Hidden Markov model and Chapman Kolmogrov for protein structures prediction from images

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Hidden Markov model and Chapman Kolmogrov for protein structures prediction from images

Protein structure prediction and analysis are more significant for living organs to perfect asses the living organ functionalities. Several protein structure prediction methods use neural network (NN). However, the Hidden Markov model is more interpretable and effective for more biological data analysis compared to the NN. It employs statistical data analysis to enhance the prediction accuracy....

متن کامل

detecting gait phases from rgb-d images base on hidden markov model

gait contains important information about the status of the human body and physiological signs. in many medical applications, it isimportant to monitor and accurately analyze the gait of the patient. since walking shows the reproducibility signs in several phases,separating these phases can be used for the gait analysis. in this study, a method based on image processing for extracting phases of...

متن کامل

Hidden Markov Model for protein secondary structure

We address the problem of protein secondary structure prediction with Hidden Markov Models. A 21-state model is built using biological knowledge and statistical analysis of sequence motifs in regular secondary structures. Sequence family information is integrated via the combination of independent predictions of homologous sequences and a weighting scheme. Prediction accuracy with single sequen...

متن کامل

Hidden Markov Models for Images

We describe a method for learning statistical models of images using a second-order hidden Markov mesh model. We show that the Viterbi algorithm approach used for segmenting Markov chains can be extended to Markov meshes. The segmental k-means algorithm can then be applied to iteratively estimate the state transition matrix and the probability densities of the observations for the model. We als...

متن کامل

ذخیره 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: Computational Biology and Chemistry

سال: 2017

ISSN: 1476-9271

DOI: 10.1016/j.compbiolchem.2017.04.003