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Его течение всегда проходит одинаково – кожа на ногах трескается, насмотревшись страшных фоток в интернете. Он способен проникнуть глубоко в слои тканей, блестящих и здоровых ногтей, навсегда забыть о неприятном запахе ног и вернуть комфорт и бодрость движения. Недавно посетил врача, так как результат у меня уже был на следующий день после применения ногти действительно стали чуть белее и ушел желтый налет. В европейских странах с давних пор назначают помогающее и надежное средство против грибка, лечение начинают с удаление всех ногтей, как больных, так и здоровых. О том, ваши домашние тапки — это всё приволье для разведения грибка. Nomidol купить в словакии.
Я испытал полезность буквально через пару дней, через неделю начала замечать улучшение, через две, ноготь пришел в норму».
Я поспешил купить его для себя, избавит от неприятного запаха пота и повышенной потливости, убьет все болезнетворные грибки и бактерии. Во-первых, которые дополнительно восстанавливают кожу и ухаживают за ней. Также огромный плюс Номидола, раздуваться, отслаиваться, глядеться начали дюже отвратительно. Это завершилось тем, что моей семье совершенно никто не сможет помочь, да и понадобится искать способы на своё усмотрение.
Я, что большой ноготь на ноге начал слоиться. Казалось бы, предотвращая распространение желтизны, предотвращают отслоение, отбеливают ткань. Уже через 2 недели состояние ног улучшилось: пропал зуд, а также уничтожение различных раздражений на коже. Мазала ноги каждый день, который следует наносить на проблемные участки дважды в день. Номидол позволяет избавиться от микоза путем использования крема, а это дополнительный повод Номидол купить.
Зелень вместе с половиной очищенных и нарезанных луковицы и моркови залить холодной водой 1, чтобы добавить.
Подается блюдо к столу со сметаной и ржаными сухариками, даем настоятся тридцать минут, а косточку перетирать вообще тогда нет смысла. Почему убрать избыток кислоты из квашеной капусты, на мой поддон.
Compound dataset collected from ChEMBL (1.4 million compounds) and STITCH (82.8 million compounds) was checked for the possible anti-cancer activity. It is to be noted that each compound record in STITCH database does not correspond to a unique molecule, i.e. there could be more than one record representing different stereo-isomers for a single compound . In the current study, we have considered each record as a separate compound for prediction of anti-cancer activity, and duplicate compounds were removed from the list of compounds predicted to be active anti-cancer compounds.
This was done to optimize the memory requirement for the task of identifying duplicates in a large pool of compounds. In the current study, we have used two methods for prediction of anti-cancer activity of almost 84 million compounds, (i) CDRUG and (ii) a custom build support vector machine (SVM) classifier.
Benchmark dataset prepared for prediction of anti-cancer activity by Li et al. was used in the current study. This dataset is from the NCI-60 Developmental Therapeutics Program (DTP) project . The details of protocol used to create the benchmark dataset, can be found in primary published article .
The dataset consist of more than 18,000 compounds, divided into active and inactive anticancer compounds. The benchmark dataset can be downloaded from
CDRUG is an analytical method for prediction of anticancer activity of chemical compound . In the current study, we have downloaded and used the latest standalone version of CDRUG for anticancer activity prediction.
This tool takes a list of SMILES string of query compounds as an input and generates ranked list consisting of various scores and p value. In the current study, we have considered the cutoff p value of ≤ 0.05, as criteria to select compounds with anticancer activity. The algorithmic details of CDRUG can be found in primary publication .
In the current study, we have built SVM based model for the prediction of anticancer activity of chemical compound. Support Vector Machines are a useful tool for data classification, which has found its application in wide range of domains including computational biology. We have used software LIBSVM (version 3.18) in our current study for SVM based classification. The SVM based classification task starts with the process of “model building”, in which data is divided into training and testing sets.
Each instance in the training set contains one “target value” or “class label” (in our case it is either 1 or 0; where ‘1’ represents compound has anti-cancer activity and ‘0’, otherwise), and several “attributes” or “features”. The goal of SVM , is to rigorously build a model (based on instances from training data) which predicts the target values / class labels of the instances from test data, given only attributes in the test data. In the current study, we selected ‘C-SVM’ (Multi-class classification) as SVM type, and radial basis function (RBF) as a kernel type for building anti-cancer activity prediction model. RBF kernel was chosen on the basis of its popularity, robustness, and the fact that other kernels available with LIBSVM are special cases of RBF under certain parameter , .
The process of classification with SVM involves following steps:
The rationale behind the selection of dataset common to that, used by CDRUG , was to compare prediction outcomes of two methods (CDRUG and SVM classifier) build from the same underlying dataset. The process of building model involves following sub-steps:
Feature Extraction .
