Đang chuẩn bị liên kết để tải về tài liệu:
Sequence information gain based motif analysis

Kim Thư 43 13 pdf

Đang chuẩn bị nút TẢI XUỐNG, xin hãy chờ Tải xuống

The detection of regulatory regions in candidate sequences is essential for the understanding of the regulation of a particular gene and the mechanisms involved. This paper proposes a novel methodology based on information theoretic metrics for finding regulatory sequences in promoter regions. |

TÀI LIỆU LIÊN QUAN

Expressed sequence tag-simple sequence repeat (EST-SSR) marker resources for genetic diversity analysis in different varieties of roses (Rosa spp.)

VADR: Validation and annotation of virus sequence submissions to GenBank

FLEXc: Protein flexibility prediction using context-based statistics, predicted structural features, and sequence information

Multidimensional mutual information methods for the analysis of covariation in multiple sequence alignments

A computational method to predict topologically associating domain boundaries combining histone Marks and sequence information

Sequence information gain based motif analysis

RPI-SE: A stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information

Parametric bootstrapping for biological sequence motifs

MSAIndelFR: A scheme for multiple protein sequence alignment using information on indel flanking regions

Summary of Computer doctoral thesis: Mining weighted sequential patterns in sequence database

Đã phát hiện trình chặn quảng cáo AdBlock

Trang web này phụ thuộc vào doanh thu từ số lần hiển thị quảng cáo để tồn tại. Vui lòng tắt trình chặn quảng cáo của bạn hoặc tạm dừng tính năng chặn quảng cáo cho trang web này.