Exon Prediction Research Articles

Splice site recognition - deciphering Exon-Intron transitions for genetic insights using Enhanced integrated Block-Level gated LSTM model

Bioinformatics is a contemporary interdisciplinary area focused on analyzing the growing number of genome sequences. Gene variants are differences in DNA sequences among individuals within a population. Splice site recognition is a crucial step in the process of gene expression, where the coding sequences of genes are joined together to form mature messenger RNA (mRNA). These genetic variants that disrupt genes are believed to be the primary reason for neuro-developmental disorders like ASD (Autism Spectrum Disorder) is a neuro-developmental disorder that is diagnosed in individuals, families, and society and occurs as the developmental delay in one among the hundred genes that are associated with these disorders. Missense variants, premature stop codons, or deletions alter both the quality and quantity of encoded proteins. Predicting genes within exons and introns presents main challenges, such as dealing with sequencing errors, short reads, incomplete genes, overlapping, and more. Although many traditional techniques have been utilized in creating an exon prediction system, the primary challenge lies in accurately identifying the length and spliced strand location classification of exons in conjunction with introns. From now on, the suggested approach utilizes a Deep Learning algorithm to analyze intricate and extensive genomic datasets. M−LSTM is utilized to categorize three binary combinations (EI as 1, IE as 2, and none as 3) using spliced DNA strands. The M−LSTM system is able to sequence extensive datasets, ensuring that long information can be stored without any impact on the current input or output. This enables it to recognize and address long-term connections and problems with rapidly increasing gradients. The proposed model is compared internally with Naïve Bayes and Random Forest to assess its efficacy. Additionally, the proposed model's performance is forecasted by utilizing probabilistic parameters like recall, F1-score, precision, and accuracy to assess the effectiveness of the proposed system.

Nucleotide-level distance metrics to quantify alternative splicing implemented in TranD.

Advances in affordable transcriptome sequencing combined with better exon and gene prediction has motivated many to compare transcription across the tree of life. We develop a mathematical framework to calculate complexity and compare transcript models. Structural features, i.e. intron retention (IR), donor/acceptor site variation, alternative exon cassettes, alternative 5'/3' UTRs, are compared and the distance between transcript models is calculated with nucleotide level precision. All metrics are implemented in a PyPi package, TranDand output can be used to summarize splicing patterns for a transcriptome (1GTF) and between transcriptomes (2GTF). TranD output enables quantitative comparisons between: annotations augmented by empirical RNA-seq data and the original transcript models; transcript model prediction tools for longread RNA-seq (e.g. FLAIR versus Isoseq3); alternate annotations for a species (e.g. RefSeq vs Ensembl); and between closely related species. In C. elegans, Z. mays, D. melanogaster, D. simulans and H. sapiens, alternative exons were observed more frequently in combination with an alternative donor/acceptor than alone. Transcript models in RefSeq and Ensembl are linked and both have unique transcript models with empirical support. D.melanogaster and D.simulans, share many transcript models and long-read RNAseq data suggests that both species are under-annotated. We recommend combined references.

Improved Hybrid Approach for Enhancing Protein Coding Regions Identification in DNA Sequences

Introduction: Identifying and predicting protein-coding regions within DNA sequences play a pivotal role in genomic research. This paper introduces an approach for identifying proteincoding regions in DNA sequences, employing a hybrid methodology that combines a digital bandpass filter with wavelet transforms and various spectral estimation techniques to enhance exon prediction. Specifically, the Haar and Daubechies wavelet transforms are applied to improve the accuracy of protein-coding region (exon) prediction, enabling the extraction of intricate details that may be obscured in the original DNA sequences. background: The identification and prediction of protein-coding regions within DNA sequences play a pivotal role in genomic research. Methods: This research showcases the utility of Haar and Daubechies wavelet transforms, both nonparametric and parametric spectral estimation methods, and the deployment of a digital band pass filter for detecting peaks in exon regions. Additionally, the application of the Electron-Ion Interaction Potential (EIIP) method for converting symbolic DNA sequences into numerical values and the utilization of sum-of-sinusoids (SoS) mathematical models with optimized parameters further enrich the toolbox for DNA sequence analysis, ensuring the success of this proposed method in modeling DNA sequences optimally and accurately identifying genes. objective: Enhanced Protein-Coding Region Identification in DNA Sequences Using Wavelet Transforms Results: The outcomes of this approach showcase a substantial enhancement in identification accuracy for protein-coding regions. In terms of peak location detection, the application of Haar and Daubechies wavelet transforms enhances the accuracy of peak localization by approximately (0.01, 3-5 dB). When employing non-parametric and parametric spectral estimation techniques, there is an improvement in peak location by approximately (0.01, 4 dB) compared to the original signal. The proposed approach also achieves higher accuracy when compared with existing methods. method: hybrid methodology that combines a digital band-pass filter with wavelet transforms and various spectral estimation techniques to enhance exon prediction. Conclusion: These findings not only bridge gaps in DNA sequence analysis but also offer a promising pathway for advancing exonic region prediction and gene identification in genomics research. The hybrid methodology presented stands as a robust contribution to the evolving landscape of genomic analysis techniques. result: The results obtained through this proposed method demonstrate significantly improved identification accuracy. These findings offer a promising avenue for DNA sequence analysis, exonic region prediction, and gene identification.

