scholarly journals INDECISIVENESS IN PROBLEMS OF MACHINE LEARNING AND AI

2020 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Aleksa Svetozar Srdanov
Keyword(s):  
Machine Learning ◽  
Scientific Research ◽  
High Quality

The problem of indecisiveness is integral part in each scientific research. However, it is still not a certainty whether this problem has an objective nature. In this paper we will extend the analysis of the sources and causes of indecisiveness and define the new categories that are a stumbling block in writing high quality software. Based on a sample, we will propose several ways to classify indecisiveness. Specifically, we will investigate indecisiveness related to a human, machine and environment. In some cases, it is possible to distinguish between remediable and unavoidable indecisiveness depending on the cause.

scholarly journals Fabrication of High-Quality Thin Solid-State Electrolyte Films Assisted by Machine Learning

2021 ◽  
pp. 1639-1648
Author(s):  
Yu-Ting Chen ◽  
Marc Duquesnoy ◽  
Darren H. S. Tan ◽  
Jean-Marie Doux ◽  
Hedi Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Solid State ◽  
High Quality ◽  
Solid State Electrolyte

scholarly journals Prediction of Sugar Content in Port Wine Vintage Grapes Using Machine Learning and Hyperspectral Imaging

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1241
Author(s):  
Véronique Gomes ◽  
Marco S. Reis ◽  
Francisco Rovira-Más ◽  
Ana Mendes-Ferreira ◽  
Pedro Melo-Pinto
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Hyperspectral Imaging ◽  
Sugar Content ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Port Wine ◽  
Monitoring And Control ◽  
High Quality ◽  
Harvesting Stage

The high quality of Port wine is the result of a sequence of winemaking operations, such as harvesting, maceration, fermentation, extraction and aging. These stages require proper monitoring and control, in order to consistently achieve the desired wine properties. The present work focuses on the harvesting stage, where the sugar content of grapes plays a key role as one of the critical maturity parameters. Our approach makes use of hyperspectral imaging technology to rapidly extract information from wine grape berries; the collected spectra are fed to machine learning algorithms that produce estimates of the sugar level. A consistent predictive capability is important for establishing the harvest date, as well as to select the best grapes to produce specific high-quality wines. We compared four different machine learning methods (including deep learning), assessing their generalization capacity for different vintages and varieties not included in the training process. Ridge regression, partial least squares, neural networks and convolutional neural networks were the methods considered to conduct this comparison. The results show that the estimated models can successfully predict the sugar content from hyperspectral data, with the convolutional neural network outperforming the other methods.

57 Precision neoantigen discovery using novel algorithms and expanded HLA-ligandome datasets

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A62-A62
Author(s):  
Dattatreya Mellacheruvu ◽  
Rachel Pyke ◽  
Charles Abbott ◽  
Nick Phillips ◽  
Sejal Desai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cell Lines ◽  
Antigen Processing ◽  
Large Scale ◽  
Prediction Models ◽  
K562 Cells ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
High Quality ◽  
Tissue Samples

BackgroundAccurately identified neoantigens can be effective therapeutic agents in both adjuvant and neoadjuvant settings. A key challenge for neoantigen discovery has been the availability of accurate prediction models for MHC peptide presentation. We have shown previously that our proprietary model based on (i) large-scale, in-house mono-allelic data, (ii) custom features that model antigen processing, and (iii) advanced machine learning algorithms has strong performance. We have extended upon our work by systematically integrating large quantities of high-quality, publicly available data, implementing new modelling algorithms, and rigorously testing our models. These extensions lead to substantial improvements in performance and generalizability. Our algorithm, named Systematic HLA Epitope Ranking Pan Algorithm (SHERPA™), is integrated into the ImmunoID NeXT Platform®, our immuno-genomics and transcriptomics platform specifically designed to enable the development of immunotherapies.MethodsIn-house immunopeptidomic data was generated using stably transfected HLA-null K562 cells lines that express a single HLA allele of interest, followed by immunoprecipitation using W6/32 antibody and LC-MS/MS. Public immunopeptidomics data was downloaded from repositories such as MassIVE and processed uniformly using in-house pipelines to generate peptide lists filtered at 1% false discovery rate. Other metrics (features) were either extracted from source data or generated internally by re-processing samples utilizing the ImmunoID NeXT Platform.ResultsWe have generated large-scale and high-quality immunopeptidomics data by using approximately 60 mono-allelic cell lines that unambiguously assign peptides to their presenting alleles to create our primary models. Briefly, our primary ‘binding’ algorithm models MHC-peptide binding using peptide and binding pockets while our primary ‘presentation’ model uses additional features to model antigen processing and presentation. Both primary models have significantly higher precision across all recall values in multiple test data sets, including mono-allelic cell lines and multi-allelic tissue samples. To further improve the performance of our model, we expanded the diversity of our training set using high-quality, publicly available mono-allelic immunopeptidomics data. Furthermore, multi-allelic data was integrated by resolving peptide-to-allele mappings using our primary models. We then trained a new model using the expanded training data and a new composite machine learning architecture. The resulting secondary model further improves performance and generalizability across several tissue samples.ConclusionsImproving technologies for neoantigen discovery is critical for many therapeutic applications, including personalized neoantigen vaccines, and neoantigen-based biomarkers for immunotherapies. Our new and improved algorithm (SHERPA) has significantly higher performance compared to a state-of-the-art public algorithm and furthers this objective.

