Quantification of knee vibroarthrographic signal irregularity associated with patellofemoral joint cartilage pathology based on entropy and envelope amplitude measures

Anterior Cruciate Ligament Tear Detection Based on T-Distribution Slice Attention Framework with Penalty Weight Loss Optimisation

Anterior cruciate ligament (ACL) plays an important role in stabilising the knee joint, prevents excessive anterior translation of the tibia, and provides rotational stability. ACL injuries commonly occur as a result of rapid deceleration, sudden change in direction, or direct impact to the knee during sports activities. Although several deep learning techniques have recently been applied in the detection of ACL tears, challenges such as effective slice filtering and the nuanced relationship between varying tear grades still remain underexplored. This study used an advanced deep learning model that integrated a T-distribution-based slice attention filtering mechanism with a penalty weight loss function to improve the performance for detection of ACL tears. A T-distribution slice attention module was effectively utilised to develop a robust slice filtering system of the deep learning model. By incorporating class relationships and substituting the conventional cross-entropy loss with a penalty weight loss function, the classification accuracy of our model is markedly increased. The combination of slice filtering and penalty weight loss shows significant improvements in diagnostic performance across six different backbone networks. In particular, the VGG-Slice-Weight model provided an area score of 0.9590 under the receiver operating characteristic curve (AUC). The deep learning framework used in this study offers an effective diagnostic tool that supports better ACL injury detection in clinical diagnosis practice.

Long-term memory in Staphylococcus aureus α-hemolysin ion channel kinetics

The kinetics of an ion channel are classically understood as a random process. However, studies have shown that in complex ion channels, formed by multiple subunits, this process can be deterministic, presenting long-term memory. Staphylococcus aureus α-hemolysin (α-HL) is a toxin that acts as the major factor in Staphylococcus aureus virulence. α-HL is a water-soluble protein capable of forming ion channels into lipid bilayers, by insertion of an amphipathic  β-barrel. Here, the α-HL was used as an experimental model to study memory in ion channel kinetics. We applied the approximate entropy (ApEn) approach to analyze randomness and the Detrended Fluctuation Analysis (DFA) to investigate the existence of long memory in α-HL channel kinetics. Single-channel currents were measured through experiments with α-HL channels incorporated in planar lipid bilayers. All experiments were carried out under the following conditions: 1 M NaCl solution, pH 4.5; transmembrane potential of + 40 mV and temperature 25 ± 1 °C. Single-channel currents were recorded in real-time in the memory of a microcomputer coupled to an A/D converter and a patch-clamp amplifier. The conductance value of the α-HL channels was 0.82 ± 0.0025 nS (n = 128). The DFA analysis showed that the kinetics of α-HL channels presents long-term memory ([Formula: see text] = 0.63 ± 0.04). The ApEn outcomes showed low complexity to dwell times when open (ApEno = 0.5514 ± 0.28) and closed (ApEnc = 0.1145 ± 0.08), corroborating the results of the DFA method.

A method of feature fusion and dimension reduction for knee joint pathology screening and separability evaluation criteria

BACKGROUND AND OBJECTIVE

Knee-joint vibroarthrographic (VAG) signal is an effective method for performing a non-invasive knee osteoarthritis (KOA) diagnosis, VAG signal analysis plays a crucial role in achieving the early pathological screening of the knee joint. In order to improve the accuracy of knee pathology screening and to investigate the method suitable for embedded in wearable diagnostic device for knee joint, this paper proposes a knee pathology screening method. Aiming to fill the gap of lacking suitable and unified evaluation indexes for single feature and fusion feature, this paper proposes feature separability evaluation criteria.

METHODS

In this paper, we propose a knee joint pathology screening method based on feature fusion and dimension reduction combined with random forest classifier, as well as, the evaluation criteria of feature separability. As for pathological screening method, this paper proposes the idea of multi-dimensional feature fusion, using principal component analysis (PCA) to reduce the redundant part of fusion feature (F-F) to obtain deep fusion feature (D-F-F) with more separability. Meanwhile, this paper proposes the maximal information coefficient (MIC) and correlation matrix collinearity (CMC) feature evaluation criteria, these not only can be used as new feature quantitative metrics, but also illustrate that the divisibility of the deep fusion feature is more potent than that before feature dimension reduction.

RESULTS

The experimental results show that the method in this paper has good performance in pathology classification on random forest classifier with 96% accuracy, especially the accuracy of SVM and K-NN are also improved after feature dimension reduction.

