Feature Extraction of Long Non-coding RNAs: A Fourier and Numerical Mapping Approach

An enhanced classification system of various rice plant diseases based on multi-level handcrafted feature extraction technique

The rice plant is one of the most significant crops in the world, and it suffers from various diseases. The traditional methods for rice disease detection are complex and time-consuming, mainly depending on the expert's experience. The explosive growth in image processing, computer vision, and deep learning techniques provides effective and innovative agriculture solutions for automatically detecting and classifying these diseases. Moreover, more information can be extracted from the input images due to different feature extraction techniques. This paper proposes a new system for detecting and classifying rice plant leaf diseases by fusing different features, including color texture with Local Binary Pattern (LBP) and color features with Color Correlogram (CC). The proposed system consists of five stages. First, input images acquire RGB images of rice plants. Second, image preprocessing applies data augmentation to solve imbalanced problems, and logarithmic transformation enhancement to handle illumination problems has been applied. Third, the features extraction stage is responsible for extracting color features using CC and color texture features using multi-level multi-channel local binary pattern (MCLBP). Fourth, the feature fusion stage provides complementary and discriminative information by concatenating the two types of features. Finally, the rice image classification stage has been applied using a one-against-all support vector machine (SVM). The proposed system has been evaluated on three benchmark datasets with six classes: Blast (BL), Bacterial Leaf Blight (BLB), Brown Spot (BS), Tungro (TU), Sheath Blight (SB), and Leaf Smut (LS) have been used. Rice Leaf Diseases First Dataset, Second Dataset, and Third Dataset achieved maximum accuracy of 99.53%, 99.4%, and 99.14%, respectively, with processing time from [Formula: see text]. Hence, the proposed system has achieved promising results compared to other state-of-the-art approaches.

Predicting Parkinson disease related genes based on PyFeat and gradient boosted decision tree

Identifying genes related to Parkinson’s disease (PD) is an active research topic in biomedical analysis, which plays a critical role in diagnosis and treatment. Recently, many studies have proposed different techniques for predicting disease-related genes. However, a few of these techniques are designed or developed for PD gene prediction. Most of these PD techniques are developed to identify only protein genes and discard long noncoding (lncRNA) genes, which play an essential role in biological processes and the transformation and development of diseases. This paper proposes a novel prediction system to identify protein and lncRNA genes related to PD that can aid in an early diagnosis. First, we preprocessed the genes into DNA FASTA sequences from the University of California Santa Cruz (UCSC) genome browser and removed the redundancies. Second, we extracted some significant features of DNA FASTA sequences using the PyFeat method with the AdaBoost as feature selection. These selected features achieved promising results compared with extracted features from some state-of-the-art feature extraction techniques. Finally, the features were fed to the gradient-boosted decision tree (GBDT) to diagnose different tested cases. Seven performance metrics were used to evaluate the performance of the proposed system. The proposed system achieved an average accuracy of 78.6%, the area under the curve equals 84.5%, the area under precision-recall (AUPR) equals 85.3%, F1-score equals 78.3%, Matthews correlation coefficient (MCC) equals 0.575, sensitivity (SEN) equals 77.1%, and specificity (SPC) equals 80.2%. The experiments demonstrate promising results compared with other systems. The predicted top-rank protein and lncRNA genes are verified based on a literature review.

MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors

One of the main challenges in applying machine learning algorithms to biological sequence data is how to numerically represent a sequence in a numeric input vector. Feature extraction techniques capable of extracting numerical information from biological sequences have been reported in the literature. However, many of these techniques are not available in existing packages, such as mathematical descriptors. This paper presents a new package, MathFeature, which implements mathematical descriptors able to extract relevant numerical information from biological sequences, i.e. DNA, RNA and proteins (prediction of structural features along the primary sequence of amino acids). MathFeature makes available 20 numerical feature extraction descriptors based on approaches found in the literature, e.g. multiple numeric mappings, genomic signal processing, chaos game theory, entropy and complex networks. MathFeature also allows the extraction of alternative features, complementing the existing packages. To ensure that our descriptors are robust and to assess their relevance, experimental results are presented in nine case studies. According to these results, the features extracted by MathFeature showed high performance (0.6350-0.9897, accuracy), both applying only mathematical descriptors, but also hybridization with well-known descriptors in the literature. Finally, through MathFeature, we overcame several studies in eight benchmark datasets, exemplifying the robustness and viability of the proposed package. MathFeature has advanced in the area by bringing descriptors not available in other packages, as well as allowing non-experts to use feature extraction techniques.

XG-ac4C: identification of N4-acetylcytidine (ac4C) in mRNA using eXtreme gradient boosting with electron-ion interaction pseudopotentials

N4-acetylcytidine (ac4C) is a post-transcriptional modification in mRNA which plays a major role in the stability and regulation of mRNA translation. The working mechanism of ac4C modification in mRNA is still unclear and traditional laboratory experiments are time-consuming and expensive. Therefore, we propose an XG-ac4C machine learning model based on the eXtreme Gradient Boost classifier for the identification of ac4C sites. The XG-ac4C model uses a combination of electron-ion interaction pseudopotentials and electron-ion interaction pseudopotentials of trinucleotide of the nucleotides in ac4C sites. Moreover, Shapley additive explanations and local interpretable model-agnostic explanations are applied to understand the importance of features and their contribution to the final prediction outcome. The obtained results demonstrate that XG-ac4C outperforms existing state-of-the-art methods. In more detail, the proposed model improves the area under the precision-recall curve by 9.4% and 9.6% in cross-validation and independent tests, respectively. Finally, a user-friendly web server based on the proposed model for ac4C site identification is made freely available at http://nsclbio.jbnu.ac.kr/tools/xgac4c/ .