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Wavelet energy-guided level set-based active contour: a segmentation method to segment highly similar regions

Achuthan A, Rajeswari M, Ramachandram D, Aziz ME, Shuaib IL

Comput Biol Med, 2010 Jul;40(7):608-20.
PMID: 20541182 DOI: 10.1016/j.compbiomed.2010.04.005

This paper introduces an approach to perform segmentation of regions in computed tomography (CT) images that exhibit intra-region intensity variations and at the same time have similar intensity distributions with surrounding/adjacent regions. In this work, we adapt a feature computed from wavelet transform called wavelet energy to represent the region information. The wavelet energy is embedded into a level set model to formulate the segmentation model called wavelet energy-guided level set-based active contour (WELSAC). The WELSAC model is evaluated using several synthetic and CT images focusing on tumour cases, which contain regions demonstrating the characteristics of intra-region intensity variations and having high similarity in intensity distributions with the adjacent regions. The obtained results show that the proposed WELSAC model is able to segment regions of interest in close correspondence with the manual delineation provided by the medical experts and to provide a solution for tumour detection.
Vascular intersection detection in retina fundus images using a new hybrid approach

Aibinu AM, Iqbal MI, Shafie AA, Salami MJ, Nilsson M

Comput Biol Med, 2010 Jan;40(1):81-9.
PMID: 20022595 DOI: 10.1016/j.compbiomed.2009.11.004

The use of vascular intersection aberration as one of the signs when monitoring and diagnosing diabetic retinopathy from retina fundus images (FIs) has been widely reported in the literature. In this paper, a new hybrid approach called the combined cross-point number (CCN) method able to detect the vascular bifurcation and intersection points in FIs is proposed. The CCN method makes use of two vascular intersection detection techniques, namely the modified cross-point number (MCN) method and the simple cross-point number (SCN) method. Our proposed approach was tested on images obtained from two different and publicly available fundus image databases. The results show a very high precision, accuracy, sensitivity and low false rate in detecting both bifurcation and crossover points compared with both the MCN and the SCN methods.
Utilizing shared interacting domain patterns and Gene Ontology information to improve protein-protein interaction prediction

Roslan R, Othman RM, Shah ZA, Kasim S, Asmuni H, Taliba J, et al.

Comput Biol Med, 2010 Jun;40(6):555-64.
PMID: 20417930 DOI: 10.1016/j.compbiomed.2010.03.009

Protein-protein interactions (PPIs) play a significant role in many crucial cellular operations such as metabolism, signaling and regulations. The computational methods for predicting PPIs have shown tremendous growth in recent years, but problem such as huge false positive rates has contributed to the lack of solid PPI information. We aimed at enhancing the overlap between computational predictions and experimental results in an effort to partially remove PPIs falsely predicted. The use of protein function predictor named PFP() that are based on shared interacting domain patterns is introduced in this study with the purpose of aiding the Gene Ontology Annotations (GOA). We used GOA and PFP() as agents in a filtering process to reduce false positive pairs in the computationally predicted PPI datasets. The functions predicted by PFP() were extracted from cross-species PPI data in order to assign novel functional annotations for the uncharacterized proteins and also as additional functions for those that are already characterized by the GO (Gene Ontology). The implementation of PFP() managed to increase the chances of finding matching function annotation for the first rule in the filtration process as much as 20%. To assess the capability of the proposed framework in filtering false PPIs, we applied it on the available S. cerevisiae PPIs and measured the performance in two aspects, the improvement made indicated as Signal-to-Noise Ratio (SNR) and the strength of improvement, respectively. The proposed filtering framework significantly achieved better performance than without it in both metrics.
Utilizing hybrid functional fuzzy wavelet neural networks with a teaching learning-based optimization algorithm for medical disease diagnosis

Majeed Alneamy JS, A Hameed Alnaish Z, Mohd Hashim SZ, Hamed Alnaish RA

Comput Biol Med, 2019 09;112:103348.
PMID: 31356992 DOI: 10.1016/j.compbiomed.2019.103348