CONCLUSION

The results indicate that this classification research has high screening efficiency for KOA diagnosis and could provide a feasible method for computer-assisted non-invasive diagnosis of KOA. And we provide a novel way for separability evaluation of VAG signal features.

Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN-Part II: Patellofemoral Joint

Cartilage loss due to osteoarthritis (OA) in the patellofemoral joint provokes pain, stiffness, and restriction of joint motion, which strongly reduces quality of life. Early diagnosis is essential for prolonging painless joint function. Vibroarthrography (VAG) has been proposed in the literature as a safe, noninvasive, and reproducible tool for cartilage evaluation. Until now, however, there have been no strict protocols for VAG acquisition especially in regard to differences between the patellofemoral and tibiofemoral joints. The purpose of this study was to evaluate the proposed examination and acquisition protocol for the patellofemoral joint, as well as to determine the optimal examination protocol to obtain the best diagnostic results. Thirty-four patients scheduled for knee surgery due to cartilage lesions were enrolled in the study and compared with 33 healthy individuals in the control group. VAG acquisition was performed prior to surgery, and cartilage status was evaluated during the surgery as a reference point. Both closed (CKC) and open (OKC) kinetic chains were assessed during VAG. The selection of the optimal signal measures was performed using a neighborhood component analysis (NCA) algorithm. The classification was performed using multilayer perceptron (MLP) and radial basis function (RBF) neural networks. The classification using artificial neural networks was performed for three variants: I. open kinetic chain, II. closed kinetic chain, and III. open and closed kinetic chain. The highest diagnostic accuracy was obtained for variants I and II for the RBF 9-35-2 and MLP 10-16-2 networks, respectively, achieving a classification accuracy of 98.53, a sensitivity of 0.958, and a specificity of 1. For variant III, a diagnostic accuracy of 97.79 was obtained with a sensitivity and specificity of 0.978 for MLP 8-3-2. This indicates a possible simplification of the examination protocol to single kinetic chain analyses.

New Methods for the Acoustic-Signal Segmentation of the Temporomandibular Joint

(1) Background: The stethoscope is one of the main accessory tools in the diagnosis of temporomandibular joint disorders (TMD). However, the clinical auscultation of the masticatory system still lacks computer-aided support, which would decrease the time needed for each diagnosis. This can be achieved with digital signal processing and classification algorithms. The segmentation of acoustic signals is usually the first step in many sound processing methodologies. We postulate that it is possible to implement the automatic segmentation of the acoustic signals of the temporomandibular joint (TMJ), which can contribute to the development of advanced TMD classification algorithms. (2) Methods: In this paper, we compare two different methods for the segmentation of TMJ sounds which are used in diagnosis of the masticatory system. The first method is based solely on digital signal processing (DSP) and includes filtering and envelope calculation. The second method takes advantage of a deep learning approach established on a U-Net neural network, combined with long short-term memory (LSTM) architecture. (3) Results: Both developed methods were validated against our own TMJ sound database created from the signals recorded with an electronic stethoscope during a clinical diagnostic trail of TMJ. The Dice score of the DSP method was 0.86 and the sensitivity was 0.91; for the deep learning approach, Dice score was 0.85 and there was a sensitivity of 0.98. (4) Conclusions: The presented results indicate that with the use of signal processing and deep learning, it is possible to automatically segment the TMJ sounds into sections of diagnostic value. Such methods can provide representative data for the development of TMD classification algorithms.

A Computational Method for the Identification of Endolysins and Autolysins

BACKGROUND

Cell lytic enzyme is a kind of highly evolved protein, which can destroy the cell structure and kill the bacteria. Compared with antibiotics, cell lytic enzyme will not cause serious problem of drug resistance of pathogenic bacteria. Thus, the study of cell wall lytic enzymes aims at finding an efficient way for curing bacteria infectious. Compared with using antibiotics, the problem of drug resistance becomes more serious. Therefore, it is a good choice for curing bacterial infections by using cell lytic enzymes. Cell lytic enzyme includes endolysin and autolysin and the difference between them is the purpose of the break of cell wall. The identification of the type of cell lytic enzymes is meaningful for the study of cell wall enzymes.

OBJECTIVE

In this article, our motivation is to predict the type of cell lytic enzyme. Cell lytic enzyme is helpful for killing bacteria, so it is meaningful for study the type of cell lytic enzyme. However, it is time consuming to detect the type of cell lytic enzyme by experimental methods. Thus, an efficient computational method for the type of cell lytic enzyme prediction is proposed in our work.

