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 ◽  
...  

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.

2020 ◽  
Vol 142 (8) ◽  
pp. 3814-3822 ◽  
Author(s):  
George S. Fanourgakis ◽  
Konstantinos Gkagkas ◽  
Emmanuel Tylianakis ◽  
George E. Froudakis

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1549-1549 ◽  
Author(s):  
Guillaume Manson ◽  
Pauline Brice ◽  
Charles Herbaux ◽  
Maria Silva ◽  
Krimo Bouabdallah ◽  
...  

Introduction Patients with relapsed/refractory Hodgkin lymphoma (R/R HL) experience high response rates upon anti-PD1 therapy. In these patients, the optimal duration of treatment and the risk of relapse after anti-PD1 discontinuation are unknown. Furthermore, the efficacy of anti-PD1 re-treatment in patients who relapse after anti-PD1 discontinuation remains to be determined. Here, we investigated the risk of relapse in patients who responded to anti-PD1 therapy and discontinued the treatment, as well as the efficacy of anti-PD1 re-treatment in patients who relapsed after anti-PD1 discontinuation. Methods We retrospectively analyzed patients with R/R HL who responded to anti-PD1 monotherapy (concomitant radiotherapy was permitted) and discontinued the treatment either because of unacceptable toxicity or prolonged remission (based on the clinician's decision). Patients who discontinued because of relapse/progression or underwent consolidation with allogenic stem cell transplantation [alloSCT] were not included. A random forest machine-learning algorithm was trained to predict relapse using 14 candidate biomarkers. Finally, we analyzed the outcome of patients who relapsed after anti-PD1 discontinuation and their response to anti-PD1 re-treatment. Results We included 32 patients from 13 Centers in France, Portugal and Belgium. Patients' characteristics are summarized in Table 1. At the time of anti-PD1 discontinuation, patients had received either nivolumab (N=27, 84.4%) or pembrolizumab (N=5, 12.5%) for a median duration of 14.6 (range, 0-33.5) months. Patients discontinued anti-PD1 treatment either because of prolonged remission (N=23, 71.9%) or unacceptable toxicity (N=9, 28.1%). Most patients were in CR (N=29, 90.1%) at the time of anti-PD1 discontinuation. After a median follow-up of 20.8 months (range, 0.7-47.6) from anti-PD1 discontinuation, 21 (65.6%) patients had not relapsed/progressed. All 3 patients who were in PR at the time of anti-PD1 discontinuation had relapsed. Among the 29 patients who were in CR at the time of anti-PD1 discontinuation, the estimated disease-free survival was 64.3% (CI 95, 46.6-88.7%) at 24 months (Figure 1). Three patients died: two from disease progression and one from severe GVHD while in CR. Interestingly, 4 patients remain in CR more than 3 years after anti-PD1 discontinuation although these patients had received only short courses of anti-PD1 (<6 months). One of them received a single dose of nivolumab for a relapse post-alloSCT and remains disease-free 47.6 months later. Using a testing set of 25 patients, the machine-learning algorithm predicted an increased risk of relapse at 12 months based on three main patients characteristics: the absence of complete metabolic response at the end of anti-PD1 treatment, prolonged time to achieve best overall response, and older age. Among the 11 patients who relapsed, 7 were re-treated with (the same) anti-PD1 (Figure 2). Five achieved a CR, 1 achieved a PR and one patient has not been evaluated yet (but is in clinical response). Conclusion A significant proportion of patients experience prolonged remissions after anti-PD1 discontinuation and thus might be cured. Using a machine-learning algorithm, we identified biomarkers capable of predicting the risk of relapse after anti-PD1 discontinuation. These biomarkers are currently being validated in an independent set of patients. Finally, among patients who relapse after anti-PD1 discontinuation, re-treatment with anti-PD1 appears to be efficient. Disclosures Manson: Bristol Myers Squibb: Honoraria. Brice:Takeda France: Consultancy, Honoraria; Millennium Takeda: Research Funding; BMS: Honoraria. Herbaux:Janssen: Honoraria; BMS: Honoraria; Takeda: Honoraria; Abbvie: Honoraria; Gilead: Honoraria. Silva:Abbvie Inc: Consultancy; Celgene: Consultancy; Gilead Sciences: Consultancy, Research Funding; Janssen Cilag: Consultancy; Roche: Consultancy. Stamatoulas Bastard:Celgene: Honoraria; Takeda: Consultancy. Houot:Bristol Myers Squibb: Honoraria; Merck Sharp Dohme: Honoraria.


2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Utkarsh Upadhyay ◽  
Graham Lancashire ◽  
Christoph Moser ◽  
Manuel Gomez-Rodriguez

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.


2019 ◽  
Vol 622 ◽  
pp. A137 ◽  
Author(s):  
V. Bonjean ◽  
N. Aghanim ◽  
P. Salomé ◽  
A. Beelen ◽  
M. Douspis ◽  
...  

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.


2021 ◽  
Author(s):  
Renan M Costa ◽  
Vijay A Dharmaraj ◽  
Ryota Homma ◽  
Curtis L Neveu ◽  
William B Kristan ◽  
...  

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.


2013 ◽  
Vol 161 (7) ◽  
pp. 1755-1758 ◽  
Author(s):  
Elisabeth A. Keitges ◽  
Romela Pasion ◽  
Rachel D. Burnside ◽  
Carla Mason ◽  
Antonio Gonzalez-Ruiz ◽  
...  

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  

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