Additional file 3: Table S7. of Systematic target function annotation of human transcription factors

The complete transcription factor annotation results. –Log10 (P value) are provided in parentheses following the target functions. (XLSX 153 kb

Additional file 4: Table S8. of Systematic target function annotation of human transcription factors

The complete list of TF pairs with significant target function overlaps but lower than expected target gene overlaps. Negative association (i.e., lower than expected target gene overlaps) of two TFs is defined as a negative Phi coefficient of the target gene overlaps of two TFs – TF1 and TF2. (XLSX 46 kb

Additional file 1: Table S1. of 3D deep convolutional neural networks for amino acid environment similarity analysis

3DCNN and MLP Network Architecture. Table summarizing the network architectures of 3DCNN and MLP. (DOCX 15 kb

Additional file 6: Table S11. of Systematic target function annotation of human transcription factors

The raw transcription factor–target gene (TFTG) relationships in GMT file format for 20,000bp window size. (GMT 1413 kb

Additional file 1: Supplementary Material. of Systematic target function annotation of human transcription factors

Supplementary Results, Methods, Figures (S1-S12), Tables (S1-S5, and S9). Tables S6, S7, S8, S10, S11 are available as separate files. Tables S10 and S11 correspond to the raw transcription factor–target gene (TFTG) relationships for 6000 and 20,000 windows, respectively, in GMT format [45, 47–49, 51–62, 71, 89, 90, 92–115, 119–121, 139–147, 155]. (DOCX 7176 kb

Additional file 2: Table S2. of 3D deep convolutional neural networks for amino acid environment similarity analysis

Individual and knowledge-based group classification accuracies of 3DCNN and MLP. Summary of the individual and knowledge-based group classification accuracies of 3DCNN and MLP. The deep 3DCNN achieves superior prediction performance compared to the MLP model, demonstrating the advantage of the deep 3D convolutional architecture over a simple flat neural network with the same input. (DOCX 12 kb

L'Auto-vélo : automobilisme, cyclisme, athlétisme, yachting, aérostation, escrime, hippisme / dir. Henri Desgranges

28 juillet 19321932/07/28 (A33,N11548)

Examples of Meta-Resources for Computational Biology.

<p>Summary comparing <i>iTools</i> to other similar meta-resources environments for archival and retrieval of software tools for computational biology.</p

A schematic and dynamic integration of <i>iTools</i> resources demonstrating interoperability of multi-disciplinary tools via graphical workflow environments.

<p>The three nodes with dash-boundaries on the <i>left</i> demonstrate schematically the integration of some computational biology tools. The graphical workflow on the <i>right</i> depicts the practical means of using <i>iTools</i> meta-data to construct module descriptions and generate multidisciplinary and heterogeneous data analysis protocols.</p

This figure illustrates the utilization of <i>iTools</i> for search, comparison and integration of bioinformatics tools.

<p>In this example, we demonstrate the use of the Basic Local Alignment Search Tool (BLAST) for comparing gene and protein sequences against other nucleic sequences available in various public databases. The <i>top row</i> shows <i>iTools</i> traversal and search (keyword = blast) using the hyperbolic graphical interface, and tools comparison and investigation of interoperability using the tabular resource view panel. The <i>bottom row</i> shows the design of a simple BLAST analysis workflow using one specific graphical workflow environment (LONI Pipeline). This BLAST analysis protocol depicts the NCBI DB formatting, index generation and filtering using <i>miBLAST</i>, sequence alignment and result textual visualization.</p