METHODS

We propose a computational method for the prediction of endolysin and autolysin. First, a data set containing 27 endolysins and 41 autolysins is built. Then the protein is represented by tripeptides composition. The features are selected with larger confidence degree. At last, the classifier is trained by the labeled vectors based on support vector machine. The learned classifier is used to predict the type of cell lytic enzyme.

RESULTS

Following the proposed method, the experimental results show that the overall accuracy can attain 97.06%, when 44 features are selected. Compared with Ding's method, our method improves the overall accuracy by nearly 4.5% ((97.06-92.9)/92.9%). The performance of our proposed method is stable, when the selected feature number is from 40 to 70. The overall accuracy of tripeptides optimal feature set is 94.12%, and the overall accuracy of Chou's amphiphilic PseAAC method is 76.2%. The experimental results also demonstrate that the overall accuracy is improved by nearly 18% when using the tripeptides optimal feature set.

CONCLUSION

The paper proposed an efficient method for identifying endolysin and autolysin. In this paper, support vector machine is used to predict the type of cell lytic enzyme. The experimental results show that the overall accuracy of the proposed method is 94.12%, which is better than some existing methods. In conclusion, the selected 44 features can improve the overall accuracy for identification of the type of cell lytic enzyme. Support vector machine performs better than other classifiers when using the selected feature set on the benchmark data set.

Vibroarthrographic Signal Spectral Features in 5-Class Knee Joint Classification

Vibroarthrography (VAG) is a non-invasive and potentially widely available method supporting the joint diagnosis process. This research was conducted using VAG signals classified to five different condition classes: three stages of chondromalacia patellae, osteoarthritis, and control group (healthy knee joint). Ten new spectral features were proposed, distinguishing not only neighboring classes, but every class combination. Additionally, Frequency Range Maps were proposed as the frequency feature extraction visualization method. The results were compared to state-of-the-art frequency features using the Bhattacharyya coefficient and the set of ten different classification algorithms. All methods evaluating proposed features indicated the superiority of the new features compared to the state-of-the-art. In terms of Bhattacharyya coefficient, newly proposed features proved to be over 25% better, and the classification accuracy was on average 9% better.

Vibroarthrographic analysis of patellofemoral joint arthrokinematics during squats with increasing external loads

Background

The patellofemoral joint (PFJ) provides extremely low kinetic friction, which results in optimal arthrokinematic motion quality. Previous research showed that these friction-reducing properties may be diminished due to the increase in articular contact forces. However, this phenomenon has not been analyzed in vivo during functional daily-living activities. The aim of this study was the vibroarthrographic assessment of changes in PFJ arthrokinematics during squats with variated loads.

Methods

114 knees from 57 asymptomatic subjects (23 females and 34 males) whose ages ranged from 19 to 26 years were enrolled in this study. Participants were asked to perform 3 trials: 4 repetitions of bodyweight squats (L0), 4 repetitions of 10 kg barbell back loaded squats (L10), 4 repetitions of 20 kg barbell back loaded squats (L20). During the unloaded and loaded (L10, L20) squats, vibroarthrographic signals were collected using an accelerometer placed on the patella and were described by the following parameters: variation of mean square (VMS), mean range (R4), and power spectral density for frequency of 50–250 Hz (P1) and 250–450 Hz (P2).

Results

Obtained results showed that the lowest values were noted in the unloaded condition and that the increased applied loads had a significant concomitant increase in all the aforementioned parameters bilaterally (p < 0.05).

Conclusion

This phenomenon indicates that the application of increasing knee loads during squats corresponds to higher intensity of vibroacoustic emission, which might be related to higher contact stress and kinetic friction as well as diminished arthrokinematic motion quality.

