 Connection
Farnoush Banaei-Kashani to Humans
This is a "connection" page, showing publications Farnoush Banaei-Kashani has written about Humans.
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Connection Strength |
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0.056 |
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Konz L, Hill A, Banaei-Kashani F. ST-DeepGait: A Spatiotemporal Deep Learning Model for Human Gait Recognition. Sensors (Basel). 2022 Oct 21; 22(20).
Score: 0.023
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Hill AC, Guo C, Litkowski EM, Manichaikul AW, Yu B, Konigsberg IR, Gorbet BA, Lange LA, Pratte KA, Kechris KJ, DeCamp M, Coors M, Ortega VE, Rich SS, Rotter JI, Gerzsten RE, Clish CB, Curtis JL, Hu X, Obeidat ME, Morris M, Loureiro J, Ngo D, O'Neal WK, Meyers DA, Bleecker ER, Hobbs BD, Cho MH, Banaei-Kashani F, Bowler RP. Large scale proteomic studies create novel privacy considerations. Sci Rep. 2023 06 07; 13(1):9254.
Score: 0.006
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Barrett CD, Suzuki Y, Hussein S, Garg L, Tumolo A, Sandhu A, West JJ, Zipse M, Aleong R, Varosy P, Tzou WS, Banaei-Kashani F, Rosenberg MA. Evaluation of Quantitative Decision-Making for Rhythm Management of Atrial Fibrillation Using Tabular Q-Learning. J Am Heart Assoc. 2023 05 02; 12(9):e028483.
Score: 0.006
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Zhuang Y, Xing F, Ghosh D, Hobbs BD, Hersh CP, Banaei-Kashani F, Bowler RP, Kechris K. Deep learning on graphs for multi-omics classification of COPD. PLoS One. 2023; 18(4):e0284563.
Score: 0.006
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Gillenwater LA, Helmi S, Stene E, Pratte KA, Zhuang Y, Schuyler RP, Lange L, Castaldi PJ, Hersh CP, Banaei-Kashani F, Bowler RP, Kechris KJ. Multi-omics subtyping pipeline for chronic obstructive pulmonary disease. PLoS One. 2021; 16(8):e0255337.
Score: 0.005
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Lusk R, Stene E, Banaei-Kashani F, Tabakoff B, Kechris K, Saba LM. Aptardi predicts polyadenylation sites in sample-specific transcriptomes using high-throughput RNA sequencing and DNA sequence. Nat Commun. 2021 03 12; 12(1):1652.
Score: 0.005
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Marzec L, Raghavan S, Banaei-Kashani F, Creasy S, Melanson EL, Lange L, Ghosh D, Rosenberg MA. Device-measured physical activity data for classification of patients with ventricular arrhythmia events: A pilot investigation. PLoS One. 2018; 13(10):e0206153.
Score: 0.004
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Connection Strength
The connection strength for concepts is the sum of the scores for each matching publication.
Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.
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