iHyd-LysSite (EPSV): Identifying Hydroxylysine Sites in Protein Using Statistical Formulation by Extracting Enhanced Position and Sequence Variant Feature Technique

Mahmood, Muhammad Khalid; Ehsan, Asma; Khan, Yaser Daanial; Chou, Kuo‐Chen

doi:10.2174/1389202921999200831142629

Cited by 36 publications

(13 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some moments are used to reveal eccentricity and orientation of data while some are used to estimate the data size 54 – 59 . Several moments have been formed by various mathematicians and statisticians based on famous distribution functions and polynomials 60 – 62 . These moments were utilized to explicate the current problem 63 .…”

Section: Methodsmentioning

confidence: 99%

A machine learning technique for identifying DNA enhancer regions utilizing CIS-regulatory element patterns

Butt

Alkhalifah

Alturise

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

Enhancers regulate gene expression, by playing a crucial role in the synthesis of RNAs and proteins. They do not directly encode proteins or RNA molecules. In order to control gene expression, it is important to predict enhancers and their potency. Given their distance from the target gene, lack of common motifs, and tissue/cell specificity, enhancer regions are thought to be difficult to predict in DNA sequences. Recently, a number of bioinformatics tools were created to distinguish enhancers from other regulatory components and to pinpoint their advantages. However, because the quality of its prediction method needs to be improved, its practical application value must also be improved. Based on nucleotide composition and statistical moment-based features, the current study suggests a novel method for identifying enhancers and non-enhancers and evaluating their strength. The proposed study outperformed state-of-the-art techniques using fivefold and tenfold cross-validation in terms of accuracy. The accuracy from the current study results in 86.5% and 72.3% in enhancer site and its strength prediction respectively. The results of the suggested methodology point to the potential for more efficient and successful outcomes when statistical moment-based features are used. The current study's source code is available to the research community at https://github.com/csbioinfopk/enpred.

show abstract

Section: Methodsmentioning

confidence: 99%

A machine learning technique for identifying DNA enhancer regions utilizing CIS-regulatory element patterns

Butt

Alkhalifah

Alturise

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Redundancy and irrelevancy are removed after feature extraction. It improves the accuracy and increases the performance of the learning model [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ].…”

Section: Methodsmentioning

confidence: 99%

Deep Learning Approaches for Detection of Breast Adenocarcinoma Causing Carcinogenic Mutations

Shah

Alturise

Alkhalifah

et al. 2022

IJMS

Self Cite

View full text Add to dashboard Cite

Genes are composed of DNA and each gene has a specific sequence. Recombination or replication within the gene base ends in a permanent change in the nucleotide collection in a DNA called mutation and some mutations can lead to cancer. Breast adenocarcinoma starts in secretary cells. Breast adenocarcinoma is the most common of all cancers that occur in women. According to a survey within the United States of America, there are more than 282,000 breast adenocarcinoma patients registered each 12 months, and most of them are women. Recognition of cancer in its early stages saves many lives. A proposed framework is developed for the early detection of breast adenocarcinoma using an ensemble learning technique with multiple deep learning algorithms, specifically: Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Bi-directional LSTM. There are 99 types of driver genes involved in breast adenocarcinoma. This study uses a dataset of 4127 samples including men and women taken from more than 12 cohorts of cancer detection institutes. The dataset encompasses a total of 6170 mutations that occur in 99 genes. On these gene sequences, different algorithms are applied for feature extraction. Three types of testing techniques including independent set testing, self-consistency testing, and a 10-fold cross-validation test is applied to validate and test the learning approaches. Subsequently, multiple deep learning approaches such as LSTM, GRU, and bi-directional LSTM algorithms are applied. Several evaluation metrics are enumerated for the validation of results including accuracy, sensitivity, specificity, Mathew’s correlation coefficient, area under the curve, training loss, precision, recall, F1 score, and Cohen’s kappa while the values obtained are 99.57, 99.50, 99.63, 0.99, 1.0, 0.2027, 99.57, 99.57, 99.57, and 99.14 respectively.

show abstract

“…4 . The feature vectors formed are clamped to the neural network input layer 36 . An optimized number of hidden layer neurons are used.…”

Section: Methodsmentioning

confidence: 99%

Identification of stress response proteins through fusion of machine learning models and statistical paradigms

Alzahrani

Alghamdi

Ullah

et al. 2021

Sci Rep

Self Cite

View full text Add to dashboard Cite

Proteins are a vital component of cells that perform physiological functions to ensure smooth operations of bodily functions. Identification of a protein's function involves a detailed understanding of the structure of proteins. Stress proteins are essential mediators of several responses to cellular stress and are categorized based on their structural characteristics. These proteins are found to be conserved across many eukaryotic and prokaryotic linkages and demonstrate varied crucial functional activities inside a cell. The in-vivo, ex vivo, and in-vitro identification of stress proteins are a time-consuming and costly task. This study is aimed at the identification of stress protein sequences with the aid of mathematical modelling and machine learning methods to supplement the aforementioned wet lab methods. The model developed using Random Forest showed remarkable results with 91.1% accuracy while models based on neural network and support vector machine showed 87.7% and 47.0% accuracy, respectively. Based on evaluation results it was concluded that random-forest based classifier surpassed all other predictors and is suitable for use in practical applications for the identification of stress proteins. Live web server is available at http://biopred.org/stressprotiens, while the webserver code available is at https://github.com/abdullah5naveed/SRP_WebServer.git

show abstract

iHyd-LysSite (EPSV): Identifying Hydroxylysine Sites in Protein Using Statistical Formulation by Extracting Enhanced Position and Sequence Variant Feature Technique

Cited by 36 publications

References 37 publications

A machine learning technique for identifying DNA enhancer regions utilizing CIS-regulatory element patterns

A machine learning technique for identifying DNA enhancer regions utilizing CIS-regulatory element patterns

Deep Learning Approaches for Detection of Breast Adenocarcinoma Causing Carcinogenic Mutations

Identification of stress response proteins through fusion of machine learning models and statistical paradigms

Contact Info

Product

Resources

About