Clinical-radiomics-based treatment decision support for KIT Exon 11 deletion in gastrointestinal stromal tumors: a multi-institutional retrospective study.

gastrointestinal stromal tumors (GISTs) with KIT exon 11 deletions have more malignant clinical outcomes. A radiomics model was constructed for the preoperative prediction of KIT exon 11 deletion in GISTs. Overall, 126 patients with GISTs who underwent preoperative enhanced CT were included. GISTs were manually segmented using ITK-SNAP in the arterial phase (AP) and portal venous phase (PVP) images of enhanced CT. Features were extracted using Anaconda (version 4.2.0) with PyRadiomics. Radiomics models were constructed by LASSO. The clinical-radiomics model (combined model) was constructed by combining the clinical model with the best diagnostic effective radiomics model. ROC curves were used to compare the diagnostic effectiveness of radiomics model, clinical model, and combined model. Diagnostic effectiveness among radiomics model, clinical model and combine model were analyzed in external cohort (n=57). Statistics were carried out using R 3.6.1. The Radscore showed favorable diagnostic efficacy. Among all radiomics models, the AP-PVP radiomics model exhibited excellent performance in the training cohort, with an AUC of 0.787 (95% CI: 0.687-0.866), which was verified in the test cohort (AUC=0.775, 95% CI: 0.608-0.895). Clinical features were also analyzed. Among the radiomics, clinical and combined models, the combined model showed favorable diagnostic efficacy in the training (AUC=0.863) and test cohorts (AUC=0.851). The combined model yielded the largest AUC of 0.829 (95% CI, 0.621-0.950) for the external validation of the combined model. GIST patients could be divided into high or low risk subgroups of recurrence and mortality by the Radscore. The radiomics models based on enhanced CT for predicting KIT exon 11 deletion mutations have good diagnostic performance.

Predictive Value of a Radiomics Nomogram Model Based on Contrast-Enhanced Computed Tomography for KIT Exon 9 Gene Mutation in Gastrointestinal Stromal Tumors.

To establish and validate a radiomics nomogram model for preoperative prediction of KIT exon 9 mutation status in patients with gastrointestinal stromal tumors (GISTs). Eighty-seven patients with pathologically confirmed GISTs were retrospectively enrolled in this study. Imaging and clinicopathological data were collected and randomly assigned to the training set (n = 60) and test set (n = 27) at a ratio of 7:3. Based on contrast-enhanced CT (CE-CT) arterial and venous phase images, the region of interest (ROI) of the tumors were manually drawn layer by layer, and the radiomics features were extracted. The intra-class correlation coefficient (ICC) was used to test the consistency between observers. Least absolute shrinkage and selection operator regression (LASSO) were used to further screen the features. The nomogram of integrated radiomics score (Rad-Score) and clinical risk factors (extra-gastric location and distant metastasis) was drawn on the basis of multivariate logistic regression. The area under the receiver operating characteristic (AUC) curve and decision curve analysis were used to evaluate the predictive efficiency of the nomogram, and the clinical benefits that the decision curve evaluation model may bring to patients. The selected radiomics features (arterial phase and venous phase features) were significantly correlated with the KIT exon 9 mutation status of GISTs. The AUC, sensitivity, specificity, and accuracy in the radiomics model were 0.863, 85.7%, 80.4%, and 85.0% for the training group (95% confidence interval [CI]: 0.750-0.938), and 0.883, 88.9%, 83.3%, and 81.5% for the test group (95% CI: 0.701-0.974), respectively. The AUC, sensitivity, specificity, and accuracy in the nomogram model were 0.902 (95% confidence interval [CI]: 0.798-0.964), 85.7%, 86.9%, and 91.7% for the training group, and 0.907 (95% CI: 0.732-0.984), 77.8%, 94.4%, and 88.9% for the test group, respectively. The decision curve showed the clinical application value of the radiomic nomogram. The radiomics nomogram model based on CE-CT can effectively predict the KIT exon 9 mutation status of GISTs and may be used for selective gene analysis in the future, which is of great significance for the accurate treatment of GISTs.