Validity of and recommendations for knee joint acoustic assessments during different movement conditions

Knee joint sounds contain information on joint health, morphology and loading. These acoustic signals may be elicited by further, as yet unknown factors. By assessing potential elicitors and their relative contributions to the acoustic signal, we investigated the validity of vibroarthrographic assessments during different movement conditions with the aim to derive recommendations for their practical usage. Cross-sectional study. Nineteen healthy participants (24.7 ± 2.8 yrs, 7 females) performed five movements: level walking, descending stairs, standing up, sitting down, and forward lunge. Knee joint sounds were recorded by two microphones (medial tibial plateau, patella). Knee joint kinematics and ground reaction forces were recorded synchronously to calculate knee joint moments (Nm/Kg). The mean amplitude (dB) and the median power frequency (Hz) were determined. A repeated measures mixed model investigated the impact of potential predictors (sagittal, frontal, transverse plane and total knee joint moments, knee angular velocity, age, sex, body mass index (BMI) and Tegner Activity Score (TAS)). Most of the amplitudes variance is explained by between-subject differences (tibia: 66.6%; patella: 75.8%), and of the median power frequencies variance by the movement condition (tibia: 97.6%; patella: 98.9%). The final model revealed several predictor variables for both sensors (tibia: sagittal plane, frontal plane, and total knee joint moments, age, and TAS; patella: sagittal plane knee moments, knee angular velocity, TAS). The standardization of the execution of the activities, a between-group matching of variables and the inclusion of co-variates are recommended to increase the validity of vibroarthrographic measurements during different movement conditions of the knee joint.

Effects of Immobilization and Re-Mobilization on Knee Joint Arthrokinematic Motion Quality

BACKGROUND

Knee immobilization is a common intervention for patients with traumatic injuries. However, it usually leads to biomechanical/morphological disturbances of articular tissues. These changes may contribute to declining kinetic friction-related quality of arthrokinematics; however, this phenomenon has not been analyzed in vivo and remains unrecognized. Thus, the aim of the present study is to investigate the effect of immobilization and subsequent re-mobilization on the quality of arthrokinematics within the patellofemoral joint, analyzed by vibroarthrography (VAG).

METHODS

Thirty-four patients after 6-weeks of knee immobilization and 37 controls were analyzed. The (VAG) signals were collected during knee flexion/extension using an accelerometer. Patients were tested on the first and last day of the 2-week rehabilitation program.

RESULTS

Immobilized knees were characterized by significantly higher values of all VAG parameters when compared to controls (p < 0.001) on the first day. After 2 weeks, the participants in the rehabilitation program that had immobilized knees showed significant improvement in all measurements compared to the baseline condition, p < 0.05. However, patients did not return to normal VAG parameters compared to controls.

CONCLUSION

Immobilization-related changes within the knee cause impairments of arthrokinematic function reflected in VAG signal patterns. The alterations in joint motion after 6 weeks of immobilization may be partially reversible; however, the 2-week physiotherapy program is not sufficient for full recovery.

Wireless multichannel vibroarthrographic recordings for the assessment of knee osteoarthritis during three activities of daily living

BACKGROUND

Variations in the internal pressure distribution applied to cartilage and synovial fluid explain the spatial dependencies of the knee vibroarthrographic signals. These spatial dependencies were assessed by multi-channel recordings during activities of daily living in patients with painful knee osteoarthrosis.

METHODS

Knee vibroarthrographic signals were detected using eight miniature accelerometers, and vibroarthrographic maps were calculated for the most affected knee of 20 osteoarthritis patients and 20 asymptomatic participants during three activities: (i) sit to stand, (ii) stairs descent, and (iii) stairs ascent in real life conditions. Vibroarthrographic maps of average rectified value, variance of means squared, form factor, mean power frequency, % of recurrence and, % of determinism were obtained from the eight VAG recordings.

FINDINGS

Higher average rectified value and lower % of recurrence were found in knee osteoarthritis patients compared with asymptomatic participants. All vibroarthrographic parameters, except for % of recurrence, differentiated the type of activity. Average rectified value, variance of means squared, form factor, and % of determinism were lowest while mean power frequency was highest during sit-to-stand compared with stairs ascent and descent.

INTERPRETATION

Distinct topographical vibroarthrographic maps underlined that the computed parameters represent unique features. The present study demonstrated that wireless multichannel vibroarthrographic recordings and the associated topographical maps highlighted differences between (i) knee osteoarthritis patients and asymptomatic participants, (ii) sit to stand, stairs descent and ascent and (iii) knee locations. The technique offers new perspectives for biomechanical assessments of physical functions of the knee joint in ecological environment.

