Extracting prime protein targets as possible drug candidates: machine learning evaluation.

Extracting "high ranking" or "prime protein targets" (PPTs) as potent MRSA drug candidates from a given set of ligands is a key challenge in efficient molecular docking. This study combines protein-versus-ligand matching molecular docking (MD) data extracted from 10 independent molecular docking (MD) evaluations - ADFR, DOCK, Gemdock, Ledock, Plants, Psovina, Quickvina2, smina, vina, and vinaxb to identify top MRSA drug candidates. Twenty-nine active protein targets (APT) from the enhanced DUD-E repository ( http://DUD-E.decoys.org ) are matched against 1040 ligands using "forward modeling" machine learning for initial "data mining and modeling" (DDM) to extract PPTs and the corresponding high affinity ligands (HALs). K-means clustering (KMC) is then performed on 400 ligands matched against 29 PTs, with each cluster accommodating HALs, and the corresponding PPTs. Performance of KMC is then validated against randomly chosen head, tail, and middle active ligands (ALs). KMC outcomes have been validated against two other clustering methods, namely, Gaussian mixture model (GMM) and density based spatial clustering of applications with noise (DBSCAN). While GMM shows similar results as with KMC, DBSCAN has failed to yield more than one cluster and handle the noise (outliers), thus affirming the choice of KMC or GMM. Databases obtained from ADFR to mine PPTs are then ranked according to the number of the corresponding HAL-PPT combinations (HPC) inside the derived clusters, an approach called "reverse modeling" (RM). From the set of 29 PTs studied, RM predicts high fidelity of 5 PPTs (17%) that bind with 76 out of 400, i.e., 19% ligands leading to a prediction of next-generation MRSA drug candidates: PPT2 (average HPC is 41.1%) is the top choice, followed by PPT14 (average HPC 25.46%), and then PPT15 (average HPC 23.12%). This algorithm can be generically implemented irrespective of pathogenic forms and is particularly effective for sparse data.

Towards Effective Consensus Scoring in Structure-Based Virtual Screening.

Virtual screening (VS) is a computational strategy that uses in silico automated protein docking inter alia to rank potential ligands, or by extension rank protein-ligand pairs, identifying potential drug candidates. Most docking methods use preferred sets of physicochemical descriptors (PCDs) to model the interactions between host and guest molecules. Thus, conventional VS is often data-specific, method-dependent and with demonstrably differing utility in identifying candidate drugs. This study proposes four universality classes of novel consensus scoring (CS) algorithms that combine docking scores, derived from ten docking programs (ADFR, DOCK, Gemdock, Ledock, PLANTS, PSOVina, QuickVina2, Smina, Autodock Vina and VinaXB), using decoys from the DUD-E repository ( http://dude.docking.org/ ) against 29 MRSA-oriented targets to create a general VS formulation that can identify active ligands for any suitable protein target. Our results demonstrate that CS provides improved ligand-protein docking fidelity when compared to individual docking platforms. This approach requires only a small number of docking combinations and can serve as a viable and parsimonious alternative to more computationally expensive docking approaches. Predictions from our CS algorithm are compared against independent machine learning evaluations using the same docking data, complementing the CS outcomes. Our method is a reliable approach for identifying protein targets and high-affinity ligands that can be tested as high-probability candidates for drug repositioning.

Automated diagnosis of normal and alcoholic EEG signals.

Electroencephalogram (EEG) signals, which record the electrical activity in the brain, are useful for assessing the mental state of a person. Since these signals are nonlinear and non-stationary in nature, it is very difficult to decipher the useful information from them using conventional statistical and frequency domain methods. Hence, the application of nonlinear time series analysis to EEG signals could be useful to study the dynamical nature and variability of the brain signals. In this paper, we propose a Computer Aided Diagnostic (CAD) technique for the automated identification of normal and alcoholic EEG signals using nonlinear features. We first extract nonlinear features such as Approximate Entropy (ApEn), Largest Lyapunov Exponent (LLE), Sample Entropy (SampEn), and four other Higher Order Spectra (HOS) features, and then use them to train Support Vector Machine (SVM) classifier of varying kernel functions: 1st, 2nd, and 3rd order polynomials and a Radial basis function (RBF) kernel. Our results indicate that these nonlinear measures are good discriminators of normal and alcoholic EEG signals. The SVM classifier with a polynomial kernel of order 1 could distinguish the two classes with an accuracy of 91.7%, sensitivity of 90% and specificity of 93.3%. As a pre-analysis step, the EEG signals were tested for nonlinearity using surrogate data analysis and we found that there was a significant difference in the LLE measure of the actual data and the surrogate data.

Application of Bayesian classifier for the diagnosis of dental pain.

