A Universal Machine Learning Algorithm for Large-Scale Screening of Materials

2020 ◽  
Vol 142 (8) ◽  
pp. 3814-3822 ◽  
Author(s):  
George S. Fanourgakis ◽  
Konstantinos Gkagkas ◽  
Emmanuel Tylianakis ◽  
George E. Froudakis
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Universal Machine ◽  
Large Scale Screening

scholarly journals Prioritization of Candidate Genes for Congenital Diaphragmatic Hernia in a Critical Region on Chromosome 4p16 using a Machine-Learning Algorithm

2018 ◽  
Vol 07 (04) ◽  
pp. 164-173 ◽  
Author(s):  
Ian Campbell ◽  
Samantha Stover ◽  
Andres Hernandez-Garcia ◽  
Shalini Jhangiani ◽  
Jaya Punetha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Congenital Diaphragmatic Hernia ◽  
Diaphragmatic Hernia ◽  
Critical Region ◽  
Large Scale ◽  
Learning Algorithm ◽  
Heart Defects ◽  
Machine Learning Algorithm ◽  
Increased Risk ◽  
Chromosome 4P16

AbstractWolf–Hirschhorn syndrome (WHS) is caused by partial deletion of the short arm of chromosome 4 and is characterized by dysmorphic facies, congenital heart defects, intellectual/developmental disability, and increased risk for congenital diaphragmatic hernia (CDH). In this report, we describe a stillborn girl with WHS and a large CDH. A literature review revealed 15 cases of WHS with CDH, which overlap a 2.3-Mb CDH critical region. We applied a machine-learning algorithm that integrates large-scale genomic knowledge to genes within the 4p16.3 CDH critical region and identified FGFRL1, CTBP1, NSD2, FGFR3, CPLX1, MAEA, CTBP1-AS2, and ZNF141 as genes whose haploinsufficiency may contribute to the development of CDH.

scholarly journals A Novel, Potentially Universal Machine Learning Algorithm to Predict Complications in Total Knee Arthroplasty

2021 ◽  
Vol 10 ◽  
pp. 135-143
Author(s):  
Sai K. Devana ◽  
Akash A. Shah ◽  
Changhee Lee ◽  
Andrew R. Roney ◽  
Mihaela van der Schaar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Total Knee Arthroplasty ◽  
Knee Arthroplasty ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Universal Machine ◽  
Total Knee

scholarly journals Large-scale randomized experiments reveals that machine learning-based instruction helps people memorize more effectively

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Utkarsh Upadhyay ◽  
Graham Lancashire ◽  
Christoph Moser ◽  
Manuel Gomez-Rodriguez
Keyword(s):  
Machine Learning ◽  
Randomized Controlled Trial ◽  
Large Scale ◽  
Learning Algorithm ◽  
Controlled Trial ◽  
Machine Learning Algorithm ◽  
Randomized Experiments ◽  
Randomized Controlled ◽  
Sequencing Algorithm ◽  
Based Instruction

AbstractWe perform a large-scale randomized controlled trial to evaluate the potential of machine learning-based instruction sequencing to improve memorization while allowing the learners the freedom to choose their review times. After controlling for the length and frequency of study, we find that learners for whom a machine learning algorithm determines which questions to include in their study sessions remember the content over ~69% longer. We also find that the sequencing algorithm has an effect on users’ engagement.

scholarly journals Star formation rates and stellar masses from machine learning

2019 ◽  
Vol 622 ◽  
pp. A137 ◽  
Author(s):  
V. Bonjean ◽  
N. Aghanim ◽  
P. Salomé ◽  
A. Beelen ◽  
M. Douspis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Star Formation ◽  
Large Scale ◽  
Learning Algorithm ◽  
Formation Rate ◽  
Stellar Mass ◽  
Machine Learning Algorithm ◽  
Photometric Redshift ◽  
Star Formation Activity ◽  
Stellar Masses

Star-formation activity is a key property to probe the structure formation and hence characterise the large-scale structures of the universe. This information can be deduced from the star formation rate (SFR) and the stellar mass (M⋆), both of which, but especially the SFR, are very complex to estimate. Determining these quantities from UV, optical, or IR luminosities relies on complex modeling and on priors on galaxy types. We propose a method based on the machine-learning algorithm Random Forest to estimate the SFR and the M⋆ of galaxies at redshifts in the range 0.01 <  z <  0.3, independent of their type. The machine-learning algorithm takes as inputs the redshift, WISE luminosities, and WISE colours in near-IR, and is trained on spectra-extracted SFR and M⋆ from the SDSS MPA-JHU DR8 catalogue as outputs. We show that our algorithm can accurately estimate SFR and M⋆ with scatters of σSFR = 0.38 dex and σM⋆ = 0.16 dex for SFR and stellar mass, respectively, and that it is unbiased with respect to redshift or galaxy type. The full-sky coverage of the WISE satellite allows us to characterise the star-formation activity of all galaxies outside the Galactic mask with spectroscopic redshifts in the range 0.01 <  z <  0.3. The method can also be applied to photometric-redshift catalogues, with best scatters of σSFR = 0.42 dex and σM⋆ = 0.24 dex obtained in the redshift range 0.1 <  z <  0.3.

scholarly journals Unsupervised functional neurocartography of the Aplysia buccal ganglion

2021 ◽  
Author(s):  
Renan M Costa ◽  
Vijay A Dharmaraj ◽  
Ryota Homma ◽  
Curtis L Neveu ◽  
William B Kristan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Motor Pattern ◽  
Pattern Generation ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Buccal Ganglion ◽  
Multiple Features ◽  
Selection Of

A major limitation of large-scale neuronal recordings is the difficulty in locating the same neuron in different subjects, referred to as the "correspondence" issue. This issue stems, at least in part, from the lack of a unique feature that unequivocally identifies each neuron. One promising approach to this problem is the functional neurocartography framework developed by Frady et al. (2016), in which neurons are identified by a semi-supervised machine learning algorithm using a combination of multiple selected features. Here, the framework was adapted to the buccal ganglia of Aplysia. Multiple features were derived from neuronal activity during motor pattern generation, responses to peripheral nerve stimulation, and the spatial properties of each cell. The feature set was optimized based on its potential usefulness in discriminating neurons from each other, and then used to match putatively homologous neurons across subjects with the functional neurocartography software. A matching method was developed based on a cyclic matching algorithm that allows for unsupervised extraction of groups of neurons, thereby enhancing scalability of the analysis. Cyclic matching was also used to automate the selection of high-quality matches, which allowed for unsupervised implementation of the machine learning algorithm. This study paves the way for investigating the roles of both well-characterized and previously uncharacterized neurons in Aplysia, as well as helps to adapt this framework to other systems.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery
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