Vibroarthrography, arthrophonography — methods for non-invasive detection of the knee cartilage damage

Phonoarthrography, vibration arthrography are non-invasive methods for assessing the condition of cartilage and the knee joint as a whole based on the sounds made by the joint movement. Acoustic sensors (accelerometers, microphones) are attached to the knee to measure the knee joint noise both in control groups (young adults and elderly subjects) and in patients with knee osteoarthropathies. Different authors propose different methods for attaching sensors, documenting and analyzing the joint sounds. The identified specific features allowed distinguishing between asymptomatic knee joints and those with osteoarthropathies. Acoustic signals were recorded and processed, and their frequency characteristics were determined and classified. The classification effectiveness correlated with the existing diagnostic tests and hence phonoarthrography and vibration arthrography can be qualified as a useful diagnostic aid.

Classifying patients with lumbar disc herniation and exploring the most effective risk factors for this disease

Objectives: To classify patients suffering from low back pain (LBP) into two different groups – patients with lumbar disc herniation (LDH) and patients without this disease based on simple questions and without magnetic resonance imaging (MRI) procedure – and to diagnose the most effective risk factors of LDH.

Methods: Four hundred patients aged over 18 years suffering from LBP for over 6 months were randomized into two groups in this cross-sectional study. The data were gathered at Besat clinic, in Kerman, southeast of Iran. Twelve dichotomous questions from the main LDH risk factors were asked. Three statistical classification methods – K-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR) – were performed. LR was used in order to diagnose the most important risk factors of LDH.

Results: SVM method was more efficient among the small sample sizes, while KNN method showed the best classification relative to other methods when the sample size increased. LR model had the least efficiency of all. The drug use increased the chance of LDH more than 7 times (OR=7.249), and the chance of having LDH among people who had associated illness was 4.847 times more compared with people who did not have. Using hookah increased the chance of having LDH more than twice (OR=2.401), and the chance of smokers for LDH was near four times higher than nonsmokers (OR=3.877).

Conclusion: The statistical classification methods had acceptable precisions for diagnosis of LDH patients. It is suggested that neurologists become more familiar with these methods and use them before MRI prescription to decrease the unnecessary burden on health services. Addiction to drugs, cigarettes, and hookah is the main factor in the creation of a lumbar disc herniation.

Analysis of patellofemoral arthrokinematic motion quality in open and closed kinetic chains using vibroarthrography

Background

Knee movements performed in open (OKC) and closed (CKC) kinetic chains generate various patterns of muscle activities and especially distinct contact stresses in the patellofemoral joint (PFJ). In contrast to these features, the arthrokinematic motion quality (AMQ) of the PFJ has not been compared between mentioned conditions. In this study we performed vibroarthrographic analysis of AMQ in movements performed in OKC and CKC, in healthy subjects and individuals with chondromalacia patellae, to assess which of the test conditions is more efficient in differentiation between healthy and deteriorated joints. Moreover, our analysis will broaden the knowledge related to behavior of normal and pathological synovial joints during motion with and without weight bearing. It is an essential issue, due to the recently observed significant interest in comparing potential benefits and limitations of CKC and OKC exercises as they relate to lower extremity rehabilitation.

Methods

100 subjects (62 healthy controls and 38 subjects with PFJ chondromalacia) were enrolled. During repeated knee flexion/extension motions performed in OKC (in a sitting position) and CKC (sit-to-stand movements), the vibroarthrographic signals were collected using an accelerometer and described by variability (VMS), amplitude (R4), and spectral power in 50–250 Hz (P1) and 250–450 Hz (P2) bands.

Results

Significant differences in VMS [V], R4 [V], P1 [V²/Hz] and P2 [V²/Hz] between OKC and CKC were found (0.0001, 0.969. 0.800 0.041 vs 0.013, 3.973, 6.790, 0.768, respectively, P < 0.001). Moreover, in both analyzed load-related conditions the subjects with chondromalacia were characterized by significantly higher values of all parameters, when compared to controls (P < 0.001), with effect size values over 0.6.

Conclusions

We showed that motion of the physiological, unloaded PFJ articular surfaces in OKC is nearly vibrationless, which corresponds with optimal AMQ of PFJ, while loaded movements in CKC are characterized by a higher vibroacoustic emission level. Moreover, chondral lesions should be considered as an increased friction-related, aggravating factor of AMQ, which is critical in CKC movements under load. Nonetheless, OKC and CKC conditions are characterized by large effect sizes, and provide an efficient test frame for differentiating physiological knees and joints with chondral lesions.

Dynamic Analysis of Vibration, Muscle Firing, and Force as a Novel Model for Non-Invasive Assessment of Joint Disruption in the knee: A Multiple Case Report

We present a new method for understanding knee pathology through non-invasive techniques. The combination of electromyography (EMG), vibroarthrographic (VAG), and force analysis in proposed to examine the force transfer between unhealthy and healthy knees. A multiple case report is presented to demonstrate the technique and its potential application for future study. The comparison of four individuals’ knee characteristics will be explained using this innovative methodology.