Toothache is the most common symptom encountered in dental practice. It is subjective and hence, there is a possibility of under or over diagnosis of oral pathologies where patients present with only toothache. Addressing the issue, the paper proposes a methodology to develop a Bayesian classifier for diagnosing some common dental diseases (D = 10) using a set of 14 pain parameters (P = 14). A questionnaire is developed using these variables and filled up by ten dentists (n = 10) with various levels of expertise. Each questionnaire is consisted of 40 real-world cases. Total 14*10*10 combinations of data are hence collected. The reliability of the data (P and D sets) has been tested by measuring (Cronbach's alpha). One-way ANOVA has been used to note the intra and intergroup mean differences. Multiple linear regressions are used for extracting the significant predictors among P and D sets as well as finding the goodness of the model fit. A naïve Bayesian classifier (NBC) is then designed initially that predicts either presence/absence of diseases given a set of pain parameters. The most informative and highest quality datasheet is used for training of NBC and the remaining sheets are used for testing the performance of the classifier. Hill climbing algorithm is used to design a Learned Bayes' classifier (LBC), which learns the conditional probability table (CPT) entries optimally. The developed LBC showed an average accuracy of 72%, which is clinically encouraging to the dentists.

An approach to model Right Iliac Fossa pain using pain-only-parameters for screening acute appendicitis.

Acute appendicitis (AA) is one of the commonest of multiple possible pathologies at the backdrop of Right Iliac Fossa (RIF) pain. RIF is the most common acute surgical condition of the abdomen. Even though AA is a recognized disease entity since decades, its diagnosis still lacks clinical confidence and mandates laboratory tests. Given the issue, this paper proposes a mathematical model using Pain-Only-Parameters (POP) obtained from available literature to screen AA. Weights have been assigned for each POP to create a training data matrix (N = 51) and used to calculate the cumulative effect or weighted sum, which is termed as the Pain Confidence Score (PCS). Based on PCS, a group of real-world patients (N = 40; AA and NA = 20 each) are classified as cases of AA or non-appendicitis (NA) with satisfactory results (sensitivity 85%, specificity 75%, precision 77%, and accuracy 80%). Most rural health centers (RHC) in developing nations lack specialist services and related infrastructure. Hence, such a tool could be useful in RHC to assist general physicians in screening AA and their timely referral to higher centers.

Computer-based identification of type 2 diabetic subjects with and without neuropathy using dynamic planter pressure and principal component analysis.

Diabetes is a chronic disease that is characterized by an increased blood glucose level due to insulin resistance. Type 2 diabetes is common in middle aged and old people. In this work, we present a technique to analyze dynamic foot pressures images and classify them into normal, diabetes type 2 with and without neuropathy classes. Plantar pressure images were obtained using the F-Scan (Tekscan, USA) in-shoe measurement system. We used Principal Component Analysis (PCA) and extracted the eigenvalues from different regions of the foot image. The features extracted from region 1 of the foot pressure image, which were found to be clinically significant, were fed into the Fuzzy classifier (Sugeno model) for automatic classification. Our results show that the proposed method is able to identify the unknown class with an accuracy of 93.7%, sensitivity of 100%, and specificity of 83.3%. Moreover, in this work, we have proposed an integrated index using the eigenvalues to differentiate the normal subjects from diabetes with and without neuropathy subjects using just one number. This index will help the clinicians in easy and objective daily screening, and it can also be used as an adjunct tool to cross check their diagnosis.

A survey and comparative study on the instruments for glaucoma detection.

Glaucoma is one of the leading causes of irreversible blindness worldwide. It has been proposed that the intraocular pressure is a causative factor in the development of glaucoma, which is an optic neuropathy. This paper surveys the use of tonometers, gonioscopes, optical coherence tomographs, scanning laser polarimeters, scanning laser ophthalmoscopes (also known as scanning laser tomographs) and corneal pachymeters for the diagnosis and management of glaucoma. The working mechanisms as well as the comparative advantages and disadvantages of each of these instruments are presented.

A novel mathematical approach to diagnose premenstrual syndrome.

Diagnosis of Premenstrual syndrome (PMS) is a research challenge due to its subjective presentation. An undiagnosed PMS case is often termed as 'borderline' ('B') that further add to the diagnostic fuzziness. This study proposes a methodology to diagnose PMS cases using a combined knowledge engineering and soft computing techniques. According to the guidelines of American College of Gynecology (ACOG), ten symptoms have been selected and technically processed for 50 cases each having class labels-'B' or 'NB' (not borderline) using domain expertise. Any Attribute that fails normality test has been excluded from the study. Decision tree (DT) has then been induced in obtaining the initial class boundaries and mining the important Attributes to classify PMS cases. Prior doing so, the best split criterion has been set using the maximum information gain measure. Initial information about classification boundaries are finally used to measure fuzzy membership values and the corresponding firing strengths have been measured for final classification of PMS 'B' cases.

