Results for “dhdna”

Detects DNS tunneling by computing Shannon entropy of DNS query names, analyzing query length distributions, inspecting TXT record payloads, and identifying high subdomain cardinality using scapy for packet capture analysis.

Provides Python bindings to interact with the DNAnexus platform, enabling file uploads, job management, and API calls.

Use when designing a Digital Down Converter (DDC) using dsphdl System objects. Triggers on requests involving DDC design, frequency down-conversion for FPGA/ASIC, NCO + mixer + decimation filter chains, fractional/non-integer sample rate conversion, or HDL-optimized receiver front-end signal processing.

Use when searching DNA or RNA queries against nucleotide sequence databases with HMMER's nucleotide homology search engine.

Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.

Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.

Detect anomalies in DNP3 protocol communications used in SCADA systems by monitoring for unauthorized control commands, firmware update attempts, protocol violations, and deviations from baseline traffic patterns using deep packet inspection and machine learning approaches.

Predict 3D binding poses of small molecule ligands to protein targets using diffusion-based molecular docking, supporting single complexes, batch processing, and virtual screening.

Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.

Use when converting RNA connectivity-table (`.ct`) files into extended FASTA with dot-bracket structures, optionally removing pseudoknots or modified bases.

Kit de ferramentas de quiminformática para controle molecular refinado. Análise de SMILES/SDF, descritores (MW, LogP, TPSA), fingerprints, busca de subestruturas, geração 2D/3D, similaridade, reações. Para fluxos de trabalho padrão com interface mais simples, use datamol (wrapper do RDKit). Use rdkit para controle avançado, sanitização customizada, algoritmos especializados.

Perform cheminformatics tasks including molecular I/O, descriptor calculation, fingerprinting, substructure search, and similarity analysis using the RDKit library.

Perform differential gene expression analysis for bulk RNA-seq data using PyDESeq2, supporting formulaic designs, Wald tests, FDR correction, LFC shrinkage, and result visualization.

Reverse engineer malware binaries using NSA's Ghidra disassembler and decompiler to understand internal logic, cryptographic routines, C2 protocols, and evasion techniques at the assembly and pseudo-C level.

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.

Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.

Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.

EL2N: Deep Learning on a Data Diet

Run differential gene expression analysis on bulk RNA-seq count matrices with PyDESeq2, the Python port of DESeq2 — size-factor normalization, dispersion estimation, Wald tests, FDR (Benjamini-Hochberg) correction, and volcano/MA plots. Use when identifying differentially expressed genes between conditions from raw bulk RNA-seq counts. Part of the AlterLab Academic Skills suite.

Comprehensive guide to dcgan. Master the concepts, implementation, best practices, and real-world applications of dcgan in professional environments.