An integrated approach for identification of exon locations using recursive Gauss Newton tuned adaptive Kaiser window

Speed Efficient Fast Fourier Transform for Signal Processing of Nucleotides to Detect Diabetic Retinopathy Using Machine Learning

Diabetic Retinopathy (DR) is a complicated disease of diabetes, which specifically affects the retina. The human-intensive analysis mechanism of DR infected retina are likely to diagnose wrongly compared to computer-intensive diagnosis systems. In this paper, in order to aid the computer based approach for the diagnosis of DR, a model based on machine learning algorithm is proposed. The nucleotides of the human retina are processed with the help of signal processing methodologies. A speed efficient Fast Fourier transform is proposed to work out the FFT of huge amount of samples with higher pace. The improvement in speed is achieved in 98% of the samples. The prediction parameters, derived from these samples are utilized to classify the healthy retina sequence and an infected retina. In this study, Fine Tree, KNN Fine, Weighted KNN, Ensemble Bagged Trees and Ensemble Subspace KNN classifiers are employed to build the models. The simulated results using MATLAB software show that the accuracy is 98% which is better than image processing based methods which were used earlier. The performance parameters such as sensitivity and specificity are determined for each model. The faithfulness of the model is studied by deriving the ROC Curve.

DNA numerical encoding schemes for exon prediction: a recent history

Bioinformatics in the present day has been firmly established as a regulator in genomics. In recent times, applications of Signal processing in exon prediction have gained a lot of attention. The exons carry protein information. Proteins are composed of connected constituents known as amino acids that characterize the specific function. Conversion of the nucleotide character string into a numerical sequence is the gateway before analyzing it through signal processing methods. This numeric encoding is the mathematical descriptor of nucleotides and is based on some statistical properties of the structure of nucleic acids. Since the type of encoding extremely affects the exon detection accuracy, this paper is devised for the review of existing encoding (mapping) schemes. The comparative analysis is formulated to emphasize the importance of the genetic code setting of amino acids considered for application related to computational elucidation for exon detection. This work covers much helpful information for future applications.

Detection of exon location in eukaryotic DNA using a fuzzy adaptive Gabor wavelet transform

The existing model-independent methods for the detection of exons in DNA could not prove to be ideal as commonly employed fixed window length strategy produces spectral leakage causing signal noise The Modified-Gabor-wavelet-transform exploits a multiscale strategy to deal with the issue to some extent. Yet, no rule regarding the occurrence of small and large exons has been specified. To overcome this randomness, scaling-factor of GWT has been adapted based on a fuzzy rule. Due to the nucleotides' genetic code and fuzzy behaviors in DNA configuration, this work could adopt the fuzzy approach. Two fuzzy membership functions (large and small) take care of the variation in the coding regions. The fuzzy-based learning parameter adaptively tunes the scale factor for fast and precise prediction of exons. The proposed approach has an immense plus point of being capable of isolating detailed sub-regions in each exon efficiently proving its efficacy comparing with existing techniques.

Hereditary disease prediction in eukaryotic DNA: an adaptive signal processing approach

Hereditary disease prediction in eukaryotic DNA using signal processing approaches is an incredible work in bioinformatics. Researchers of various fields are trying to put forth a noninvasive approach to forecast the disease-related genes. As diseased genes are more random than the healthy ones, in this work, a comparison of the diseased gene is made against the healthy ones. An adaptive signal processing method like functional link artificial neural network-based Levenberg-Marquardt filter has been proposed in this regard. For parameter upgradation, the algorithm is modified using particle swarm optimization. Here, disease genes are discriminated from healthy ones based on the magnitude of mean square error (MSE), which is calculated through the adaptive filter. The performance of the algorithm is inspected by computing some evaluation parameters. Since accuracy is the prime concern, authors in this work have taken an attempt to improve the accuracy level compared to the existing methods. Taking the reference gene as healthy, the overall process is accomplished by categorizing the diseased and healthy targets with MSE value at a threshold of 0.012. The proposed technique predicts the test gene sets successfully.

Identification of CpG Islands in DNA Sequences Using Short-Time Fourier Transform

In the era of big data analysis, genomics data analysis is highly needed to extract the hidden information present in the DNA sequences. One of the important hidden features present in the DNA sequences is CpG islands. CpG Islands are important as these are used as gene markers and also these are associated with cancer etc. Therefore, various methods have been reported for the identification of CpG islands in DNA sequences. The key contributions of this work are (i) extraction of the periodicity feature associated with CpG islands using Short-time Fourier transform (ii) a short-time Fourier transform-based algorithm has been proposed for the identification of CpG Islands in DNA sequences. The results of the proposed algorithm amply demonstrate its better performance as compared to other reported methods on CpG islands detection.