An Evaluation of Peak Finding for DVR Classification of Biological Data

2012 ◽  
pp. 91-106
Author(s):  
Aaron Knoll ◽  
Rolf Westerteiger ◽  
Hans Hagen
Keyword(s):  
Biological Data ◽  
Peak Finding

Alzheimer's Disease Classification Based on Multi-feature Fusion

2019 ◽  
Vol 15 (2) ◽  
pp. 161-169 ◽  
Author(s):  
Nuwan Madusanka ◽  
Heung-Kook Choi ◽  
Jae-Hong So ◽  
Boo-Kyeong Choi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Fusion ◽  
Biological Data ◽  
Correct Classification ◽  
Mr Images ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Morphometric Features

Background: In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer’s Disease (AD). Methods: In particular, we classified subjects with Alzheimer’s disease, Mild Cognitive Impairment (MCI) and Normal Control (NC) based on texture and morphometric features. Currently, neuropsychiatric categorization provides the ground truth for AD and MCI diagnosis. This can then be supported by biological data such as the results of imaging studies. Cerebral atrophy has been shown to correlate strongly with cognitive symptoms. Hence, Magnetic Resonance (MR) images of the brain are important resources for AD diagnosis. In the proposed method, we used three different types of features identified from structural MR images: Gabor, hippocampus morphometric, and Two Dimensional (2D) and Three Dimensional (3D) Gray Level Co-occurrence Matrix (GLCM). The experimental results, obtained using a 5-fold cross-validated Support Vector Machine (SVM) with 2DGLCM and 3DGLCM multi-feature fusion approaches, indicate that we achieved 81.05% ±1.34, 86.61% ±1.25 correct classification rate with 95% Confidence Interval (CI) falls between (80.75-81.35) and (86.33-86.89) respectively, 83.33%±2.15, 84.21%±1.42 sensitivity and 80.95%±1.52, 85.00%±1.24 specificity in our classification of AD against NC subjects, thus outperforming recent works found in the literature. For the classification of MCI against AD, the SVM achieved a 76.31% ± 2.18, 78.95% ±2.26 correct classification rate, 75.00% ±1.34, 76.19%±1.84 sensitivity and 77.78% ±1.14, 82.35% ±1.34 specificity. Results and Conclusion: The results of the third experiment, with MCI against NC, also showed that the multiclass SVM provided highly accurate classification results. These findings suggest that this approach is efficient and may be a promising strategy for obtaining better AD, MCI and NC classification performance.

scholarly journals Optimized artificial neural network for classification of biological data

2018 ◽  
Vol 7 (2) ◽  
pp. 817
Author(s):  
Senthilselvan Natarajan ◽  
Rajarajan S ◽  
Subramaniyaswamy V
Keyword(s):  
Neural Network ◽  
Mental Stress ◽  
Causes Of Death ◽  
Biological Data ◽  
High Dimensionality ◽  
Soft Computing Techniques ◽  
Artificial Neural ◽  
Multi Class Classification ◽  
Neural Networks Optimization

Biological data suffers from the problem of high dimensionality which makes the process of multi-class classification difficult and also these data have elements that are incomplete and redundant. Breast Cancer is currently one of the most pre-dominant causes of death in women around the globe. The current methods for classifying a tumour as malignant or benign involve physical procedures. This often leads to mental stress. Research has now sought to implement soft computing techniques in order to classify tumours based on the data available. In this paper, a novel classifier model is implemented using Artificial Neural Networks. Optimization is done in this neural network by using a meta-heuristic algorithm called the Whale Swarm Algorithm in order to make the classifier model accurate. Experimental results show that new technique outperforms other existing models.