Accurate medical disease diagnosis is considered to be an important classification problem. The main goal of the classification process is to determine the class to which a certain pattern belongs. In this article, a new classification technique based on a combination of The Teaching Learning-Based Optimization (TLBO) algorithm and Fuzzy Wavelet Neural Network (FWNN) with Functional Link Neural Network (FLNN) is proposed. In addition, the TLBO algorithm is utilized for training the new hybrid Functional Fuzzy Wavelet Neural Network (FFWNN) and optimizing the learning parameters, which are weights, dilation and translation. To evaluate the performance of the proposed method, five standard medical datasets were used: Breast Cancer, Heart Disease, Hepatitis, Pima-Indian diabetes and Appendicitis. The efficiency of the proposed method is evaluated using 5-fold cross-validation and 10-fold cross-validation in terms of mean square error (MSE), classification accuracy, running time, sensitivity, specificity and kappa. The experimental results show that the efficiency of the proposed method for the medical classification problems is 98.309%, 91.1%, 91.39%, 88.67% and 93.51% for the Breast Cancer, Heart Disease, Hepatitis, Pima-Indian diabetes and Appendicitis datasets, respectively, in terms of accuracy after 30 runs for each dataset with low computational complexity. In addition, it has been observed that the proposed method has efficient performance compared with the performance of other methods found in the related previous studies.
Unsteady magnetohydrodynamic blood flow through irregular multi-stenosed arteries

Mustapha N, Amin N, Chakravarty S, Mandal PK

Comput Biol Med, 2009 Oct;39(10):896-906.
PMID: 19665698 DOI: 10.1016/j.compbiomed.2009.07.004

Flow of an electrically conducting fluid characterizing blood through the arteries having irregular shaped multi-stenoses in the environment of a uniform transverse magnetic-field is analysed. The flow is considered to be axisymmetric with an outline of the irregular stenoses obtained from a three-dimensional casting of a mild stenosed artery, so that the physical problem becomes more realistic from the physiological point of view. The marker and cell (MAC) and successive-over-relaxation (SOR) methods are respectively used to solve the governing unsteady magnetohydrodynamic (MHD) equations and pressure-Poisson equation quantitatively and to observe the flow separation. The results obtained show that the flow separates mostly towards the downstream of the multi-stenoses. However, the flow separation region keeps on shrinking with the increasing intensity of the magnetic-field which completely disappears with sufficiently large value of the Hartmann number. The present observations certainly have some clinical implications relating to magnetotherapy which help reducing the complex flow separation zones causing flow disorder leading to the formation and progression of the arterial diseases.
Unidirectional ablation minimizes unwanted thermal damage and promotes better thermal ablation efficacy in time-based switching bipolar radiofrequency ablation

Ooi EH, Ooi ET

Comput Biol Med, 2021 10;137:104832.
PMID: 34508975 DOI: 10.1016/j.compbiomed.2021.104832

Switching bipolar radiofrequency ablation (bRFA) is a thermal treatment modality used for liver cancer treatment that is capable of producing larger, more confluent and more regular thermal coagulation. When implemented in the no-touch mode, switching bRFA can prevent tumour track seeding; a medical phenomenon defined by the deposition of cancer cells along the insertion track. Nevertheless, the no-touch mode was found to yield significant unwanted thermal damage as a result of the electrodes' position outside the tumour. It is postulated that the unwanted thermal damage can be minimized if ablation can be directed such that it focuses only within the tumour domain. As it turns out, this can be achieved by partially insulating the active tip of the RF electrodes such that electric current flows in and out of the tissue only through the non-insulated section of the electrode. This concept is known as unidirectional ablation and has been shown to produce the desired effect in monopolar RFA. In this paper, computational models based on a well-established mathematical framework for modelling RFA was developed to investigate if unidirectional ablation can minimize unwanted thermal damage during time-based switching bRFA. From the numerical results, unidirectional ablation was shown to produce treatment efficacy of nearly 100%, while at the same time, minimizing the amount of unwanted thermal damage. Nevertheless, this effect was observed only when the switch interval of the time-based protocol was set to 50 s. An extended switch interval negated the benefits of unidirectional ablation.
The effects of electrical and thermal boundary condition on the simulation of radiofrequency ablation of liver cancer for tumours located near to the liver boundary

Ooi EH, Lee KW, Yap S, Khattab MA, Liao IY, Ooi ET, et al.

