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Output of miR-29b Target Prediction using TargetScan
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Integrative regulatory network analysis results based on two network structure parameters (degree and betweenness centrality).
Integrative regulatory network analysis results based on two network structure parameters (degree and betweenness centrality).
Figshare2016-12-23 更新60
Master regulators and cofactors of human neuronal cell fate specification identified by CRISPR gene activation screens [CAS_TF_sgASCL1_Screen_AmpliconSeq]. Master regulators and cofactors of human neuronal cell fate specification identified by CRISPR gene activation screens [CAS_TF_sgASCL1_Screen_AmpliconSeq]
Technologies to reprogram cell-type specification have revolutionized the fields of regenerative medicine and disease modeling. Currently, the selection of fate-determining factors for cell reprogramm
NIAID Data Ecosystem60
Data_Sheet_3_Dynamic TF-lncRNA Regulatory Networks Revealed Prognostic Signatures in the Development of Ovarian Cancer.PDF
The pathological development of ovarian cancer (OC) is a complex progression that depends on multiple alterations of coding and non-coding genes. Therefore, it is important to capture the transcriptio
NIAID Data Ecosystem30
Additional file 2 of Prediction of differentially expressed microRNAs in blood as potential biomarkers for Alzheimer’s disease by meta-analysis and adaptive boosting ensemble learning
Additional file 2. The ABMDA results.
NIAID Data Ecosystem60
Punica granatum Transcriptome. Punica granatum
Fruits of 'Taishanhong' pomegranate at developmental stages S1, S2 and S3 were collected, of which mRNA from peels was sequenced by Illumina HiSeq 4000, respectively. Transcriptomes were used to eluci
NIAID Data Ecosystem40
