Upcoming Thematic Issue – Entropy Measures for Medical Image Analysis – Current Bioinformatics

CM- THEMATIC FLYER -Dr. K. Kamalanand

http://benthamscience.com/journals/current-bioinformatics/special-issues/#top

Upcoming Thematic Issue – Entropy Measure for Medical Image Analysis– Current Bioinformatics

CM- THEMATIC FLYER -Dr. K. Kamalanand

http://benthamscience.com/journals/current-bioinformatics/

New Issue :: Current Bioinformatics 12, Issue 2

Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth reviews, drug clinical trial studies and guest edited thematic issues written by leaders in the field, covering a wide range of the integration of biology with computer and information science.

The journal focuses on reviews on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.

Current Bioinformatics is an essential journal for all academic and industrial researchers who want expert knowledge on all major advances in bioinformatics.

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Articles from the journal Current Bioinformatics 12, Issue 2:

For details on the articles, please visit this link :: http://bit.ly/2plPUKB

 

Upcoming Thematic Issue – Latest machine learning Techniques for Biomedicine and Bioinformatics

CB-THEMATIC FLYER -Quan Zou

http://www.eurekaselect.com/642/journal/current-bioinformatics

Upcoming Thematic Issue – Optimization in design of Natural Structures, Biomaterials, Bioinformatics and Biometric techniques for solving Physiological needs and ultimate performance of Bio-Devices

CB-THEMATIC FLYER -Dr Kelvin KL Wong

http://www.eurekaselect.com/642/journal/current-bioinformatics

Upcoming Thematic Issue – Bioinformatics in Biological Big Data Era

CB- THEMATIC FLYER- Shuigeng Zhou-1

http://benthamscience.com/journals/current-bioinformatics/

 

Upcoming Thematic Issue – Plant Bioinformatics: From Genome to Phenome

cb-thematic-flyer-ming-chen

http://www.eurekaselect.com/642/journal/current-bioinformatics

New Issue :: Current Bioinformatics 12, Issue 1

Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth reviews, drug clinical trial studies and guest edited thematic issues written by leaders in the field, covering a wide range of the integration of biology with computer and information science.

The journal focuses on reviews on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.

Current Bioinformatics is an essential journal for all academic and industrial researchers who want expert knowledge on all major advances in bioinformatics.

cbio

Articles from the journal Current Bioinformatics 12, Issue 1:

For details on the articles, please visit this link :: http://bit.ly/2jYq9Os

Highlighted Article Flyer for the journal “Current Bioinformatics”

cb-articles_11-2016-md-tamjidul-hoque

http://benthamscience.com/journals/current-bioinformatics/

Article by Disease-“Suitability of Sequence-Based Feature Vector for Classification Algorithm Improves Accuracy of Human Protein-Protein Interaction Prediction: A Red Blood Cell Case Study”

Article by Disease on “Haematology

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Abstract: To classify human protein-protein interaction information and consolidate existing data, supervised learning algorithms are implemented. These algorithms require a feature vector to generate a prediction model and feature vectors could be constructed based on various input data. The suitability of feature vector for classification algorithm results in a more predictive model and predictions with higher accuracies based on low-dimension vectors. To investigate the proper combination of feature sets and the algorithms, three feature vectors including AA Frequency, AA Graphical Parameter, and AA Triplex based on the sole knowledge of primary structure of human red blood cell proteins were constructed and then applied to five different classification methods. The results indicated that support vector machine (SVM) algorithm produced the highest accuracy of 84.65% with AA Graphical Parameter feature set while it reached accuracy of 80.65% with AA Triplex feature set. Random forest (RF) achieved high accuracy of 83.69% with all three feature sets on average. Bayesian classifier of TAN performed better than NB using all three features. Artificial neural network (ANN) classifier demonstrated the lowest average accuracy of 76%; however, the performance was comparable with TAN where AA triplex learning feature was used with the accuracy of 77.90%. These figures demonstrated that selecting an appropriate feature set for a classification task results in a higher accuracy with the advantage of utilizing low-dimension feature vectors constructed from more simple data.

Read more: http://benthamscience.com/journals/current-bioinformatics/volume/11/issue/2/page/291/