Neural network approaches to grade adult depression.

Depression is a common but worrying psychological disorder that adversely affects one's quality of life. It is more ominous to note that its incidence is increasing. On the other hand, screening and grading of depression is still a manual and time consuming process that might be biased. In addition, grades of depression are often determined in continuous ranges, e.g., 'mild to moderate' and 'moderate to severe' instead of making them more discrete as 'mild', 'moderate', and 'severe'. Grading as a continuous range is confusing to the doctors and thus affecting the management plan at large. Given this practical issue, the present paper attempts to differentiate depression grades more accurately using two neural net learning approaches-'supervised', i.e., classification with Back propagation neural network (BPNN) and Adaptive Network-based Fuzzy Inference System (ANFIS) classifiers, and 'unsupervised', i.e., 'clustering' technique with Self-organizing map (SOM), built in MATLAB 7. The reason for using the supervised and unsupervised learning approaches is that, supervised learning depends exclusively on domain knowledge. Supervision may induce biasness and subjectivities related to the decision-making. Finally, the performance of BPNN and ANFIS are compared and discussed. It was observed that ANFIS, being a hybrid system, performed much better compared to the BPNN classifier. On the other hand, SOM-based clustering technique is also found useful in constructing three distinct clusters. It also assists visualizing the multidimensional data clusters into 2-D.

Application of recurrence quantification analysis for the automated identification of epileptic EEG signals.

Epilepsy is a common neurological disorder that is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals are widely used to diagnose seizures. Because of the non-linear and dynamic nature of the EEG signals, it is difficult to effectively decipher the subtle changes in these signals by visual inspection and by using linear techniques. Therefore, non-linear methods are being researched to analyze the EEG signals. In this work, we use the recorded EEG signals in Recurrence Plots (RP), and extract Recurrence Quantification Analysis (RQA) parameters from the RP in order to classify the EEG signals into normal, ictal, and interictal classes. Recurrence Plot (RP) is a graph that shows all the times at which a state of the dynamical system recurs. Studies have reported significantly different RQA parameters for the three classes. However, more studies are needed to develop classifiers that use these promising features and present good classification accuracy in differentiating the three types of EEG segments. Therefore, in this work, we have used ten RQA parameters to quantify the important features in the EEG signals.These features were fed to seven different classifiers: Support vector machine (SVM), Gaussian Mixture Model (GMM), Fuzzy Sugeno Classifier, K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), Decision Tree (DT), and Radial Basis Probabilistic Neural Network (RBPNN). Our results show that the SVM classifier was able to identify the EEG class with an average efficiency of 95.6%, sensitivity and specificity of 98.9% and 97.8%, respectively.

A framework for studying perceptions of rural healthcare staff and basic ICT support for e-health use: an Indian experience.

Current research observes that electronic healthcare has various advantages, such as easy recording, retrieval, and sharing of patient data anytime and anywhere while providing data privacy. Almost all developed countries currently practice e-health. On the other hand, many developing countries still rely on traditional paper-based healthcare systems that are quite vulnerable to data loss, loss of patients' privacy due to nonsecured data sharing, and mandatory consumption of physical space to store patients' records as stacks of files. India is a developing country that broadly applies a traditional healthcare system. Unfortunately, no studies have been conducted to identify precise reasons why e-health solutions have not been adopted in the Indian primary health centers (PHCs). To fill the research gap, this work is an attempt to propose a complete framework that includes (1) a systematic survey of available resources at the level of healthcare staffs' perceptions toward using e-health and basic information communication technology (ICT) supports at the organizational level and (2) a mathematical model to engineer significant factors for analysis of overall preparedness of the health centers. Healthcare administrators (Block Medical Officer of Health) from each PHC (n = 10) and in total 50 healthcare staff (e.g., doctors, nurses, pharmacists, and midwives) participated in the study. Initially, a systematic survey was conducted to explore the possible factors at the individual (e.g., healthcare personnel) and organizational (e.g., healthcare administration) levels. A questionnaire was generated to capture the data based on the factors identified. The collected data were mathematically modeled to run regressions with significance tests examining the effects of these factors on the level of satisfaction of the end users. The result shows that basic ICT for support at the organizational levels is significantly lacking to implement e-health in these PHCs, although healthcare staffs are ready to use it. Proper measures have to be adopted mostly at the organizational level, such as improving basic ICT support before what will in all probability be a successful implementation and practice of e-health in Indian PHCs.