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The current focus of many functional genomic studies is on the elucidation of gene regulatory networks. The functional analyses of transcription factors and their DNA-binding sites, in conjunction with genome-wide expression profiling, are crucial in understanding of gene regulatory networks. This paper describes an efficient and easy method for characterizing the DNA-binding sequence specificity of novel plant transcription factors. This new method is based on the fusion of a DNA-binding protein (DBP) to 6×His-tagged cellulase D (CELD), which serves both as a means for affinity purification of DBP-DNA complex in the selection of binding sites from a pool of biotinylated random-sequence oligonucleotides and as a reporter for measurement of DNA-binding activity. Thus, it eliminates the use of radioactivity and gel electrophoresis techniques currently used for purification of DBP-DNA complexes and assays of DNA-binding activity. The effectiveness of this method was demonstrated by the success of simultaneous selection of the binding sites of nine plant DBPs from four superfamilies (AP2, bHLH, NAC and MYB). The high-throughput capacity of CELD-based DNA-binding assays allows the quantitative analysis of the binding sequence specificity from a large number of DBP-selected oligonucleotides. The binding sequence specificity of three novel transcription factors (rice OsbHLH66, wheat TaNAC69 and TaMYB80), determined with this method, is presented. This new method provides the capacity of high-throughput analysis on the DNA-binding sequence specificity of a large number of putative transcription factors, predicted on the basis of conserved DNA-binding domains. [ABSTRACT FROM AUTHOR]

Breast cancer is the second leading cause of death in the world. Breast cancer research is focused towards its early prediction, diagnosis, and prognosis. Breast cancer can be predicted on omics profiles, clinical tests, and pathological images. The omics profiles comprise of genomic, proteomic, and transcriptomic profiles that are available as high‐dimensional datasets. Survival prediction is carried out on omics data to predict early the onset of disease, relapse, reoccurrence of diseases, and biomarker identification. The early prediction of breast cancer is desired for the effective treatment of patients as delay can aggravate the staging of cancer. In this study, extreme learning machine (ELM) based model for breast cancer survival prediction named eBreCaP is proposed. It integrates the genomic (gene expression, copy number alteration, DNA methylation, protein expression) and pathological image datasets; and trains them using an ensemble of ELM with the six best‐chosen models suitable to be applied on integrated data. eBreCaP has been evaluated on nine performance parameters, namely sensitivity, specificity, precision, accuracy, Matthews correlation coefficient, area under curve, area under precision–recall, hazard ratio, and concordance Index. eBreCaP has achieved an accuracy of 85% for early breast cancer survival prediction using the ensemble of ELM with gradient boosting. [ABSTRACT FROM AUTHOR]