Comput Biol Med, 2019 03;106:12-23.
PMID: 30665137 DOI: 10.1016/j.compbiomed.2019.01.003

Effects of different boundary conditions prescribed across the boundaries of radiofrequency ablation (RFA) models of liver cancer are investigated for the case where the tumour is at the liver boundary. Ground and Robin-type conditions (electrical field) and body temperature and thermal insulation (thermal field) conditions are examined. 3D models of the human liver based on publicly-available CT images of the liver are developed. An artificial tumour is placed inside the liver at the boundary. Simulations are carried out using the finite element method. The numerical results indicated that different electrical and thermal boundary conditions led to different predictions of the electrical potential, temperature and thermal coagulation distributions. Ground and body temperature conditions presented an unnatural physical conditions around the ablation site, which results in more intense Joule heating and excessive heat loss from the tissue. This led to thermal damage volumes that are smaller than the cases when the Robin type or the thermal insulation conditions are prescribed. The present study suggests that RFA simulations in the future must take into consideration the choice of the type of electrical and thermal boundary conditions to be prescribed in the case where the tumour is located near to the liver boundary.
Fulltext Telehealth utilization during the Covid-19 pandemic: A systematic review

Garfan S, Alamoodi AH, Zaidan BB, Al-Zobbi M, Hamid RA, Alwan JK, et al.

Comput Biol Med, 2021 Nov;138:104878.
PMID: 34592585 DOI: 10.1016/j.compbiomed.2021.104878

During the coronavirus disease (COVID-19) pandemic, different technologies, including telehealth, are maximised to mitigate the risks and consequences of the disease. Telehealth has been widely utilised because of its usability and safety in providing healthcare services during the COVID-19 pandemic. However, a systematic literature review which provides extensive evidence on the impact of COVID-19 through telehealth and which covers multiple directions in a large-scale research remains lacking. This study aims to review telehealth literature comprehensively since the pandemic started. It also aims to map the research landscape into a coherent taxonomy and characterise this emerging field in terms of motivations, open challenges and recommendations. Articles related to telehealth during the COVID-19 pandemic were systematically searched in the WOS, IEEE, Science Direct, Springer and Scopus databases. The final set included (n = 86) articles discussing telehealth applications with respect to (i) control (n = 25), (ii) technology (n = 14) and (iii) medical procedure (n = 47). Since the beginning of the pandemic, telehealth has been presented in diverse cases. However, it still warrants further attention. Regardless of category, the articles focused on the challenges which hinder the maximisation of telehealth in such times and how to address them. With the rapid increase in the utilization of telehealth in different specialised hospitals and clinics, a potential framework which reflects the authors' implications of the future application and opportunities of telehealth has been established. This article improves our understanding and reveals the full potential of telehealth during these difficult times and beyond.
Structural probing of HapR to identify potent phytochemicals to control Vibrio cholera through integrated computational approaches

Tahir Ul Qamar M, Ahmad S, Khan A, Mirza MU, Ahmad S, Abro A, et al.

Comput Biol Med, 2021 11;138:104929.
PMID: 34655900 DOI: 10.1016/j.compbiomed.2021.104929

Cholera is a severe small intestine bacterial disease caused by consumption of water and food contaminated with Vibrio cholera. The disease causes watery diarrhea leading to severe dehydration and even death if left untreated. In the past few decades, V. cholerae has emerged as multidrug-resistant enteric pathogen due to its rapid ability to adapt in detrimental environmental conditions. This research study aimed to design inhibitors of a master virulence gene expression regulator, HapR. HapR is critical in regulating the expression of several set of V. cholera virulence genes, quorum-sensing circuits and biofilm formation. A blind docking strategy was employed to infer the natural binding tendency of diverse phytochemicals extracted from medicinal plants by exposing the whole HapR structure to the screening library. Scoring function criteria was applied to prioritize molecules with strong binding affinity (binding energy
Stone detection in MRCP images using controlled region growing