Classification of Biological Sequences

Data Mining ◽  
2013 ◽  
pp. 1019-1042
Author(s):  
Pratibha Rani ◽  
Vikram Pudi
Keyword(s):  
Naive Bayes ◽  
Analysis Data ◽  
Naïve Bayes ◽  
Biological Data ◽  
Rapid Progress ◽  
Biological Data Analysis ◽  
Depth Analysis ◽  
Iterative Scaling ◽  
Mining Methods

The rapid progress of computational biology, biotechnology, and bioinformatics in the last two decades has led to the accumulation of tremendous amounts of biological data that demands in-depth analysis. Data mining methods have been applied successfully for analyzing this data. An important problem in biological data analysis is to classify a newly discovered sequence like a protein or DNA sequence based on their important features and functions, using the collection of available sequences. In this chapter, we study this problem and present two Bayesian classifiers RBNBC (Rani & Pudi, 2008a) and REBMEC (Rani & Pudi, 2008c). The algorithms used in these classifiers incorporate repeated occurrences of subsequences within each sequence (Rani, 2008). Specifically, Repeat Based Naive Bayes Classifier (RBNBC) uses a novel formulation of Naive Bayes, and the second classifier, Repeat Based Maximum Entropy Classifier (REBMEC) uses a novel framework based on the classical Generalized Iterative Scaling (GIS) algorithm.

On Eigen-matrix translation method for classification of biological data

2015 ◽  
Vol 28 (5) ◽  
pp. 1212-1230
Author(s):  
Hao Jiang ◽  
Yushan Qiu ◽  
Xiaoqing Cheng ◽  
Waiki Ching
Keyword(s):  
Biological Data ◽  
Translation Method

LOGIC FORMULAS BASED KNOWLEDGE DISCOVERY AND ITS APPLICATION TO THE CLASSIFICATION OF BIOLOGICAL DATA

BIOMAT 2008 ◽  
2009 ◽  
Author(s):  
G. FELICI ◽  
P. BERTOLAZZI ◽  
M. R. GUARRACINO ◽  
A. CHINCHULUUN ◽  
P. M. PARDALOS
Keyword(s):  
Knowledge Discovery ◽  
Biological Data

scholarly journals ClassiPhages 2.0: Sequence-based classification of phages using Artificial Neural Networks

2019 ◽  
Author(s):  
Cynthia Maria Chibani ◽  
Florentin Meinecke ◽  
Anton Farr ◽  
Sascha Dietrich ◽  
Heiko Liesegang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Markov Models ◽  
International Committee ◽  
Biological Data ◽  
Sequencing Technologies ◽  
Artificial Neural ◽  
Taxonomic Markers ◽  
Artificial Neural Network Ann

AbstractBackground/ MotivationIn the era of affordable next generation sequencing technologies we are facing an exploding amount of new phage genome sequences. This requests high throughput phage classification tools that meet the standards of the International Committee on Taxonomy of Viruses (ICTV). However, an accurate prediction of phage taxonomic classification derived from phage sequences still poses a challenge due to the lack of performant taxonomic markers. Since machine learning methods have proved to be efficient for the classification of biological data we investigated how artificial neural networks perform on the task of phage taxonomy.ResultsIn this work, 5,920 constructed and refined profile Hidden Markov Models (HMMs), derived from 8,721 phage sequences classified into 12 well known phage families, were used to scan phage proteome datasets. The resulting Phage Family-proteome to Phage-derived-HMMs scoring matrix was used to develop and train an Artificial Neural Network (ANN) to find patterns for phage classification into one of the phage families. Results show that using the 100 fold cross-validation test, the proposed method achieved an overall accuracy of 84.18 %. The ANN was tested on a set of unclassified phages and resulted in a taxonomic prediction. The ANN prediction was benchmarked against the prediction resulting of multi-HMM hits, and showed that the ANN performance is dependent on the quality of the input matrix.ConclusionsWe believe that, as long as some phage families on public databases are underrepresented, multi-HMM hits can be used as a classification method to populate those phage families, which in turn will improve the performance and accuracy of the ANN. We believe that the proposed method is an effective and promising method for phage classification. The good performance of the ANN and HMM based predictor indicates the efficiency of the method for phage classification, where we foresee its improvement with an increasing number of sequenced viral genomes.

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