weka drugs

BACKGROUND: Tuberculosis is the second leading cause of death from an infectious disease worldwide after HIV, thus reasoning the expeditions in antituberculosis research. The rising number of cases of infection by resistant forms of M. tuberculosis has given impetus to the development of novel drugs that have different targets and mechanisms of action against the bacterium. METHODS: In this study, we have used machine learning algorithms on the available high throughput screening data of inhibitors of fructose bisphosphate aldolase, an enzyme central to the glycolysis pathway in M...

Malaria parasites show resistance to most of the antimalarial drugs and hence developing antimalarials which can act on multitargets rather than a single target will be a promising strategy of drug design. Here we report a new approach by which virtual screening of 292 unique phytochemicals present in 72 traditionally important herbs is used for finding out inhibitors of plasmepsin-2 and falcipain-2 for antimalarial activity against P. falciparum. Initial screenings of the selected molecules by Random Forest algorithm model of Weka using the bioassay datasets AID 504850 and AID 2302 screened 120 out of the total 292 phytochemicals to be active against the targets...

Decision trees are renowned in the computational chemistry and machine learning communities for their interpretability. Their capacity and usage are somewhat limited by the fact that they normally work on categorical data. Improvements to known decision tree algorithms are usually carried out by increasing and tweaking parameters, as well as the post-processing of the class assignment. In this work we attempted to tackle both these issues. Firstly, conditional mutual information was used as the criterion for selecting the attribute on which to split instances...

Increasingly, biofilms are being recognised for their causative role in persistent infections (like cystic fibrosis, otitis media, diabetic foot ulcers) and nosocomial diseases (biofilm-infected vascular catheters, implants and prosthetics). Given the clinical relevance of biofilms and their recalcitrance to conventional antibiotics, it is imperative that alternative therapeutics are proactively sought. We have developed dPABBs, a web server that facilitates the prediction and design of anti-biofilm peptides...

Microarray platforms enable the investigation of allelic variants that may be correlated to phenotypes. Among those, the Affymetrix DMET (Drug Metabolism Enzymes and Transporters) platform enables the simultaneous investigation of all the genes that are related to drug absorption, distribution, metabolism and excretion (ADME). Although recent studies demonstrated the effectiveness of the use of DMET data for studying drug response or toxicity in clinical studies, there is a lack of tools for the automatic analysis of DMET data...

Using computational algorithms to design tailored drug cocktails for highly active antiretroviral therapy (HAART) on specific populations is a goal of major importance for both pharmaceutical industry and public health policy institutions. New combinations of compounds need to be predicted in order to design HAART cocktails. On the one hand, there are the biomolecular factors related to the drugs in the cocktail (experimental measure, chemical structure, drug target, assay organisms, etc.); on the other hand, there are the socioeconomic factors of the specific population (income inequalities, employment levels, fiscal pressure, education, migration, population structure, etc...

Machine learning techniques have been widely used in drug discovery and development in the areas of cheminformatics. Aspartyl aminopeptidase (M18AAP) of Plasmodium falciparum is crucial for survival of malaria parasite. We have created predictive models using weka and evaluated their performance based on various statistical parameters. Random Forest based model was found to be the most specificity (97.94%), with best accuracy (97.3%), MCC (0.306) as well as ROC (86.1%). The accuracy and MCC of these models indicated that they could be used to classify huge dataset of unknown compounds to predict their antimalarial compounds to develop effective drugs...

The extracellular matrix (ECM) is a dynamic composite of secreted proteins that play important roles in numerous biological processes such as tissue morphogenesis, differentiation and homeostasis. Furthermore, various diseases are caused by the dysfunction of ECM proteins. Therefore, identifying these important ECM proteins may assist in understanding related biological processes and drug development. In view of the serious imbalance in the training dataset, a Random Forest-based ensemble method with hybrid features is developed in this paper to identify ECM proteins...

BACKGROUND: Identification of drug-like molecules is one of the major challenges in the field of drug discovery. Existing approach like Lipinski rule of 5 (Ro5), Operea have their own limitations. Thus, there is a need to develop computational method that can predict drug-likeness of a molecule with precision. In addition, there is a need to develop algorithm for screening chemical library for their drug-like properties. RESULTS: In this study, we have used 1347 approved and 3206 experimental drugs for developing a knowledge-based computational model for predicting drug-likeness of a molecule...

Rhipicephalus (Boophilus) microplus is a tick that causes huge economic losses in cattle. The indiscriminate use of acaricides has generated resistance to most compounds present on the market. This makes further investigation on other potential acaricides necessary, the in silico assay being an alternative to the design of new compounds. In the present study a biosilico assay was performed using TOMOCOMD-CARDD (TOpological MOlecular COMputer Design-Computer-Aided Rational Drug Design) and WEKA (Waikato Environment for Knowledge Analysis) software...

