scholarly journals Potential use of machine learning to determine yield locus parameters

2021 ◽  
Vol 1157 (1) ◽  
pp. 012064
Author(s):  
C Karadogan ◽  
P Cyron ◽  
M Liewald
Keyword(s):  
Machine Learning ◽  
Yield Locus ◽  
Potential Use

scholarly journals Synthetic heparan sulfate standards and machine learning facilitate the development of solid-state nanopore analysis

2021 ◽  
Vol 118 (11) ◽  
pp. e2022806118
Author(s):  
Ke Xia ◽  
James T. Hagan ◽  
Li Fu ◽  
Brian S. Sheetz ◽  
Somdatta Bhattacharya ◽  
...  
Keyword(s):  
Machine Learning ◽  
Solid State ◽  
Nucleic Acid ◽  
Heparan Sulfate ◽  
Image Recognition ◽  
Single Molecule ◽  
Monosaccharide Composition ◽  
Minimal Representation ◽  
Recognition Algorithms ◽  
Potential Use

The application of solid-state (SS) nanopore devices to single-molecule nucleic acid sequencing has been challenging. Thus, the early successes in applying SS nanopore devices to the more difficult class of biopolymer, glycosaminoglycans (GAGs), have been surprising, motivating us to examine the potential use of an SS nanopore to analyze synthetic heparan sulfate GAG chains of controlled composition and sequence prepared through a promising, recently developed chemoenzymatic route. A minimal representation of the nanopore data, using only signal magnitude and duration, revealed, by eye and image recognition algorithms, clear differences between the signals generated by four synthetic GAGs. By subsequent machine learning, it was possible to determine disaccharide and even monosaccharide composition of these four synthetic GAGs using as few as 500 events, corresponding to a zeptomole of sample. These data suggest that ultrasensitive GAG analysis may be possible using SS nanopore detection and well-characterized molecular training sets.

scholarly journals Imputation of Missing Gas Permeability Data for Polymer Membranes using Machine Learning

2020 ◽  
Author(s):  
Qi Yuan ◽  
Mariagiulia Longo ◽  
Aaron Thornton ◽  
Neil B. McKeown ◽  
Bibiana Comesana-Gandara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficient ◽  
Missing Values ◽  
Gas Permeability ◽  
Polymer Membranes ◽  
Gas Separations ◽  
Initial Stage ◽  
Sparse Features ◽  
Potential Use ◽  
Insight Into

<p><a>Polymer-based membranes can be used for energy efficient gas separations. Successful exploitation of new materials requires accurate knowledge of the transport properties of all gases of interest. An open source database of such data is of significant benefit to the research community. The Membrane Society of Australasia (https://membrane-australasia.org/) hosts a database for experimentally measured and reported polymer gas permeabilities. However, the database is incomplete, limiting its potential use as a research tool. Here, missing values in the database were filled using machine learning (ML). The ML model was validated against gas permeability measurements that were not recorded in the database. Through imputing the missing data, it is possible to re-analyse historical polymers and look for potential “missed” candidates with promising gas selectivity. In addition, for systems with limited experimental data, ML using sparse features was performed, and we suggest that once the permeability of CO<sub>2</sub> and/or O<sub>2</sub> for a polymer has been measured, most other gas permeabilities and selectivities, including those for CO<sub>2</sub>/CH<sub>4</sub> and CO<sub>2</sub>/N<sub>2</sub>, can be quantitatively estimated. This early insight into the gas permeability of a new system can be used at an initial stage of experimental measurements to rapidly identify polymer membranes worth further investigation.</a></p>

scholarly journals Random forest model identifies serve strength as a key predictor of tennis match outcome

2021 ◽  
pp. 1-8
Author(s):  
Zijian Gao ◽  
Amanda Kowalczyk
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Sports Analytics ◽  
Record Keeping ◽  
Machine Learning Methods ◽  
Simple Machine ◽  
Similar Information ◽  
Betting Odds ◽  
Potential Use ◽  
Tennis Match

Tennis is a popular sport worldwide, boasting millions of fans and numerous national and international tournaments. Like many sports, tennis has benefitted from the popularity of rigorous record-keeping of game and player information, as well as the growth of machine learning methods for use in sports analytics. Of particular interest to bettors and betting companies alike is potential use of sports records to predict tennis match outcomes prior to match start. We compiled, cleaned, and used the largest database of tennis match information to date to predict match outcome using fairly simple machine learning methods. Using such methods allows for rapid fit and prediction times to readily incorporate new data and make real-time predictions. We were able to predict match outcomes with upwards of 80%accuracy, much greater than predictions using betting odds alone, and identify serve strength as a key predictor of match outcome. By combining prediction accuracies from three models, we were able to nearly recreate a probability distribution based on average betting odds from betting companies, which indicates that betting companies are using similar information to assign odds to matches. These results demonstrate the capability of relatively simple machine learning models to quite accurately predict tennis match outcomes.

scholarly journals Photometric classification of Hyper Suprime-Cam transients using machine learning

2020 ◽  
Vol 72 (5) ◽  
Author(s):  
Ichiro Takahashi ◽  
Nao Suzuki ◽  
Naoki Yasuda ◽  
Akisato Kimura ◽  
Naonori Ueda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Area Under The Curve ◽  
Full Dataset ◽  
Transient Data ◽  
Type Classification ◽  
Potential Use

