Full "Laplacianised" posterior naive Bayesian algorithm

BACKGROUND: In the last decade the standard Naive Bayes (SNB) algorithm has been widely employed in multi–class classification problems in cheminformatics. This popularity is mainly due to the fact that the algorithm is simple to implement and in many cases yields respectable classification results. Using clever heuristic arguments “anchored” by insightful cheminformatics knowledge, Xia et al. have simplified the SNB algorithm further and termed it the Laplacian Corrected Modified Naive Bayes (LCMNB) approach, which has been widely used in cheminformatics since its publication. In this note we mathematically illustrate the conditions under which Xia et al.’s simplification holds. It is our hope that this clarification could help Naive Bayes practitioners in deciding when it is appropriate to employ the LCMNB algorithm to classify large chemical datasets. RESULTS: A general formulation that subsumes the simplified Naive Bayes version is presented. Unlike the widely used NB method, the Standard Naive Bayes description presented in this work is discriminative (not generative) in nature, which may lead to possible further applications of the SNB method. CONCLUSIONS: Starting from a standard Naive Bayes (SNB) algorithm, we have derived mathematically the relationship between Xia et al.’s ingenious, but heuristic algorithm, and the SNB approach. We have also demonstrated the conditions under which Xia et al.’s crucial assumptions hold. We therefore hope that the new insight and recommendations provided can be found useful by the cheminformatics community

Proposed algorithm for image classification using regression-based pre-processing and recognition models

Image classification algorithms can categorise pixels regarding to image attributes with the pre-processing of learner’s trained samples. The precision and classification accuracy are complex to compute due to the variable size of pixels (different image width and height) and numerous characteristics of image per se. This research proposes an image classification algorithm based on regression-based pre-processing and the recognition models. The proposed algorithm focuses on an optimization of pre-processing results such as accuracy and precision. To evaluate and validate, recognition model is mapped in order to cluster the digital images which are developing the problem of a multidimensional state space. Simulation results show that compared to existing algorithms, the proposed method outperforms with the optimal number of precision and accuracy in classification as well as results higher matching percentage based upon image analytics

Granularity analysis of classification and estimation for complex datasets with MOA

Dispersed and unstructured datasets are substantial parameters to realize an exact amount of the required space. Depending upon the size and the data distribution, especially, if the classes are significantly associating, the level of granularity to agree a precise classification of the datasets exceeds. The data complexity is one of the major attributes to govern the proper value of the granularity, as it has a direct impact on the performance. Dataset classification exhibits the vital step in complex data analytics and designs to ensure that dataset is prompt to be efficiently scrutinized. Data collections are always causing missing, noisy and out-of-the-range values. Data analytics which has not been wisely classified for problems as such can induce unreliable outcomes. Hence, classifications for complex data sources help comfort the accuracy of gathered datasets by machine learning algorithms. Dataset complexity and pre-processing time reflect the effectiveness of individual algorithm. Once the complexity of datasets is characterized then comparatively simpler datasets can further investigate with parallelism approach. Speedup performance is measured by the execution of MOA simulation. Our proposed classification approach outperforms and improves granularity level of complex datasets

Target Fishing: A Single-Label or Multi-Label Problem?

According to Cobanoglu et al and Murphy, it is now widely acknowledged that the single target paradigm (one protein or target, one disease, one drug) that has been the dominant premise in drug development in the recent past is untenable. More often than not, a drug-like compound (ligand) can be promiscuous - that is, it can interact with more than one target protein. In recent years, in in silico target prediction methods the promiscuity issue has been approached computationally in different ways. In this study we confine attention to the so-called ligand-based target prediction machine learning approaches, commonly referred to as target-fishing. With a few exceptions, the target-fishing approaches that are currently ubiquitous in cheminformatics literature can be essentially viewed as single-label multi-classification schemes; these approaches inherently bank on the single target paradigm assumption that a ligand can home in on one specific target. In order to address the ligand promiscuity issue, one might be able to cast target-fishing as a multi-label multi-class classification problem. For illustrative and comparison purposes, single-label and multi-label Naive Bayes classification models (denoted here by SMM and MMM, respectively) for target-fishing were implemented. The models were constructed and tested on 65,587 compounds and 308 targets retrieved from the ChEMBL17 database. SMM and MMM performed differently: for 16,344 test compounds, the MMM model returned recall and precision values of 0.8058 and 0.6622, respectively; the corresponding recall and precision values yielded by the SMM model were 0.7805 and 0.7596, respectively. However, at a significance level of 0.05 and one degree of freedom McNemar test performed on the target prediction results returned by SMM and MMM for the 16,344 test ligands gave a chi-squared value of 15.656, in favour of the MMM approach

