater requires an accurate model to predict the system performance. This study evaluates the effectiveness of an artificial intelligence (AI) model, based on the combination of artificial neural networks (ANNs) and genetic algorithms (GAs), to find the optimum performance of an up-flow anaerobic sludge blanket reactor (UASB) for saline wastewater treatment. Chemical oxygen demand (COD) removal was predicted using conductivity, organic loading rate (OLR) and temperature as input variables...
March 2017: Water Science and Technology: a Journal of the International Association on Water Pollution Research
https://www.readbyqxmd.com/read/28325604/early-prediction-of-radiotherapy-induced-parotid-shrinkage-and-toxicity-based-on-ct-radiomics-and-fuzzy-classification
#16
Marco Pota, Elisa Scalco, Giuseppe Sanguineti, Alessia Farneti, Giovanni Mauro Cattaneo, Giovanna Rizzo, Massimo Esposito
MOTIVATION: Patients under radiotherapy for head-and-neck cancer often suffer of long-term xerostomia, and/or consistent shrinkage of parotid glands. In order to avoid these drawbacks, adaptive therapy can be planned for patients at risk, if the prediction is obtained timely, before or during the early phase of treatment. Artificial intelligence can address the problem, by learning from examples and building classification models. In particular, fuzzy logic has shown its suitability for medical applications, in order to manage uncertain data, and to build transparent rule-based classifiers...
March 18, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28325441/a-review-of-fuzzy-cognitive-maps-in-medicine-taxonomy-methods-and-applications
#17
REVIEW
Abdollah Amirkhani, Elpiniki I Papageorgiou, Akram Mohseni, Mohammad R Mosavi
BACKGROUND AND OBJECTIVE: A high percentage of medical errors, committed because of physician's lack of experience, huge volume of data to be analyzed, and inaccessibility to medical records of previous patients, can be reduced using computer-aided techniques. Therefore, designing more efficient medical decision-support systems (MDSSs) to assist physicians in decision-making is crucially important. Through combining the properties of fuzzy logic and neural networks, fuzzy cognitive maps (FCMs) are among the latest, most efficient, and strongest artificial intelligence techniques for modeling complex systems...
April 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28321856/automation-is-it-really-different-this-time
#18
REVIEW
Judy Wajcman
This review examines several recent books that deal with the impact of automation and robotics on the future of jobs. Most books in this genre predict that the current phase of digital technology will create massive job loss in an unprecedented way, that is, that this wave of automation is different from previous waves. Uniquely digital technology is said to automate professional occupations for the first time. This review critically examines these claims, puncturing some of the hyperbole about automation, robotics and Artificial Intelligence...
March 2017: British Journal of Sociology
https://www.readbyqxmd.com/read/28298701/identification-of-probabilities
#19
Paul M B Vitányi, Nick Chater
Within psychology, neuroscience and artificial intelligence, there has been increasing interest in the proposal that the brain builds probabilistic models of sensory and linguistic input: that is, to infer a probabilistic model from a sample. The practical problems of such inference are substantial: the brain has limited data and restricted computational resources. But there is a more fundamental question: is the problem of inferring a probabilistic model from a sample possible even in principle? We explore this question and find some surprisingly positive and general results...
February 2017: Journal of Mathematical Psychology
https://www.readbyqxmd.com/read/28285459/a-critical-review-for-developing-accurate-and-dynamic-predictive-models-using-machine-learning-methods-in-medicine-and-health-care
#20
Hamdan O Alanazi, Abdul Hanan Abdullah, Kashif Naseer Qureshi
Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed...
April 2017: Journal of Medical Systems
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