Splice-site identification for exon prediction using bidirectional LSTM-RNN approach

Machine learning methods played a major role in improving the accuracy of predictions and classification of DNA (Deoxyribonucleic Acid) and protein sequences. In eukaryotes, Splice-site identification and prediction is though not a straightforward job because of numerous false positives. To solve this problem, here, in this paper, we represent a bidirectional Long Short Term Memory (LSTM) Recurrent Neural Network (RNN) based deep learning model that has been developed to identify and predict the splice-sites for the prediction of exons from eukaryotic DNA sequences. During the splicing mechanism of the primary mRNA transcript, the introns, the non-coding region of the gene are spliced out and the exons, the coding region of the gene are joined. This bidirectional LSTM-RNN model uses the intron features that start with splice site donor (GT) and end with splice site acceptor (AG) in order of its length constraints. The model has been improved by increasing the number of epochs while training. This designed model achieved a maximum accuracy of 95.5%. This model is compatible with huge sequential data such as the complete genome.

A Bidirectional LSTM-RNN and GRU Method to Exon Prediction Using Splice-Site Mapping

Deep Learning techniques (DL) significantly improved the accuracy of predictions and classifications of deoxyribonucleic acid (DNA). On the other hand, identifying and predicting splice sites in eukaryotes is difficult due to many erroneous discoveries. To address this issue, we propose a deep learning model for recognizing and anticipating splice sites in eukaryotic DNA sequences based on a bidirectional Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) and Gated recurrent unit (GRU). The non-coding introns of the gene are spliced out, and the coding exons are joined during the splicing of the original mRNA transcript. This bidirectional LSTM-RNN-GRU model incorporates intron features in order of their length constraints, beginning with splice site donor (GT) and ending with splice site acceptor (AG). The performance of the model improves as the number of training epochs grows. The best level of accuracy for this model is 96.1 percent.

Splice-Site Identification for Exon Prediction Using Bidirectional Lstm-Rnn Approach

Splice-Site Identification for Exon Prediction Using Bidirectional Lstm-Rnn Approach

Lung adenocarcinoma: development of nomograms based on PET/CT images for prediction of epidermal growth factor receptor mutation status and subtypes.

To develop nomograms that combine clinical characteristics, computed tomographic (CT) features and 18F-fluorodeoxyglucose PET (18F-FDG PET) metabolic parameters for individual prediction of epidermal growth factor receptor (EGFR) mutation status and exon 19 deletion mutation and exon 21 point mutation (21 L858R) subtypes in lung adenocarcinoma. In total 124 lung adenocarcinoma patients who underwent EGFR mutation testing and whole-body 18F-FDG PET/CT were enrolled. Each patient's clinical characteristics (age, sex, smoking history, etc.), CT features (size, location, margins, etc.) and four metabolic parameters (SUVmax, SUVmean, MTV and TLG) were recorded and analyzed. Logistic regression analyses were performed to screen for significant predictors of EGFR mutation status and subtypes, and these predictors were presented as easy-to-use nomograms. According to the results of multiple regression analysis, three nomograms for individualized prediction of EGFR mutation status and subtypes were constructed. The area under curve values of three nomograms were 0.852 (95% CI, 0.783-0.920), 0.857 (95% CI, 0.778-0.937) and 0.893 (95% CI, 0.819-0.968) of EGFR mutation vs. wild-type, 19 deletion mutation vs. wild-type and 21 L858R vs. wild-type, respectively. Only calcification showed significant differences between the EGFR 19 deletion and 21 L858R mutations. EGFR 21 L858R mutation was more likely to be nonsolid texture with air bronchograms and pleural retraction on CT images. And they were more likely to be associated with lower FDG metabolic activity compared with those wild-types. The sex difference was mainly caused by the 19 deletion mutation, and calcification was more frequent in them.