Компьютерное прогнозирование спектров биологической активности химических соединений: возможности и ограничения

oai:www.bmc-rm.org:article/4An essential characteristic of chemical compounds is their biological activity since its presence can become the basis for the use of the substance for therapeutic purposes, or, on the contrary, limit the possibilities of its practical application due to the manifestation of side action and toxic effects. Computer assessment of the biological activity spectra makes it possible to determine the most promising directions for the study of the pharmacological action of particular substances, and to filter out potentially dangerous molecules at the early stages of research. For more than 25 years, we have been developing and improving the computer program PASS (Prediction of Activity Spectra for Substances), designed to predict the biological activity spectrum of substance based on the structural formula of its molecules. The prediction is carried out by the analysis of structure-activity relationships for the training set, which currently contains information on structures and known biological activities for more than one million molecules. The structure of the organic compound is represented in PASS using Multilevel Neighborhoods of Atoms descriptors; the activity prediction for new compounds is performed by the naive Bayes classifier and the structure-activity relationships determined by the analysis of the training set. We have created and improved both local versions of the PASS program and freely available web resources based on PASS (http://www.way2drug.com). They predict several thousand biological activities (pharmacological effects, molecular mechanisms of action, specific toxicity and adverse effects, interaction with the unwanted targets, metabolism and action on molecular transport), cytotoxicity for tumor and non-tumor cell lines, carcinogenicity, induced changes of gene expression profiles, metabolic sites of the major enzymes of the first and second phases of xenobiotics biotransformation, and belonging to substrates and/or metabolites of metabolic enzymes. The web resource Way2Drug is used by over 19 000 researchers from more than 100 countries around the world, which allowed them to obtain over 600 000 predictions and publish about 500 papers describing the obtained results. The analysis of the published works shows that in some cases the interpretation of the prediction results presented by the authors of these publications requires an adjustment. In this work, we provide the theoretical basis and consider, on particular examples, the opportunities and limitations of computer-aided prediction of biological activity spectra.Важной характеристикой химических соединений является их биологическая активность, поскольку ее наличие может стать основой для использования вещества в терапевтических целях, либо, напротив, ограничить возможности его практического применения вследствие проявления побочных и токсических эффектов. Компьютерная оценка спектра биологической активности дает возможность определить наиболее перспективные направления для тестирования фармакологического действия конкретных веществ и отсеять потенциально опасные молекулы на ранних стадиях исследований. Свыше 25 лет нами осуществляется разработка и совершенствование компьютерной программы PASS (Prediction of Activity Spectra for Substances), предназначенной для прогнозирования спектра биологической активности вещества по структурной формуле его молекул. Прогноз осуществляется на основе анализа зависимостей «структура-активность» для соединений обучающей выборки, в настоящее время содержащей информацию о структурах и известных видах биологической активности более чем для миллиона молекул. Описание структуры молекул органического соединения реализовано в PASS посредством дескрипторов атомных окрестностей (Multilevel Neighborhoods of Atoms), прогнозирование активности для новых соединений выполняется алгоритмом на основе «наивного Байесовского подхода» и зависимостей «структура-активность», выявляемых при анализе обучающей выборки. Нами созданы и совершенствуются как локальные версии программы PASS, так и свободно доступные в Интернет веб-ресурсы на основе PASS (http://way2drug.com): прогноз нескольких тысяч видов биологической активности (фармакологические эффекты, молекулярные механизмы действия, специфическая токсичность и побочное действие, метаболизм, а также влияние на нежелательные мишени, молекулярный транспорт, генную экспрессию), прогноз цитотоксичности для опухолевых и неопухолевых клеточных линий, прогноз канцерогенности, прогноз индуцированных органическими соединениями изменений профилей экспрессии генов, прогноз взаимодействия с ферментами метаболизма лекарств, в том числе прогноз сайтов метаболизма, а также прогноз принадлежности к субстратам и/или метаболитам этих ферментов. Веб-ресурс Way2Drug используют свыше 19 тысяч исследователей более чем из 100 стран мира, что позволило им осуществить свыше 600 тысяч прогнозов и опубликовать около 500 работ с описанием полученных результатов. Анализ опубликованных работ показывает, что в некоторых случаях приводимая авторами этих публикаций интерпретация результатов прогноза требует корректировки. В рамках настоящей работы мы представим теоретическое обоснование и рассмотрим на конкретных примерах возможности и ограничения компьютерного прогнозирования спектров биологической активности