Logeswaran R, Eswaran C

Comput Biol Med, 2007 Aug;37(8):1084-91.
PMID: 17112496

Stones in the biliary tract are routinely identified using MRCP (magnetic resonance cholangiopancreatography). The noisy nature of the images, as well as varying intensity, size and location of the stones, defeat most automatic detection algorithms, making computer-aided diagnosis difficult. This paper proposes a multi-stage segment-based scheme for semi-automated detection of choledocholithiasis and cholelithiasis in the MRCP images, producing good performance in tests, differentiating them from "normal" MRCP images. With the high success rate of over 90%, refinement of the scheme could be applicable in the clinical environment as a tool in aiding diagnosis, with possible applications in telemedicine.
Shear wave elastography for characterization of breast lesions: Shearlet transform and local binary pattern histogram techniques

Acharya UR, Ng WL, Rahmat K, Sudarshan VK, Koh JEW, Tan JH, et al.

Comput Biol Med, 2017 12 01;91:13-20.
PMID: 29031099 DOI: 10.1016/j.compbiomed.2017.10.001

Shear wave elastography (SWE) examination using ultrasound elastography (USE) is a popular imaging procedure for obtaining elasticity information of breast lesions. Elasticity parameters obtained through SWE can be used as biomarkers that can distinguish malignant breast lesions from benign ones. Furthermore, the elasticity parameters extracted from SWE can speed up the diagnosis and possibly reduce human errors. In this paper, Shearlet transform and local binary pattern histograms (LBPH) are proposed as an original algorithm to differentiate malignant and benign breast lesions. First, Shearlet transform is applied on the SWE images to acquire low frequency, horizontal and vertical cone coefficients. Next, LBPH features are extracted from the Shearlet transform coefficients and subjected to dimensionality reduction using locality sensitivity discriminating analysis (LSDA). The reduced LSDA components are ranked and then fed to several classifiers for the automated classification of breast lesions. A probabilistic neural network classifier trained only with seven top ranked features performed best, and achieved 98.08% accuracy, 98.63% sensitivity, and 97.59% specificity in distinguishing malignant from benign breast lesions. The high sensitivity and specificity of our system indicates that it can be employed as a primary screening tool for faster diagnosis of breast malignancies, thereby possibly reducing the mortality rate due to breast cancer.
Fulltext Scenario analysis of COVID-19 transmission dynamics in Malaysia with the possibility of reinfection and limited medical resources scenarios

Salman AM, Ahmed I, Mohd MH, Jamiluddin MS, Dheyab MA

Comput Biol Med, 2021 06;133:104372.
PMID: 33864970 DOI: 10.1016/j.compbiomed.2021.104372

COVID-19 is a major health threat across the globe, which causes severe acute respiratory syndrome (SARS), and it is highly contagious with significant mortality. In this study, we conduct a scenario analysis for COVID-19 in Malaysia using a simple universality class of the SIR system and extensions thereof (i.e., the inclusion of temporary immunity through the reinfection problems and limited medical resources scenarios leads to the SIRS-type model). This system has been employed in order to provide further insights on the long-term outcomes of COVID-19 pandemic. As a case study, the COVID-19 transmission dynamics are investigated using daily confirmed cases in Malaysia, where some of the epidemiological parameters of this system are estimated based on the fitting of the model to real COVID-19 data released by the Ministry of Health Malaysia (MOH). We observe that this model is able to mimic the trend of infection trajectories of COVID-19 pandemic in Malaysia and it is possible for transmission dynamics to be influenced by the reinfection force and limited medical resources problems. A rebound effect in transmission could occur after several years and this situation depends on the intensity of reinfection force. Our analysis also depicts the existence of a critical value in reinfection threshold beyond which the infection dynamics persist and the COVID-19 outbreaks are rather hard to eradicate. Therefore, understanding the interplay between distinct epidemiological factors using mathematical modelling approaches could help to support authorities in making informed decisions so as to control the spread of this pandemic effectively.
SVR modelling of mechanomyographic signals predicts neuromuscular stimulation-evoked knee torque in paralyzed quadriceps muscles undergoing knee extension exercise