Gastrointestinal stromal tumors: associations between contrast-enhanced CT images and KIT exon 11 gene mutation.

BackgroundMutation screening for gastrointestinal stromal tumor (GIST) is crucial and the c kit gene (KIT) exon 11 mutation is the most common type. This study aimed to explore the associations between GIST with KIT exon 11 mutation and contrast-enhanced computed tomography (CT) images.MethodsPathologically proven GISTs with definitive genotype testing results in our hospital were retrospectively included. Abdominal contrast-enhanced CT images were analyzed. Conventional CT image features and radiomic features were recorded and extracted to build the following models: model [CT], model [radiomic + clinic] and model [CT + radiomic + clinic]. The diagnostic performances of GISTs with KIT exon 11 mutation and KIT exon 11 deletion involving codons 557–558 were evaluated.ResultsIn total, 327 GISTs (255 with KIT exon 11 mutation, and 73 with KIT exon 11 mutation deletion involving codons 557–558) were included. Significant CT features were found for GISTs with KIT exon 11 mutation. The area under curves (AUCs) of the models for KIT exon 11 mutation were 0.7158, 0.7530, and 0.8375 in the training cohort, and 0.6777, 0.7349, and 0.8105 in validation cohort, respectively. The AUCs of the models for KIT exon 11 mutation deletion involving codons 557–558 were 0.7155, 8621, and 0.8691 in the training cohort, and 0.7099, 0.8355, and 0.8488 in the validation cohort, respectively. The model [CT + radiomic + clinic] demonstrated the highest AUCs for prediction of KIT exon 11 mutation and those with deletion involving codons 557–558 (P<0.05), respectively. The model [radiomic + clinic] showed higher diagnostic performance than model [CT] significantly.ConclusionsOur results demonstrated the associations between GIST with KIT exon 11 mutation and contrast-enhanced CT images. We found combing conventional image analysis and texture analysis is a useful tool to distinguish GIST with KIT exon 11 mutation. CT radiogenomics exhibited good application potential in predict the KIT exon 11 mutation of GIST.

Optimum model selection and statistical analysis for DNA sequences

In this article, we study the statistical characteristics and examine the performance of original representation and mathematical modelling of deoxyribonucleic acid (DNA) sequences. The proposed mathematical modelling approach is presented to create closed formulas for the original DNA data sequences with different methods. Accuracy of representation is studied based on evaluation metric values. The root Mean Squared Error (RMSE) and correlation coefficient (R) are used for examining the accuracy of all mathematical models to select the optimum one for DNA representation. In addition, statistical parameters such as energy, entropy, standard deviation, variance, mean, range, Mean Absolute Deviation (MAD), skewness and kurtosis are also used for the selection of the optimum model for DNA representation. Finally, spectral estimation methods are used for exon prediction, which means determination of the coding region (exon) for actual sequences and selected mathematical model: Sum of Sinusoids (SoS) with 8 terms and Gaussian with 8 terms. The exon prediction results from original DNA sequences and mathematically modelled DNA sequences coincide and ensure the success of the proposed sum-of-­sinusoids for modelling of DNA sequences, while the Gaussian model is not appropriate for this task.

Prediction of Eukaryotic Exons using Bidirectional LSTM-RNN based Deep Learning Model

Exon prediction has always been a challenge for computational biologist. Although there have been many advances in identification and prediction of exons by computational methods. The efficacy and efficiency of prediction methods need to be further improved using new parameters and algorithms. Moreover, it is essential to develop new prediction methods by combining already existing approaches that can greatly improve prediction accuracy. A eukaryotic gene contains several exons and introns that are separated by splice site junction. It is important to accurately identify splice sites in a gene. Splice sites regions are known, but computational signal prediction is still challenging due to numerous false positives and other problems. In this paper, a novel combination of Support Vector Machine and bidirectional LSTM-RNN based Deep Learning approaches has been applied to improve the efficiency and accuracy of exon prediction. The proposed method takes into account the conventional machine learning as well as the deep learning approach on predictive accuracy of eukaryotic exons.