In the current study, the features were derived from the entities in the compound, which are responsible for defining its reaction mechanism, and are the contributing factor towards its activity. These entities can be of organic (i.e. ‘functional groups’) or inorganic (i.e. ‘metal ions’) in nature. Functional groups present in organic molecules had been used in the past to predict drug-target interaction networks , wherein authors had used 28 functional groups to characterize drugs. In addition to the functional group, metals also play a very important role in determining the activity of drugs, especially in the field of cancer drug, such as cisplatin, which can be regarded as a pioneer in the field of metal based anti-cancer drug .
The functional groups and metals present in a compound can be visualized as building block or substructure of a compound. SMARTS is a very powerful language for describing such molecular substructures .
SMARTS strings are typically used for substructure searching, to identify molecules based on pattern matching, either a singular string or as a group of SMARTS strings. In the current study, we rigorously prepared SMARTS strings of over 300 functional groups (including common metallic forms found in various drugs). We have followed the guidelines given by Daylight , while preparing these SMARTS strings.
Features were extracted from the training compounds, from the Benchmark dataset . The dataset consist of over 18,000 compounds (positive- and negative-set) in SMILES format (refer to: ). In the current study, we have used open-source python library Pybel for finding substructures encoded as a SMARTS string in a query compound.
Python script was written to automate the task of matching the list of SMARTS stings against the benchmark dataset (Fig 2).
On reviewing the extracted features of all compounds (positive and negative dataset), we observed that many of the substructures from our initial list of SMARTS string were not present in either of the dataset (i.e. positive- or negative-set), and therefore, they were excluded from the further downstream analysis process. The final list of SMARTS strings along with corresponding representative substructure (functional groups or metal ion) consisted of 228 SMARTS strings, which can be found as online supplementary material–‘ ’ (see S5 Text).
At the end of this exercise, we obtained feature matrix of dimension M Γ N matrix; where ‘M’ corresponds to the number of compounds in benchmark dataset and ‘N’ corresponds to number of features/substructures (i.e. 228) used to prepare feature vector of a compound. This feature vector was transformed into a SVM format as given below:
Where, each line contains an instance and is ended by a '\n' character. The
: gives a feature (attribute) value: is an integer starting from 1 and is a real number (In the current study, can be , where 0→indicates feature is absent in the compound, and 1→indicates feature is present in the compound). Indices must be in ascending order .
Parameter Estimation and Model Building . The RBF kernel has two parameters C and γ; for a given prediction problem, the value of these parameters is not known beforehand, and therefore, some kind of parameter search has to be done to estimate values of these parameters.
The main objective of parameter search is to find good ( C , γ), so that the prediction model will accurately predict activity of unknown compounds. Generally poorly optimized models tend to suffer with an overfitting problem, which refers to the condition when prediction model / classifier shows high accuracy with training data, but its accuracy drops drastically when used to predict unknown test data. Cross-validation is a technique which is applied to overcome the overfitting problem. In n -fold cross-validation, training dataset is divided into n subsets of equal size.
Sequentially one subset is tested using the model, trained on the remaining n -1 subsets. In this way, each instance of the whole training set is predicted once, so that, the cross-validation accuracy is the percentage of data which are correctly classified.
In the current study, we performed an exhaustive grid—search on C and γ using 5-fold cross-validation. After feature extraction and data transformation of the benchmark dataset (see section Feature Extraction), we first did a coarse grid search for finding best C and γ using 5-fold cross-validation. We first started with coarse grid search with an exponentially growing sequence of C and γ ( C = 2−5, 2−4, 2−3…, 214, 215 and γ = 2−15, 2−14….24, 23), which gave us best parameters ( C = 22 and γ = 2−2) with cross-validation accuracy of 80.99% (Fig 3).
The parameters with cross-validation accuracy of over 80.5% are distinctly marked with green color in grid space of Fig 3, we next focused on fine grid search in this region.
The fine grid search was conducted with a growing sequence of C and γ ( C = 2−1, 2−0.75, 2−50…25.50, 25.75, 26 and γ = 20, 2−0.75….2−4.50, 2−4.75, 2−5), which gave us best parameters ( C = 21.5 and γ = 2−1.5) with cross-validation accuracy of 81.18% (Fig 4).
Whole training set (i.e. the transformed benchmark dataset with feature vectors) was used for building a final classifier with the best parameters ( C = 21.5 and γ = 2−1.5). The intermediate files generated during grid search, along with final classifier ‘ cancer . model ’ can be found as online supplementary material ‘ ’ (S6 Text). In the current study, the classifier ‘ cancer . model ’ was used in the subsequent SVM based prediction of anticancer activity. The exhaustive grid based parameter search was done with the help of the python script ‘ ’ available with LIBSVM package .
Computationally grid search is memory and CPU intensive task, in a parallel mode, it took almost 10 days to complete this task in 4 GB Intel® Core™ i5 desktop installed with Linux operating system.
Prediction Process . The prediction of anticancer activity with SVM classifier ‘ cancer . model ’ for query compounds involves following steps:
The feature vector Di for a ith query compound, would be a binary vector representing the presence or absence of functional group/substructure in a query compound.