Abstract The advancement of technology has resulted in a rapid increase in supernova (SN) discoveries. The Subaru/Hyper Suprime-Cam (HSC) transient survey, conducted from fall 2016 through spring 2017, yielded 1824 SN candidates. This gave rise to the need for fast type classification for spectroscopic follow-up and prompted us to develop a machine learning algorithm using a deep neural network with highway layers. This algorithm is trained by actual observed cadence and filter combinations such that we can directly input the observed data array without any interpretation. We tested our model with a dataset from the LSST classification challenge (Deep Drilling Field). Our classifier scores an area under the curve (AUC) of 0.996 for binary classification (SN Ia or non-SN Ia) and 95.3% accuracy for three-class classification (SN Ia, SN Ibc, or SN II). Application of our binary classification to HSC transient data yields an AUC score of 0.925. With two weeks of HSC data since the first detection, this classifier achieves 78.1% accuracy for binary classification, and the accuracy increases to 84.2% with the full dataset. This paper discusses the potential use of machine learning for SN type classification purposes.

scholarly journals Imputation of Missing Gas Permeability Data for Polymer Membranes using Machine Learning

2020 ◽  
Author(s):  
Qi Yuan ◽  
Mariagiulia Longo ◽  
Aaron Thornton ◽  
Neil B. McKeown ◽  
Bibiana Comesana-Gandara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficient ◽  
Missing Values ◽  
Gas Permeability ◽  
Polymer Membranes ◽  
Gas Separations ◽  
Initial Stage ◽  
Sparse Features ◽  
Potential Use ◽  
Insight Into

<p><a>Polymer-based membranes can be used for energy efficient gas separations. Successful exploitation of new materials requires accurate knowledge of the transport properties of all gases of interest. An open source database of such data is of significant benefit to the research community. The Membrane Society of Australasia (https://membrane-australasia.org/) hosts a database for experimentally measured and reported polymer gas permeabilities. However, the database is incomplete, limiting its potential use as a research tool. Here, missing values in the database were filled using machine learning (ML). The ML model was validated against gas permeability measurements that were not recorded in the database. Through imputing the missing data, it is possible to re-analyse historical polymers and look for potential “missed” candidates with promising gas selectivity. In addition, for systems with limited experimental data, ML using sparse features was performed, and we suggest that once the permeability of CO<sub>2</sub> and/or O<sub>2</sub> for a polymer has been measured, most other gas permeabilities and selectivities, including those for CO<sub>2</sub>/CH<sub>4</sub> and CO<sub>2</sub>/N<sub>2</sub>, can be quantitatively estimated. This early insight into the gas permeability of a new system can be used at an initial stage of experimental measurements to rapidly identify polymer membranes worth further investigation.</a></p>

scholarly journals Neoadjuvant therapy in pancreatic cancer: what is the true oncological benefit?

2020 ◽  
Vol 405 (7) ◽  
pp. 879-887
Author(s):  
Lei Ren ◽  
Carmen Mota Reyes ◽  
Helmut Friess ◽  
Ihsan Ekin Demir
Keyword(s):  
Machine Learning ◽  
Pancreatic Cancer ◽  
Neoadjuvant Therapy ◽  
Early Stage ◽  
Scientific Evidence ◽  
Locally Advanced ◽  
Borderline Resectable ◽  
Multidisciplinary Communication ◽  
Arterial Resection ◽  
Potential Use

Abstract Background Neoadjuvant therapies (neoTx) have revolutionized the treatment of borderline resectable (BR) and locally advanced (LA) pancreatic cancer (PCa) by significantly increasing the rate of R0 resections, which remains the only curative strategy for these patients. However, there is still room for improvement of neoTx in PCa. Purpose Here, we aimed to critically analyze the benefits of neoTx in LA and BR PCa and its potential use on patients with resectable PCa. We also explored the feasibility of arterial resection (AR) to increase surgical radicality and the incorporation of immunotherapy to optimize neoadjuvant approaches in PCa. Conclusion For early stage, i.e., resectable, PCa, there is not enough scientific evidence for routinely recommending neoTx. For LA and BR PCa, optimization of neoadjuvant therapy necessitates more sophisticated complex surgical resections, machine learning and radiomic approaches, integration of immunotherapy due to the high antigen load, standardized histopathological assessment, and improved multidisciplinary communication.

scholarly journals Machine Learning Techniques for Better Data Driven Decisions Revisited

2020 ◽  
Vol 9 (4) ◽  
pp. 460-464
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Human Knowledge ◽  
Learning Techniques ◽  
Data Driven Decisions ◽  
Speed And Accuracy ◽  
Potential Use

The main goal of machine learning is to accurately predict the decisions to the problems without human expert intervention. These decisions depend upon patterns found and facts learnt during training tenure. However, prior incorporation of human knowledge is necessary for better prediction of the test data. The main aim is to make machines self-reliant for decision making. Providing machine with this vision makes it useful in every modern field. This makes the stepping stone to make computers behave as the humans do. Enhancing its speed and accuracy are the next step in this field. This paper presents a stock of techniques used to train the machines to respond to patterns present in the data sets so that useful information may be extracted for its potential use.

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