Cloud Based Exon Prediction Methodology using Logarithmic Adaptive Algorithms for Genomic Signal Analysis

Cloud Based Exon Prediction Methodology using Logarithmic Adaptive Algorithms for Genomic Signal Analysis

EXFI: Exon and splice graph prediction without a reference genome.

For population genetic studies in nonmodel organisms, it is important to use every single source of genomic information. This paper presents EXFI, a Python pipeline that predicts the splice graph and exon sequences using an assembled transcriptome and raw whole‐genome sequencing reads. The main algorithm uses Bloom filters to remove reads that are not part of the transcriptome, to predict the intron–exon boundaries, to then proceed to call exons from the assembly, and to generate the underlying splice graph. The results are returned in GFA1 format, which encodes both the predicted exon sequences and how they are connected to form transcripts. EXFI is written in Python, tested on Linux platforms, and the source code is available under the MIT License at https://github.com/jlanga/exfi.

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.

Efficient implementation of parametric spectral estimation techniques for DNA exon prediction

This paper is mainly concerned with the application of different parametric spectral estimation techniques on deoxyribonucleic acid (DNA) sequences. The objective of this study is to allow the analysis of these sequences for useful information extraction such as exon information. It is known that the exon, if existing, is represented with a spectral peak at the normalized frequency of 0.667. A comparison study is presented between Burg, Covariance, Modified Covariance, Yule-Walker, MUltiple SIgnal Classification (MUSIC) and Auto-Regressive Moving Average (ARMA) techniques for efficient representation of DNA sequences in the frequency domain for further exon prediction. Moreover, to filter the out-of-band noise that appears in the frequency domain in the prediction process, an inverse Chebyshev bandpass filter tuned at 0.667 is utilized. The obtained results reveal the importance of bandpass filtering and ensure that Burg, Covariance and Modified Covariance techniques are the best for exon prediction with a detection range of about 60 dB.

A tri-nucleotide mapping scheme based on residual volume of amino acids for short length exon prediction using sliding window DFT method

One of the great challenges in the field of bioinformatics is how to locate the accurate protein-coding regions in a given DNA sequence. The accurate identification of the protein-coding region is useful in many applications. For instance; it helps in characterizing new proteins, drug designing, and also in revealing the evolutionary background of a particular organism. DSP based techniques are quite popular in the protein-coding region identification. The first essential step of the DSP based exon prediction technique is to convert the base sequences into the numerical sequence. The choice of the numerical mapping scheme affects how well the characteristic feature of the DNA sequence is reflected in the numerical domain which helps in finding the accurate location of exons. In the last two decades, numbers of mapping schemes have been successfully used for exon prediction. However, locating the short length exon is still a difficult task. In this paper, we have proposed a tri-nucleotide mapping scheme that exploits the residual volume property of amino acid to encode the given DNA sequence. It is obtained that the proposed tri-nucleotide mapping scheme provides better results than other mapping schemes in case of short length exon detection.

Advances in radiological studies of gastrointestinal stromal tumors

The commonly used radiological examinations for gastrointestinal stromal tumor (GIST) include ultrasound, CT, magnetic resonance imaging and PET, which play important roles in the individualized treatment of GIST through the assistance of the detection, location, diagnose and differential diagnosis, biological behavior and risk evaluation, operation planning, therapeutic effects prediction and follow-up. Standardized predisposal, multi-planar reconstruction and multi-phase enhancement are of great significance in the detection of small GIST lesions. Differences in drainage vessels and enhancement characteristics of tumors are helpful in differentiating small intestinal GIST from pancreatic cancer, neuroendocrine tumors and other abdominal non-GIST tumors. Radiology can provide multiple indicators to qualitatively or quantitatively reflect the risk of GIST. Recent study found that the risk classification of GIST has correlation with necrosis, but not cystic degeneration. The risk stratification of GIST by dual-energy CT showed that CT value and standardized iodine concentration in arterial-venous and delayed phases of high-risk tumors were significantly higher than those of non-high-risk tumors. The correlation between tumor enhancement pattern and risk stratificationwas still controversial. Recently, the rise of radiomics and deep learning techniques provide new direction for risk assessment of GIST. Some studies used various models and parameters to predict the risk classifications of GIST and achieved AUC values above 0.85, and some results were also superior to conventional indicators and radiologists' subjective judgment in the prediction of exon 11 mutations, mitotic counts and survivals. Recent developments in PET and diffusion-weighted MRI provide more precise functional quantitative indicators for the prediction and early evaluation of the response of GIST to targeted therapy. These studies are constantly expanding our field of exploration, and prompting us to stride toward the goal of individualized treatment of GIST.