Ibitoye MO, Hamzaid NA, Abdul Wahab AK, Hasnan N, Olatunji SO, Davis GM

Comput Biol Med, 2020 02;117:103614.
PMID: 32072969 DOI: 10.1016/j.compbiomed.2020.103614

BACKGROUND AND OBJECTIVE: Using traditional regression modelling, we have previously demonstrated a positive and strong relationship between paralyzed knee extensors' mechanomyographic (MMG) signals and neuromuscular electrical stimulation (NMES)-assisted knee torque in persons with spinal cord injuries. In the present study, a method of estimating NMES-evoked knee torque from the knee extensors' MMG signals using support vector regression (SVR) modelling is introduced and performed in eight persons with chronic and motor complete spinal lesions.
METHODS: The model was developed to estimate knee torque from experimentally derived MMG signals and other parameters related to torque production, including the knee angle and stimulation intensity, during NMES-assisted knee extension.
RESULTS: When the relationship between the actual and predicted torques was quantified using the coefficient of determination (R2), with a Gaussian support vector kernel, the R2 value indicated an estimation accuracy of 95% for the training subset and 94% for the testing subset while the polynomial support vector kernel indicated an accuracy of 92% for the training subset and 91% for the testing subset. For the Gaussian kernel, the root mean square error of the model was 6.28 for the training set and 8.19 for testing set, while the polynomial kernels for the training and testing sets were 7.99 and 9.82, respectively.
CONCLUSIONS: These results showed good predictive accuracy for SVR modelling, which can be generalized, and suggested that the MMG signals from paralyzed knee extensors are a suitable proxy for the NMES-assisted torque produced during repeated bouts of isometric knee extension tasks. This finding has potential implications for using MMG signals as torque sensors in NMES closed-loop systems and provides valuable information for implementing this method in research and clinical settings.
SPlitSSI-SVM: an algorithm to reduce the misleading and increase the strength of domain signal

Kalsum HU, Shah ZA, Othman RM, Hassan R, Rahim SM, Asmuni H, et al.

Comput Biol Med, 2009 Nov;39(11):1013-9.
PMID: 19720371 DOI: 10.1016/j.compbiomed.2009.08.002

Protein domains contain information about the prediction of protein structure, function, evolution and design since the protein sequence may contain several domains with different or the same copies of the protein domain. In this study, we proposed an algorithm named SplitSSI-SVM that works with the following steps. First, the training and testing datasets are generated to test the SplitSSI-SVM. Second, the protein sequence is split into subsequence based on order and disorder regions. The protein sequence that is more than 600 residues is split into subsequences to investigate the effectiveness of the protein domain prediction based on subsequence. Third, multiple sequence alignment is performed to predict the secondary structure using bidirectional recurrent neural networks (BRNN) where BRNN considers the interaction between amino acids. The information of about protein secondary structure is used to increase the protein domain boundaries signal. Lastly, support vector machines (SVM) are used to classify the protein domain into single-domain, two-domain and multiple-domain. The SplitSSI-SVM is developed to reduce misleading signal, lower protein domain signal caused by primary structure of protein sequence and to provide accurate classification of the protein domain. The performance of SplitSSI-SVM is evaluated using sensitivity and specificity on single-domain, two-domain and multiple-domain. The evaluation shows that the SplitSSI-SVM achieved better results compared with other protein domain predictors such as DOMpro, GlobPlot, Dompred-DPS, Mateo, Biozon, Armadillo, KemaDom, SBASE, HMMPfam and HMMSMART especially in two-domain and multiple-domain.
Role of saline concentration during saline-infused radiofrequency ablation: Observation of secondary Joule heating along the saline-tissue interface

Kho AS, Ooi EH, Foo JJ, Ooi ET

Comput Biol Med, 2021 01;128:104112.
PMID: 33212331 DOI: 10.1016/j.compbiomed.2020.104112

