Neural networks and support vector machines based bio-activity classification

Classification of various compounds into their respective biological activity classes is important in drug discovery applications from an early phase virtual compound filtering and screening point of view. In this work two types of neural networks, multi layer perceptron (MLP) and radial basis functions (RBF), and support vector machines (SVM) were employed for the classification of three types of biologically active enzyme inhibitors. Both of the networks were trained with back propagation learning method with chemical compounds whose active inhibition properties were previously known. A group of topological indices, selected with the help of principle component analysis (PCA) were used as descriptors. The results of all the three classification methods show that the performance of both the neural networks is better than the SVM

Clustering of Chemical Compounds using Unsupervised Neural Networks Algorithms: a comparison

Clustering of chemical databases has tremendous significance in the process of compound selection, virtual screening and in the drug designing and discovery process as a whole. Traditionally, hierarchical methods like Ward’s and Group Average (Gave) and nonhierarchical methods like Jarvis Patrick’s and k-means are preferred methods to cluster a diverse set of compounds for a number of drug targets (using fingerprints based descriptors). In this work the applications of a number of self-organizing map (SOM) neural network algorithms to the clustering of chemical datasets are investigated. The results of the SOM neural networks, Wards and Group-Average methods are evaluated for the clustering of different biologically active chemical molecules that can be used as drug like compounds based on topological descriptors. The results show that the Wards and Group Average methods are equally good; however, the performance of Kohonen neural selforganizing maps (SOM) is also important due to its almost similar performance as the hierarchical clustering methods with the advantage of its efficiency

Fuzzy clustering algorithms and their applications to chemical datasets

In this work the importance of fuzzy based clustering methods is highlighted and their applications in the field of chemoinformatics, and issues involved are reviewed. The various methods and approaches of fuzzy clustering are outlined. The issue of number of valid clusters in a dataset is also discussed. The hyper dimensional chemical datasets are traditionally been treated only with the help of conventional clustering methods like hierarchical and non-hierarchical methods. In this paper we look into the issue of clustering these chemical datasets with fuzzy paradigms. In this paper a number of fuzzy clustering approaches like fuzzy c-mean, Gustafson and Kessel , Gath and Geva, fuzzy c-varieties, adaptive fuzzy , fuzzy based c-shell algorithms and some other aspects of fuzzy clustering are discussed

Characteristics and clinical features of cauda equina syndrome: insights from a study on 256 patients

Objective: To determine the frequency, clinical presentation, and etiological factors of cauda equina syndrome (CES). Materials and method: This retrospective study was done on 256 participants, and aimed to analyze the frequency and patterns of clinical presentation in suspected cases of CES. The inclusion criteria included participants aged 18 or older with medical records available for review and having red-flagged symptoms for CES. The study collected information on various factors such as age, gender, confirmation of CES on MRI, neurological deficits, etiological factors, duration of symptoms, and more. The data collected was analyzed using descriptive statistics and logistic regression to identify significant variables between MRI-proven CES and suspected CES. Results: The mean age was 58.05 ± 19.26 years, with 151 females (58.98%) and 105 males (41.02%). The majority (50.78%) had a neurological deficit, while other symptoms included difficulty initiating micturition or impaired sensation of urinary flow (17.58%), loss of sensation of rectal fullness (3.12%), urinary or faecal incontinence (35.16%), bilateral sciatica (21.88%), neurological symptoms in the lower limbs (25.00%), anaesthesia or any leg weakness (24.22%), and bilateral sciatica as the predominant symptom (21.88%). Symptoms were chronic in 47.27% and acute in 21.88%. The odds of MRI-proven CES increase by 3% per year of age. Neurological deficit was strongly associated with MRI-proven CES (OR = 14.97), while loss of sensation of rectal fullness increased the odds by 10-fold (OR = 10.62). Conclusion: CES can present with various symptoms, including the bilateral neurological deficit, urinary and faecal incontinence, and bilateral sciatica, with age, severe bilateral neurological deficit, and loss of sensation of rectal fullness being associated with MRI-proven CES. Early diagnosis and treatment are crucial for better outcomes