Exon prediction based on multiscale products of a genomic-inspired multiscale bilateral filtering.

Multiscale signal processing techniques such as wavelet filtering have proved to be particularly successful in predicting exon sequences. Traditional wavelet predictor is domain filtering, and enforces exon features by weighting nucleotide values with coefficients. Such a measure performs linear filtering and is not suitable for preserving the short coding exons and the exon-intron boundaries. This paper describes a prediction framework that is capable of non-linearly processing DNA sequences while achieving high prediction rates. There are two key contributions. The first is the introduction of a genomic-inspired multiscale bilateral filtering (MSBF) which exploits both weighting coefficients in the spatial domain and nucleotide similarity in the range. Similarly to wavelet transform, the MSBF is also defined as a weighted sum of nucleotides. The difference is that the MSBF takes into account the variation of nucleotides at a specific codon position. The second contribution is the exploitation of inter-scale correlation in MSBF domain to find the inter-scale dependency on the differences between the exon signal and the background noise. This favourite property is used to sharp the important structures while weakening noise. Three benchmark data sets have been used in the evaluation of considered methods. By comparison with four existing techniques, the prediction results demonstrate that: the proposed method reveals at least improvement of 4.1%, 50.5%, 25.6%, 2.5%, 10.8%, 15.5%, 11.1%, 12.3%, 9.2% and 2.4% on the exons length of 1–24, 25–49, 50–74, 75–99, 100–124, 125–149, 150–174, 175–199, 200–299 and 300–300+, respectively. The MSBF of its nonlinear nature is good at energy compaction, which makes it capable of locating the sharp variations around short exons. The direct scale multiplication of coefficients at several adjacent scales obviously enhanced exon features while the noise contents were suppressed. We show that the non-linear nature and correlation-based property achieved in proposed predictor is greater than that for traditional filtering, which leads to better exon prediction performance. There are some possible applications of this predictor. Its good localization and protection of sharp variations will make the predictor be suitable to perform fault diagnosis of aero-engine.

New Exon Prediction Techniques Using Adaptive Signal Processing Algorithms for Genomic Analysis

Substantial research and monetary aids to healthcare establishments are provided by cloud computing. A benign position to store and handle vast genome data is offered by cloud services. Labs for gene sequencing send out raw and contingent data over the Internet to multiple sequence collections under conservative flow of gene information. The use of cloud services also reduces the storage costs of deoxyribonucleic acid (DNA) sequencing. Here, an efficient and new bio-informatics genomic system is proposed by the use of cloud services from Amazon to access the stored gene data and process it. A key task in bio-informatics is to locate protein-coding sections in a gene sequence based on three base periodicity (TBP) is for disease diagnosis and design drugs. Here, a novel cloud-based adaptive exon predictor (AEP) using Amazon cloud services is proposed to improve the accuracy in exon finding ability as well as aimed at superior convergence. Noise in the input gene sequence given to the proposed AEPs is pre-processed using normalized LMS filtering. Computational complexity can be reduced using proposed data normalized form of least logarithmic absolute difference (NLLAD) algorithm and its error normalized variants. It was shown that sign regressor NLLAD (SRNLLAD) dependent AEP is efficient in exon forecast applications using different metrics for a performance like sensitivity 0.8037, precision 0.8052 along with specificity 0.8146 by different gene sequences considered from the National Center for Biotechnology Information (NCBI) databank. The proposed AEPs have shown upright performance than typical LMS and other AEPs in terms of exon prediction accuracy, convergence, and computational complexity. Their less computational complexity will be found attractive, and they are suitable to use in bio-informatics nano devices.