Infusion of saline prior to radiofrequency ablation (RFA) is known to enlarge the thermal coagulation zone. The abundance of ions in saline elevate the electrical conductivity of the saline-saturated region. This promotes greater electric current flow inside the tissue, which increases the amount of RF energy deposition and subsequently enlarges the coagulation zone. In theory, infusion of higher concentration of saline should lead to larger coagulation zone due to the greater number of ions. Nevertheless, existing studies on the effects of concentration on saline-infused RFA have been conflicting, with the exact role of saline concentration yet to be fully elucidated. In this paper, computational models of saline-infused RFA were developed to investigate the role of saline concentration on the outcome of saline-infused RFA. The elevation in tissue electrical conductivity was modelled using the microscopic mixture model, while RFA was modelled using the coupled dual porosity-Joule heating model. Results obtained indicated that the presence of a concentration threshold to which no further elevation in tissue electrical conductivity and enlargement in thermal coagulation can occur. This threshold was determined to be at 15% NaCl. Analysis of the Joule heating distribution revealed the presence of a secondary Joule heating site located along the interface between wet and dry tissue. This secondary Joule heating was responsible for the enlargement in coagulation volume and its rapid growth phase during ablation.
Robust biometric system using session invariant multimodal EEG and keystroke dynamics by the ensemble of self-ONNs

Rahman A, Chowdhury MEH, Khandakar A, Tahir AM, Ibtehaz N, Hossain MS, et al.

Comput Biol Med, 2022 Mar;142:105238.
PMID: 35077938 DOI: 10.1016/j.compbiomed.2022.105238

Harnessing the inherent anti-spoofing quality from electroencephalogram (EEG) signals has become a potential field of research in recent years. Although several studies have been conducted, still there are some vital challenges present in the deployment of EEG-based biometrics, which is stable and capable of handling the real-world scenario. One of the key challenges is the large signal variability of EEG when recorded on different days or sessions which impedes the performance of biometric systems significantly. To address this issue, a session invariant multimodal Self-organized Operational Neural Network (Self-ONN) based ensemble model combining EEG and keystroke dynamics is proposed in this paper. Our model is tested successfully on a large number of sessions (10 recording days) with many challenging noisy and variable environments for the identification and authentication tasks. In most of the previous studies, training and testing were performed either over a single recording session (same day) only or without ensuring appropriate splitting of the data on multiple recording days. Unlike those studies, in our work, we have rigorously split the data so that train and test sets do not share the data of the same recording day. The proposed multimodal Self-ONN based ensemble model has achieved identification accuracy of 98% in rigorous validation cases and outperformed the equivalent ensemble of deep CNN models. A novel Self-ONN Siamese network has also been proposed to measure the similarity of templates during the authentication task instead of the commonly used simple distance measure techniques. The multimodal Siamese network reduces the Equal Error Rate (EER) to 1.56% in rigorous authentication. The obtained results indicate that the proposed multimodal Self-ONN model can automatically extract session invariant unique non-linear features to identify and authenticate users with high accuracy.
Review on CFD simulation in heart with dilated cardiomyopathy and myocardial infarction

Chan BT, Lim E, Chee KH, Abu Osman NA

Comput Biol Med, 2013 May;43(4):377-85.
PMID: 23428371 DOI: 10.1016/j.compbiomed.2013.01.013

The heart is a sophisticated functional organ that plays a crucial role in the blood circulatory system. Hemodynamics within the heart chamber can be indicative of exert cardiac health. Due to the limitations of current cardiac imaging modalities, computational fluid dynamics (CFD) have been widely used for the purposes of cardiac function assessment and heart disease diagnosis, as they provide detailed insights into the cardiac flow field. An understanding of ventricular hemodynamics and pathological severities can be gained through studies that employ the CFD method. In this research the hemodynamics of two common myocardial diseases, dilated cardiomyopathy (DCM) and myocardial infarction (MI) were investigated, during both the filling phase and the whole cardiac cycle, through a prescribed geometry and fluid structure interaction (FSI) approach. The results of the research indicated that early stage disease identification and the improvement of cardiac assisting devices and therapeutic procedures can be facilitated through the use of the CFD method.
Retinal image analysis for disease screening through local tetra patterns

Porwal P, Pachade S, Kokare M, Giancardo L, Mériaudeau F

Comput Biol Med, 2018 11 01;102:200-210.
PMID: 30308336 DOI: 10.1016/j.compbiomed.2018.09.028