scholarly journals Evolutionary Machine Learning for Multi-Objective Class Solutions in Medical Deformable Image Registration

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 99 ◽  
Author(s):  
Kleopatra Pirpinia ◽  
Peter A. N. Bosman ◽  
Jan-Jakob Sonke ◽  
Marcel van Herk ◽  
Tanja Alderliesten
Keyword(s):  
Machine Learning ◽  
Image Registration ◽  
State Of The Art ◽  
Deformable Image Registration ◽  
Optimization Approach ◽  
High Quality ◽  
Trade Off ◽  
Multi Objective ◽  
Current State ◽  
Image Artefacts

Current state-of-the-art medical deformable image registration (DIR) methods optimize a weighted sum of key objectives of interest. Having a pre-determined weight combination that leads to high-quality results for any instance of a specific DIR problem (i.e., a class solution) would facilitate clinical application of DIR. However, such a combination can vary widely for each instance and is currently often manually determined. A multi-objective optimization approach for DIR removes the need for manual tuning, providing a set of high-quality trade-off solutions. Here, we investigate machine learning for a multi-objective class solution, i.e., not a single weight combination, but a set thereof, that, when used on any instance of a specific DIR problem, approximates such a set of trade-off solutions. To this end, we employed a multi-objective evolutionary algorithm to learn sets of weight combinations for three breast DIR problems of increasing difficulty: 10 prone-prone cases, 4 prone-supine cases with limited deformations and 6 prone-supine cases with larger deformations and image artefacts. Clinically-acceptable results were obtained for the first two problems. Therefore, for DIR problems with limited deformations, a multi-objective class solution can be machine learned and used to compute straightforwardly multiple high-quality DIR outcomes, potentially leading to more efficient use of DIR in clinical practice.

scholarly journals A machine learning approach for somatic mutation discovery

2018 ◽  
Vol 10 (457) ◽  
pp. eaar7939 ◽  
Author(s):  
Derrick E. Wood ◽  
James R. White ◽  
Andrew Georgiadis ◽  
Beth Van Emburgh ◽  
Sonya Parpart-Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cancer Patients ◽  
Somatic Mutation ◽  
Mutation Detection ◽  
Checkpoint Inhibitors ◽  
False Negative ◽  
The Cancer Genome Atlas ◽  
High Quality ◽  
Lung Cancer Patients ◽  
Mutation Discovery

Variability in the accuracy of somatic mutation detection may affect the discovery of alterations and the therapeutic management of cancer patients. To address this issue, we developed a somatic mutation discovery approach based on machine learning that outperformed existing methods in identifying experimentally validated tumor alterations (sensitivity of 97% versus 90 to 99%; positive predictive value of 98% versus 34 to 92%). Analysis of paired tumor-normal exome data from 1368 TCGA (The Cancer Genome Atlas) samples using this method revealed concordance for 74% of mutation calls but also identified likely false-positive and false-negative changes in TCGA data, including in clinically actionable genes. Determination of high-quality somatic mutation calls improved tumor mutation load–based predictions of clinical outcome for melanoma and lung cancer patients previously treated with immune checkpoint inhibitors. Integration of high-quality machine learning mutation detection in clinical next-generation sequencing (NGS) analyses increased the accuracy of test results compared to other clinical sequencing analyses. These analyses provide an approach for improved identification of tumor-specific mutations and have important implications for research and clinical management of cancer patients.

scholarly journals Scientific and practical fundamentals of growing cabbage (Brassica capitata lizg.) in Uzbekistan

2021 ◽  
Vol 939 (1) ◽  
pp. 012044
Author(s):  
A J Shokirov ◽  
S S Lapasov ◽  
K J Shokirov
Keyword(s):  
Large Scale ◽  
Scientific Research ◽  
Mineral Fertilizers ◽  
Biological Productivity ◽  
High Quality ◽  
Planting Time ◽  
Yield And Quality ◽  
White Cabbage ◽  
Urgent Task

