Displaying 1 to 9 from 9 results

yeti - Your Everyday Threat Intelligence

  •    Python

Yeti is a platform meant to organize observables, indicators of compromise, TTPs, and knowledge on threats in a single, unified repository. Yeti will also automatically enrich observables (e.g. resolve domains, geolocate IPs) so that you don't have to. Yeti provides an interface for humans (shiny Bootstrap-based UI) and one for machines (web API) so that your other tools can talk nicely to it. Yeti was born out of frustration of having to answer the question "where have I seen this artifact before?" or Googling shady domains to tie them to a malware family.

CENTIPEDE.tutorial - :bug: How to use CENTIPEDE to determine if a transcription factor is bound.

  •    R

CENTIPEDE fits a bayesian hierarchical mixture model to learn TF-specific distribution of experimental data on a particular cell-type for a set of candidate binding sites described by a motif. This is a practical tutorial for running CENTIPEDE with DNase-Seq data. It explains how to prepare the data and how to run the analysis. The goal is to predict if a putative transcription factor binding site is actually bound or not. For details about the statistical models underlying the methods, please see (Pique-Regi, et al. 2011).

snpsea - :bar_chart: Identify cell types and pathways affected by genetic risk loci.

  •    C++

SNPsea is an algorithm to identify cell types and pathways likely to be affected by risk loci. It requires a list of SNP identifiers and a matrix of genes and conditions.

go_enrichment - Transcripts annotation and GO enrichment Fisher tests

  •    Python

go_enrichment annotates transcript sequences and performs GO enrichment Fisher tests. The transcript sequences are blasted against the swissprot protein database and the uniprot information corresponding to the hit is retrieved from the uniprot website. Fisher tests are performed with the goatools Python module. If you do not have administrator rights on the computer you will be using or have little experience compiling, installing and adding programs to your PATH environment variable, you will potentially need to ask an administrator to install the following programs and databases.

watson-discovery-food-reviews - Combine Watson Knowledge Studio and Watson Discovery to discover customer sentiment from product reviews

  •    Javascript

In this Code Pattern, we walk you through a working example of a web application that queries and manipulates data from the Watson Discovery Service. With the aid of a custom model built with Watson Knowledge studio, the data will have additional enrichments that will provide improved insights for user analysis. This web app contains multiple UI components that you can use as a starting point for developing your own Watson Discovery and Knowledge Studio service applications.

Cortex-Analyzers - Cortex Analyzers Repository

  •    Python

The following repository is used by TheHive Project to develop and store Cortex analyzers. Analyzers can be written in any programming language supported by Linux such as Python, Ruby, Perl, etc. Refer to the How to Write and Submit an Analyzer page for details on how to write and submit one.

pyeti - Python bindings for Yeti's API

  •    Python

$ python setup.py install should get you started. After this gets a little more maturity, we will submit it to Pypy for usage with pip. First thing is to import the library and instantiate a client.