Age-related Macular Degeneration (AMD) and Diabetic Retinopathy (DR) are the most prevalent diseases responsible for visual impairment in the world. This work investigates discrimination potential in the texture of color fundus images to distinguish between diseased and healthy cases by avoiding the prior lesion segmentation step. It presents a retinal background characterization approach and explores the potential of Local Tetra Patterns (LTrP) for texture classification of AMD, DR and Normal images. Five different experiments distinguishing between DR - normal, AMD - normal, DR - AMD, pathological - normal and AMD - DR - normal cases were conducted and validated using the proposed approach, and promising results were obtained. For all five experiments, different classifiers namely, AdaBoost, c4.5, logistic regression, naive Bayes, neural network, random forest and support vector machine were tested. We experimented with three public datasets, ARIA, STARE and E-Optha. Further, the performance of LTrP is compared with other texture descriptors, such as local phase quantization, local binary pattern and local derivative pattern. In all cases, the proposed method obtained the area under the receiver operating characteristic curve and f-score values higher than 0.78 and 0.746 respectively. It was found that both performance measures achieve over 0.995 for DR and AMD detection using a random forest classifier. The obtained results suggest that the proposed technique can discriminate retinal disease using texture information and has potential to be an important component for an automated screening solution for retinal images.
Remote protein homology detection and fold recognition using two-layer support vector machine classifiers

Muda HM, Saad P, Othman RM

Comput Biol Med, 2011 Aug;41(8):687-99.
PMID: 21704312 DOI: 10.1016/j.compbiomed.2011.06.004

Remote protein homology detection and fold recognition refer to detection of structural homology in proteins where there are small or no similarities in the sequence. To detect protein structural classes from protein primary sequence information, homology-based methods have been developed, which can be divided to three types: discriminative classifiers, generative models for protein families and pairwise sequence comparisons. Support Vector Machines (SVM) and Neural Networks (NN) are two popular discriminative methods. Recent studies have shown that SVM has fast speed during training, more accurate and efficient compared to NN. We present a comprehensive method based on two-layer classifiers. The 1st layer is used to detect up to superfamily and family in SCOP hierarchy using optimized binary SVM classification rules. It used the kernel function known as the Bio-kernel, which incorporates the biological information in the classification process. The 2nd layer uses discriminative SVM algorithm with string kernel that will detect up to protein fold level in SCOP hierarchy. The results obtained were evaluated using mean ROC and mean MRFP and the significance of the result produced with pairwise t-test was tested. Experimental results show that our approaches significantly improve the performance of remote protein homology detection and fold recognition for all three different version SCOP datasets (1.53, 1.67 and 1.73). We achieved 4.19% improvements in term of mean ROC in SCOP 1.53, 4.75% in SCOP 1.67 and 4.03% in SCOP 1.73 datasets when compared to the result produced by well-known methods. The combination of first layer and second layer of BioSVM-2L performs well in remote homology detection and fold recognition even in three different versions of datasets.
Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification

Algamal ZY, Lee MH

Comput Biol Med, 2015 Dec 1;67:136-45.
PMID: 26520484 DOI: 10.1016/j.compbiomed.2015.10.008

Cancer classification and gene selection in high-dimensional data have been popular research topics in genetics and molecular biology. Recently, adaptive regularized logistic regression using the elastic net regularization, which is called the adaptive elastic net, has been successfully applied in high-dimensional cancer classification to tackle both estimating the gene coefficients and performing gene selection simultaneously. The adaptive elastic net originally used elastic net estimates as the initial weight, however, using this weight may not be preferable for certain reasons: First, the elastic net estimator is biased in selecting genes. Second, it does not perform well when the pairwise correlations between variables are not high. Adjusted adaptive regularized logistic regression (AAElastic) is proposed to address these issues and encourage grouping effects simultaneously. The real data results indicate that AAElastic is significantly consistent in selecting genes compared to the other three competitor regularization methods. Additionally, the classification performance of AAElastic is comparable to the adaptive elastic net and better than other regularization methods. Thus, we can conclude that AAElastic is a reliable adaptive regularized logistic regression method in the field of high-dimensional cancer classification.

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