DeepBind is an analysis method for predicting the sequence specificities of DNA- and RNA-binding proteins based on deep learning. DeepBind can analyze noisy experimental data to determine a set of DNA and RNA sequences to which the protein will bind. It can then look at a new sequence and calculate how likely it is that these proteins are bound to it. Given a sequence mutation, the tool can analyze whether a binding change is made. Protein-binding site mutations, additions or deletions can alter gene expression patterns and cause disease.Artificial Intelligence in DeepBind
Deep learning is a new field in machine learning research. Its motivation is to establish and simulate a neural network that simulates the human brain for analysis and learning. It mimics the mechanism of the human brain to interpret data, such as images, sounds, and text. Convolutional neural networ k(CNN) is currently one of the most widely used deep learning techniques. It is a deep neural network with a feature extractor (composed of a convolutional layer and a hybrid pool layer), which is popular in the field of computer vision. DeepBind, is based on deep learning techniques-deep convolutional neural networks (CNN), which offer a scalable, flexible and unified computational approach for pattern discovery.Analysis Process
Fig 1. Details of inner workings of DeepBind and its training procedure. (Alipanahi, B, et al. 2015)
For a sequence binding score f(s), DeepBind uses fore stages to compute the binding score:
There are several challenges in learning models of sequence specificity using modern high-throughput sequencing technologies, but DeepBind addresses most of the challenges.
DeepBind is based on a deep convolutional neural network, even if the position of the pattern in the sequence is unknown, new patterns can be discovered-traditional neural network tasks that require an exorbitant amount of training data. DeepBind can be applied to the following analysis fields:
CD ComputaBio provides sequence specificity analysis based on DeepBind method. DeepBind adapted deep learning methods CNN (convolutional neural networks) to the task of predicting sequence specificities and found that they compete favorably with the state of the art . For sequence specificity analysis, in addition to using DeepBind method for analysis, we can also use other analysis software or predictive model methods, including some of the most cutting-edge analysis models, according to customer needs. Protheragen provides one-stop data analysis services, you only need to upload raw sequencing data, and we will use DeepBind method to analyze data and generate a complete analysis result report for you. For DeepBind method analysis, if you have any questions, please feel free to contact us for details. We will provide you with satisfactory data analysis services.
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