Abstract At present, scientific research is underway to further develop vegetable growing in the secondary crop, in particular to further increase the yield and quality of white cabbage, to select a system of planting time-sowing scheme that maximizes the biological productivity of varieties, and to apply the most optimal standards of fertilization and irrigation. In this regard, the urgent task remains to determine the optimal varieties of cabbage that can be grown in repeated crops, their optimal planting scheme, timing, development and implementation of optimal standards for each variety of mineral fertilizers and irrigation, and its solution is large-scale throughout the country. Besides that a number of problematic issues are addressed, which could allow to get high and high-quality harvest of white cabbage in repeated sowing in grain-free areas.

scholarly journals Basic research in the context of the development of medical substances and technologies

2018 ◽  
pp. 84-86
Author(s):  
Л.В. Кузнецова ◽  
Л.Ю. Бахтина ◽  
И.Ю. Малышев
Keyword(s):  
Experimental Design ◽  
Technology Development ◽  
Scientific Research ◽  
Basic Research ◽  
Pharmaceutical Companies ◽  
High Quality ◽  
Pharmacological Research ◽  
Design And Methods

В кратком обзоре обсуждаются задачи фармацевтических компаний, и вопросы о наиболее рациональном соотношении скорости, стоимости и качества процесса разработки лекарств и технологий (DDD). Делается заключение, что экспериментальный дизайн и методы медико-фармакологических исследований должны разрабатываться на основании современных принципов и представлений о содержании этапов DDD, с особым акцентом на высокое качество научных исследований на этапе открытия. This brief review discusses challenges of pharmaceutical companies and issues of the most rational relationship between the speed, cost, and quality of the process for drug and technology development (DTD). It was concluded that the experimental design and methods of medical and pharmacological research should be developed on the basis of modern principles and ideas about the essence of DTD stages with a particular emphasis on the high quality of scientific research at the stage of discovery.

scholarly journals From journal to headline: the accuracy of climate science news in Danish high quality newspapers

2011 ◽  
Vol 10 (02) ◽  
pp. A03 ◽  
Author(s):  
Gunver Lystbæk Vestergård
Keyword(s):  
Climate Change ◽  
Content Analysis ◽  
Mass Media ◽  
Scientific Research ◽  
Climate Science ◽  
High Quality ◽  
Scientific Publications ◽  
News Stories ◽  
Science News ◽  
Preliminary Study

A significant number of mass media news stories on climate change quote scientific publications. However, the journalistic process of popularizing scientific research regarding climate change has been profoundly criticized for being manipulative and inaccurate. This preliminary study used content analysis to examine the accuracy of Danish high quality newspapers in quoting scientific publications from 1997 to 2009. Out of 88 articles, 46 contained inaccuracies though the majority was found to be insignificant and random. The study concludes that Danish broadsheet newspapers are ‘moderately inaccurate’ in quoting science publications but are not deliberately hyping scientific claims. However, the study also shows that 11% contained confusion of source, meaning that statements originating from press material or other news outlets were incorrectly credited to scientific peer-reviewed publications.

Machine Learning Approaches to Retrieve High-Quality, Clinically Relevant, Evidence from the Biomedical Literature: A Systematic Review (Preprint)

2021 ◽  
Author(s):  
Wael Abdelkader ◽  
Tamara Navarro ◽  
Rick Parrish ◽  
Chris Cotoi ◽  
Federico Germini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Strong Evidence ◽  
Clinical Care ◽  
Biomedical Literature ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
High Quality ◽  
Applied Machine Learning

BACKGROUND The rapid growth of the biomedical literature makes identifying strong evidence a time-consuming task. Applying machine learning to the process could be a viable solution that limits effort while maintaining accuracy. OBJECTIVE To summarize the nature and comparative performance of machine learning approaches that have been applied to retrieve high-quality evidence for clinical consideration from the biomedical literature. METHODS We conducted a systematic review of studies that applied machine learning techniques to identify high-quality clinical articles in the biomedical literature. Multiple databases were searched to July 2020. Extracted data focused on the applied machine learning model, steps in the development of the models, and model performance. RESULTS From 3918 retrieved studies, 10 met our inclusion criteria. All followed a supervised machine learning approach and applied, from a limited range of options, a high-quality standard for the training of their model. The results show that machine learning can achieve a sensitivity of 95% while maintaining a high precision of 86%. CONCLUSIONS Applying machine learning to distinguish studies with strong evidence for clinical care has the potential to decrease the workload of manually identifying these. The evidence base is active and evolving. Reported methods were variable across the studies but focused on supervised machine learning approaches. Performance may improve by applying more sophisticated approaches such as active learning, auto-machine learning, and unsupervised machine learning approaches.

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