INTRODUCTION: Decision-tree analysis; a core component of data mining analysis can build predictive models for the therapeutic outcome to antiviral therapy in chronic hepatitis C virus (HCV) patients. AIM: To develop a prediction model for the end virological response (ETR) to pegylated interferon PEG-IFN plus ribavirin (RBV) therapy in chronic HCV patients using routine clinical, laboratory, and histopathological data. PATIENTS AND METHODS: Retrospective initial data (19 attributes) from 3719 Egyptian patients with chronic HCV presumably genotype-4 was assigned to model building using the J48 decision tree-inducing algorithm (Weka implementation of C4...

BACKGROUND: The computational processing and analysis of small molecules is at heart of cheminformatics and structural bioinformatics and their application in e.g. metabolomics or drug discovery. Pipelining or workflow tools allow for the Lego™-like, graphical assembly of I/O modules and algorithms into a complex workflow which can be easily deployed, modified and tested without the hassle of implementing it into a monolithic application. The CDK-Taverna project aims at building a free open-source cheminformatics pipelining solution through combination of different open-source projects such as Taverna, the Chemistry Development Kit (CDK) or the Waikato Environment for Knowledge Analysis (WEKA)...

The stability and hydrolysis kinetics of a phosphate prodrug, adefovir dipivoxil, in solid formulations were studied. The stability relationship between five solid formulations was explored. An autocatalytic mechanism for hydrolysis could be proposed according to the kinetic behavior which fits the Prout-Tompkins model well. For the classical kinetic models could hardly describe and predict the hydrolysis kinetics of adefovir dipivoxil in solid formulations accurately when the temperature is high, a feedforward multilayer perceptron (MLP) neural network was constructed to model the hydrolysis kinetics...

Protein's subcellular location, which indicates where a protein resides in a cell, is an important characteristic of protein. Correctly assigning proteins to their subcellular locations would be of great help to the prediction of proteins' function, genome annotation, and drug design. Yet, in spite of great technical advance in the past decades, it is still time-consuming and laborious to experimentally determine protein subcellular locations on a high throughput scale. Hence, four integrated-algorithm methods were developed to fulfill such high throughput prediction in this article...

OBJECTIVE: Study the actual dosage of clinical prescription in gynecology of traditional Chinese medicine (TCM), and offer the consummate data for formulating the dosage standard of Chinese cut crude drug. METHOD: We made use of the methods of stratified sampling and random sampling to investigate 41,226 broth prescriptions of gynecology of TCM in 21 various hospitals, 18 provinces, and constructed the database, then applied Weka KDD software to analyze. RESULT: We identified the frequency and clinical dosage intervals of 300 drugs, and the practical dosage of 155 drugs which dose has difference between in the actuality and in the pharmacopoeia...

OBJECTIVE: Study the actual dosage of clinical prescription in medical department of traditional Chinese medicine (TCM), and offer the consummate data for formulating the dosage standard of Chinese cut crude drug. METHOD: We made use of the methods of stratified sampling and random sampling to investigate 269 882 broth prescriptions of Medical Department of TCM in 21 various hospitals, 18 provinces, and constructed the database, then applied Weka KDD software to analyze...

OBJECTIVE: Study the actual dosage of clinical prescription in pediatrics of traditional Chinese medicine (TCM), and offer the consummate data for formulating the dosage standard of Chinese cut crude drug. METHOD: We made use of the methods of stratified sampling and random sampling to investigate 38 736 broth prescriptions of pediatrics of TCM in 21 various hospitals, 18 provinces, and constructed the database, then applied Weka KDD software to analyze. RESULT: We identified the frequency and clinical dosage intervals of 300 drugs, and the practical dosage of 155 drugs...

OBJECTIVE: Study the actual dosage of clinical prescription in surgery of traditional Chinese medicine (TCM), and offer the consummate data for formulating the dosage standard of Chinese cut crude drug. METHOD: We made use of the methods of stratified sampling and random sampling to investigate 84,759 broth prescriptions of surgery of TCM in 21 various hospitals, 18 provinces, and constructed the database, then applied Weka KDD software to analyze. RESULT: We identified the frequency and clinical dosage intervals of 300 drugs, and the practical dosage of 155 drugs which dose has difference between in the actuality and in the pharmacopoeia...

The drug discovery and development process is lengthy and expensive, and bringing a drug to market may take up to 18 years and may cost up to 2 billion $US. The extensive use of computer-assisted drug design techniques may considerably increase the chances of finding valuable drug candidates, thus decreasing the drug discovery time and costs. The most important computational approach is represented by structure-activity relationships that can discriminate between sets of chemicals that are active/inactive towards a certain biological receptor...

BACKGROUND: Alzheimer's disease (AD) is a progressive brain disease with a huge cost to human lives. The impact of the disease is also a growing concern for the governments of developing countries, in particular due to the increasingly high number of elderly citizens at risk. Alzheimer's is the most common form of dementia, a common term for memory loss and other cognitive impairments. There is no current cure for AD, but there are drug and non-drug based approaches for its treatment...