Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data.
about
EP-DNN: A Deep Neural Network-Based Global Enhancer Prediction AlgorithmTaking promoters out of enhancers in sequence based predictions of tissue-specific mammalian enhancersIn silico identification of enhancers on the basis of a combination of transcription factor binding motif occurrencesComputational schemes for the prediction and annotation of enhancers from epigenomic assays.An annotation agnostic algorithm for detecting nascent RNA transcripts in GRO-seq.Supervised learning method for predicting chromatin boundary associated insulator elements.
P2860
Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data.
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2013 nî lūn-bûn
@nan
2013 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2013 թվականի դեկտեմբերին հրատարակված գիտական հոդված
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2013年の論文
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name
Active enhancer positions can ...... ollective sequence motif data.
@ast
Active enhancer positions can ...... ollective sequence motif data.
@en
type
label
Active enhancer positions can ...... ollective sequence motif data.
@ast
Active enhancer positions can ...... ollective sequence motif data.
@en
prefLabel
Active enhancer positions can ...... ollective sequence motif data.
@ast
Active enhancer positions can ...... ollective sequence motif data.
@en
P2860
P50
P1433
P1476
Active enhancer positions can ...... ollective sequence motif data.
@en
P2093
Agnieszka Podsiadło
Mariusz Wrzesień
P2860
P2888
P356
10.1186/1752-0509-7-S6-S16
P478
P577
2013-12-13T00:00:00Z
P5875
P6179
1013491565