TACO: a general-purpose tool for predicting cell-type-specific transcription factor dimers
نویسندگان
چکیده
منابع مشابه
FactorNet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data
Due to the large numbers of transcription factors (TFs) and cell types, querying binding profiles of all TF/cell type pairs is not experimentally feasible, owing to constraints in time and resources. To address this issue, we developed a convolutional-recurrent neural network model, called FactorNet, to computationally impute the missing binding data. FactorNet trains on binding data from refer...
متن کاملCATCG: a general purpose parsing tool applied
This paper focuses on the language processing tool being developed at our centre and briefly describes two of its applications. CATCG, our morphosyntactic analyser, is designed to deal with general written Catalan text. In CATCG the whole processing task has been divided into specific subtasks and for each one of them we try to apply the best strategy available. The most relevant properties of ...
متن کاملA General Purpose Network Simulation Tool
This paper presents NESSY (NEtwork Simulation SYstem): a whole environment for performance evaluation of communication networks. The modeling methodology proposed by NESSY is mainly based upon the OO (ObjectOriented) techniques and the FDT (Formal Description Technique). With OO approach the network model is a collection of objects. FDT is used to describe the objects that model the network com...
متن کاملOntoVisT: A general purpose Ontological Visualization Tool
UNLABELLED Ontologies have emerged as a fast growing research topic in the area of semantic web during last decade. Currently there are 204 ontologies that are available through OBO Foundry and BioPortal. Several excellent tools for navigating the ontological structure are available, however most of them are dedicated to a specific annotation data or integrated with specific analysis applicatio...
متن کاملMaterial for Predicting tissue specific transcription factor binding sites
Our k-mer based PBM model (Figure 1a in main text) is motivated by the biophysics of TF binding to the probes in PBM experiments. Following Zhao et al. [1], we denote by Yi the experimentally measured intensity of the i-Th. probe on the PBM array. We denote by F (i) the (unobserved) binding probability of the TF to this probe. While these two quantities are related, due to experiential errors a...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: BMC Genomics
سال: 2014
ISSN: 1471-2164
DOI: 10.1186/1471-2164-15-208