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Items: 1 to 20 of 66

1.

Labeling images with facial emotion and the potential for pediatric healthcare.

Kalantarian H, Jedoui K, Washington P, Tariq Q, Dunlap K, Schwartz J, Wall DP.

Artif Intell Med. 2019 Jul;98:77-86. doi: 10.1016/j.artmed.2019.06.004. Epub 2019 Jul 6.

PMID:
31521254
2.

The Potential for Machine Learning-Based Wearables to Improve Socialization in Teenagers and Adults With Autism Spectrum Disorder-Reply.

Voss C, Haber N, Wall DP.

JAMA Pediatr. 2019 Sep 9. doi: 10.1001/jamapediatrics.2019.2969. [Epub ahead of print] No abstract available.

PMID:
31498377
3.

Inherited and De Novo Genetic Risk for Autism Impacts Shared Networks.

Ruzzo EK, Pérez-Cano L, Jung JY, Wang LK, Kashef-Haghighi D, Hartl C, Singh C, Xu J, Hoekstra JN, Leventhal O, Leppä VM, Gandal MJ, Paskov K, Stockham N, Polioudakis D, Lowe JK, Prober DA, Geschwind DH, Wall DP.

Cell. 2019 Aug 8;178(4):850-866.e26. doi: 10.1016/j.cell.2019.07.015.

4.

Exploratory study examining the at-home feasibility of a wearable tool for social-affective learning in children with autism.

Daniels J, Schwartz JN, Voss C, Haber N, Fazel A, Kline A, Washington P, Feinstein C, Winograd T, Wall DP.

NPJ Digit Med. 2018 Aug 2;1:32. doi: 10.1038/s41746-018-0035-3. eCollection 2018.

5.

Identification and Quantification of Gaps in Access to Autism Resources in the United States: An Infodemiological Study.

Ning M, Daniels J, Schwartz J, Dunlap K, Washington P, Kalantarian H, Du M, Wall DP.

J Med Internet Res. 2019 Jul 10;21(7):e13094. doi: 10.2196/13094.

6.

Effect of Wearable Digital Intervention for Improving Socialization in Children With Autism Spectrum Disorder: A Randomized Clinical Trial.

Voss C, Schwartz J, Daniels J, Kline A, Haber N, Washington P, Tariq Q, Robinson TN, Desai M, Phillips JM, Feinstein C, Winograd T, Wall DP.

JAMA Pediatr. 2019 May 1;173(5):446-454. doi: 10.1001/jamapediatrics.2019.0285.

7.

Coalitional Game Theory Facilitates Identification of Non-Coding Variants Associated With Autism.

Sun MW, Gupta A, Varma M, Paskov KM, Jung JY, Stockham NT, Wall DP.

Biomed Inform Insights. 2019 Mar 8;11:1178222619832859. doi: 10.1177/1178222619832859. eCollection 2019.

8.

A Low Rank Model for Phenotype Imputation in Autism Spectrum Disorder.

Paskov KM, Wall DP.

AMIA Jt Summits Transl Sci Proc. 2018 May 18;2017:178-187. eCollection 2018.

9.

Machine learning approach for early detection of autism by combining questionnaire and home video screening.

Abbas H, Garberson F, Glover E, Wall DP.

J Am Med Inform Assoc. 2018 Aug 1;25(8):1000-1007. doi: 10.1093/jamia/ocy039.

PMID:
29741630
10.

Brain-specific functional relationship networks inform autism spectrum disorder gene prediction.

Duda M, Zhang H, Li HD, Wall DP, Burmeister M, Guan Y.

Transl Psychiatry. 2018 Mar 6;8(1):56. doi: 10.1038/s41398-018-0098-6.

11.

Feasibility Testing of a Wearable Behavioral Aid for Social Learning in Children with Autism.

Daniels J, Haber N, Voss C, Schwartz J, Tamura S, Fazel A, Kline A, Washington P, Phillips J, Winograd T, Feinstein C, Wall DP.

Appl Clin Inform. 2018 Jan;9(1):129-140. doi: 10.1055/s-0038-1626727. Epub 2018 Feb 21.

12.

Sparsifying machine learning models identify stable subsets of predictive features for behavioral detection of autism.

Levy S, Duda M, Haber N, Wall DP.

Mol Autism. 2017 Dec 19;8:65. doi: 10.1186/s13229-017-0180-6. eCollection 2017.

13.

The GapMap project: a mobile surveillance system to map diagnosed autism cases and gaps in autism services globally.

Daniels J, Schwartz J, Albert N, Du M, Wall DP.

Mol Autism. 2017 Oct 23;8:55. doi: 10.1186/s13229-017-0163-7. eCollection 2017.

14.

Human Genome Sequencing at the Population Scale: A Primer on High-Throughput DNA Sequencing and Analysis.

Goldfeder RL, Wall DP, Khoury MJ, Ioannidis JPA, Ashley EA.

Am J Epidemiol. 2017 Oct 15;186(8):1000-1009. doi: 10.1093/aje/kww224. Review.

15.

GapMap: Enabling Comprehensive Autism Resource Epidemiology.

Albert N, Daniels J, Schwartz J, Du M, Wall DP.

JMIR Public Health Surveill. 2017 May 4;3(2):e27. doi: 10.2196/publichealth.7150.

16.

Cross-disorder comparative analysis of comorbid conditions reveals novel autism candidate genes.

Diaz-Beltran L, Esteban FJ, Varma M, Ortuzk A, David M, Wall DP.

BMC Genomics. 2017 Apr 20;18(1):315. doi: 10.1186/s12864-017-3667-9.

17.

Refining the role of de novo protein-truncating variants in neurodevelopmental disorders by using population reference samples.

Kosmicki JA, Samocha KE, Howrigan DP, Sanders SJ, Slowikowski K, Lek M, Karczewski KJ, Cutler DJ, Devlin B, Roeder K, Buxbaum JD, Neale BM, MacArthur DG, Wall DP, Robinson EB, Daly MJ.

Nat Genet. 2017 Apr;49(4):504-510. doi: 10.1038/ng.3789. Epub 2017 Feb 13.

18.

MC-GenomeKey: a multicloud system for the detection and annotation of genomic variants.

Elshazly H, Souilmi Y, Tonellato PJ, Wall DP, Abouelhoda M.

BMC Bioinformatics. 2017 Jan 20;18(1):49. doi: 10.1186/s12859-016-1454-2.

19.

DE NOVO MUTATIONS IN AUTISM IMPLICATE THE SYNAPTIC ELIMINATION NETWORK.

Venkataraman GR, O'Connell C, Egawa F, Kashef-Haghighi D, Wall DP.

Pac Symp Biocomput. 2017;22:521-532. doi: 10.1142/9789813207813_0048.

20.

The Quantified Brain: A Framework for Mobile Device-Based Assessment of Behavior and Neurological Function.

Stark DE, Kumar RB, Longhurst CA, Wall DP.

Appl Clin Inform. 2016 May 4;7(2):290-8. doi: 10.4338/ACI-2015-12-LE-0176. eCollection 2016. No abstract available.

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