Analysis and multiclass classification of pathological knee joints using vibroarthrographic signals

BACKGROUND AND OBJECTIVE

Vibroarthrography (VAG) is a method developed for sensitive and objective assessment of articular function. Although the VAG method is still in development, it shows high accuracy, sensitivity and specificity when comparing results obtained from controls and the non-specific, knee-related disorder group. However, the multiclass classification remains practically unknown. Therefore the aim of this study was to extend the VAG method classification to 5 classes, according to different disorders of the patellofemoral joint.

METHODS

We assessed 121 knees of patients (95 knees with grade I-III chondromalacia patellae, 26 with osteoarthritis) and 66 knees from 33 healthy controls. The vibroarthrographic signals were collected during knee flexion/extension motion using an acceleration sensor. The genetic search algorithm was chosen to select the most relevant features of the VAG signal for classification. Four different algorithms were used for classification of selected features: logistic regression with automatic attribute selection (SimpleLogistic in Weka), multilayer perceptron with sigmoid activation function (MultilayerPerceptron), John Platt's sequential minimal optimization algorithm implementation of support vector classifier (SMO) and random forest tree (RandomForest). The generalization error of classification algorithms was evaluated by stratified 10-fold cross-validation.

RESULTS

We obtained levels of accuracy and AUC metrics over 90%, more than 93% sensitivity and more than 84% specificity for the logistic regression-based method (SimpleLogistic) for a 2-class classification. For the 5-class method, we obtained 69% and 90% accuracy and AUC respectively, and sensitivity and specificity over 91% and 69%.

CONCLUSIONS

The results of this study confirm the high usefulness of quantitative analysis of VAG signals based on classification techniques into normal and pathological knees and as a promising tool in classifying signals of various knee joint disorders and their stages.

Complex Network Clustering by a Multi-objective Evolutionary Algorithm Based on Decomposition and Membrane Structure

The field of complex network clustering is gaining considerable attention in recent years. In this study, a multi-objective evolutionary algorithm based on membranes is proposed to solve the network clustering problem. Population are divided into different membrane structures on average. The evolutionary algorithm is carried out in the membrane structures. The population are eliminated by the vector of membranes. In the proposed method, two evaluation objectives termed as Kernel J-means and Ratio Cut are to be minimized. Extensive experimental studies comparison with state-of-the-art algorithms proves that the proposed algorithm is effective and promising.

Recombination Hotspot/Coldspot Identification Combining Three Different Pseudocomponents via an Ensemble Learning Approach

Recombination presents a nonuniform distribution across the genome. Genomic regions that present relatively higher frequencies of recombination are called hotspots while those with relatively lower frequencies of recombination are recombination coldspots. Therefore, the identification of hotspots/coldspots could provide useful information for the study of the mechanism of recombination. In this study, a new computational predictor called SVM-EL was proposed to identify hotspots/coldspots across the yeast genome. It combined Support Vector Machines (SVMs) and Ensemble Learning (EL) based on three features including basic kmer (Kmer), dinucleotide-based auto-cross covariance (DACC), and pseudo dinucleotide composition (PseDNC). These features are able to incorporate the nucleic acid composition and their order information into the predictor. The proposed SVM-EL achieves an accuracy of 82.89% on a widely used benchmark dataset, which outperforms some related methods.

BP Neural Network Could Help Improve Pre-miRNA Identification in Various Species

MicroRNAs (miRNAs) are a set of short (21–24 nt) noncoding RNAs that play significant regulatory roles in cells. In the past few years, research on miRNA-related problems has become a hot field of bioinformatics because of miRNAs' essential biological function. miRNA-related bioinformatics analysis is beneficial in several aspects, including the functions of miRNAs and other genes, the regulatory network between miRNAs and their target mRNAs, and even biological evolution. Distinguishing miRNA precursors from other hairpin-like sequences is important and is an essential procedure in detecting novel microRNAs. In this study, we employed backpropagation (BP) neural network together with 98-dimensional novel features for microRNA precursor identification. Results show that the precision and recall of our method are 95.53% and 96.67%, respectively. Results further demonstrate that the total prediction accuracy of our method is nearly 13.17% greater than the state-of-the-art microRNA precursor prediction software tools.