A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data.
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2016 update on APBioNet's annual international conference on bioinformatics (InCoB)Computer-aided biomarker discovery for precision medicine: data resources, models and applications.Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.The Human Cell Atlas: Technical approaches and challenges.
P2860
A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data.
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2016 nî lūn-bûn
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2016 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած
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2016 թվականի դեկտեմբերին հրատարակված գիտական հոդված
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2016年の論文
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2016年論文
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2016年論文
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2016年論文
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2016年論文
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2016年論文
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2016年论文
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name
A machine learning approach fo ...... ngle-cell transcriptomic data.
@ast
A machine learning approach fo ...... ngle-cell transcriptomic data.
@en
type
label
A machine learning approach fo ...... ngle-cell transcriptomic data.
@ast
A machine learning approach fo ...... ngle-cell transcriptomic data.
@en
prefLabel
A machine learning approach fo ...... ngle-cell transcriptomic data.
@ast
A machine learning approach fo ...... ngle-cell transcriptomic data.
@en
P2093
P2860
P921
P1433
P1476
A machine learning approach fo ...... ngle-cell transcriptomic data.
@en
P2093
Hiroaki Kitano
Hui Peng Li
Lawrence Jin Kiat Wee
Samik Ghosh
See Kiong Ng
Shyam Prabhakar
Takeshi Hase
P2860
P2888
P356
10.1186/S12864-016-3317-7
P407
P433
P577
2016-12-22T00:00:00Z
P6179
1033219559