DrugTax: package for drug taxonomy identification and explainable feature extraction.
Ontology highlight
ABSTRACT:
SUBMITTER: Preto AJ
PROVIDER: S-EPMC9609197 | biostudies-literature | 2022 Oct
REPOSITORIES: biostudies-literature
Publications
DrugTax: package for drug taxonomy identification and explainable feature extraction.
Preto A J AJ Correia Paulo C PC Moreira Irina S IS
Journal of cheminformatics 20221027 1
DrugTax is an easy-to-use Python package for small molecule detailed characterization. It extends a previously explored chemical taxonomy making it ready-to-use in any Artificial Intelligence approach. DrugTax leverages small molecule representations as input in one of their most accessible and simple forms (SMILES) and allows the simultaneously extraction of taxonomy information and key features for big data algorithm deployment. In addition, it delivers a set of tools for bulk analysis and vis ...[more]
Similar Datasets
Project description:Artificial intelligence models do not provide information about exactly how the predictions are reached. This lack of transparency is a major drawback. Particularly in medical applications, interest in explainable artificial intelligence (XAI), which helps to develop methods of visualizing, explaining, and analyzing deep learning models, has increased recently. With explainable artificial intelligence, it is possible to understand whether the solutions offered by deep learning techniques are safe. This paper aims to diagnose a fatal disease such as a brain tumor faster and more accurately using XAI methods. In this study, we preferred datasets that are widely used in the literature, such as the four-class kaggle brain tumor dataset (Dataset I) and the three-class figshare brain tumor dataset (Dataset II). To extract features, a pre-trained deep learning model is chosen. DenseNet201 is used as the feature extractor in this case. The proposed automated brain tumor detection model includes five stages. First, training of brain MR images with DenseNet201, the tumor area was segmented with GradCAM. The features were extracted from DenseNet201 trained using the exemplar method. Extracted features were selected with iterative neighborhood component (INCA) feature selector. Finally, the selected features were classified using support vector machine (SVM) with 10-fold cross-validation. An accuracy of 98.65% and 99.97%, were obtained for Datasets I and II, respectively. The proposed model obtained higher performance than the state-of-the-art methods and can be used to aid radiologists in their diagnosis.
| S-EPMC10000758 | biostudies-literature
Project description:One of the main challenges in applying machine learning algorithms to biological sequence data is how to numerically represent a sequence in a numeric input vector. Feature extraction techniques capable of extracting numerical information from biological sequences have been reported in the literature. However, many of these techniques are not available in existing packages, such as mathematical descriptors. This paper presents a new package, MathFeature, which implements mathematical descriptors able to extract relevant numerical information from biological sequences, i.e. DNA, RNA and proteins (prediction of structural features along the primary sequence of amino acids). MathFeature makes available 20 numerical feature extraction descriptors based on approaches found in the literature, e.g. multiple numeric mappings, genomic signal processing, chaos game theory, entropy and complex networks. MathFeature also allows the extraction of alternative features, complementing the existing packages. To ensure that our descriptors are robust and to assess their relevance, experimental results are presented in nine case studies. According to these results, the features extracted by MathFeature showed high performance (0.6350-0.9897, accuracy), both applying only mathematical descriptors, but also hybridization with well-known descriptors in the literature. Finally, through MathFeature, we overcame several studies in eight benchmark datasets, exemplifying the robustness and viability of the proposed package. MathFeature has advanced in the area by bringing descriptors not available in other packages, as well as allowing non-experts to use feature extraction techniques.
| S-EPMC8769707 | biostudies-literature
Project description:Traffic identification of the target category is currently a significant challenge for network monitoring and management. To identify the target category with pertinence, a feature extraction algorithm based on the subset with highest proportion is presented in this paper. The method is proposed to be applied to the identification of any category that is assigned as the target one, but not restricted to certain specific category. We divide the process of feature extraction into two stages. In the stage of primary feature extraction, the feature subset is extracted from the dataset which has the highest proportion of the target category. In the stage of secondary feature extraction, the features that can distinguish the target and interfering categories are added to the feature subset. Our theoretical analysis and experimental observations reveal that the proposed algorithm is able to extract fewer features with greater identification ability of the target category. Moreover, the universality of the proposed algorithm proves to be available with the experiment that every category is set to be the target one.
| S-EPMC5104389 | biostudies-literature
Project description:Brain tumors are one of the leading diseases imposing a huge morbidity rate across the world every year. Classifying brain tumors accurately plays a crucial role in clinical diagnosis and improves the overall healthcare process. ML techniques have shown promise in accurately classifying brain tumors based on medical imaging data such as MRI scans. These techniques aid in detecting and planning treatment early, improving patient outcomes. However, medical image datasets are frequently affected by a significant class imbalance, especially when benign tumors outnumber malignant tumors in number. This study presents an explainable ensemble-based pipeline for brain tumor classification that integrates a Dual-GAN mechanism with feature extraction techniques, specifically designed for highly imbalanced data. This Dual-GAN mechanism facilitates the generation of synthetic minority class samples, addressing the class imbalance issue without compromising the original quality of the data. Additionally, the integration of different feature extraction methods facilitates capturing precise and informative features. This study proposes a novel deep ensemble feature extraction (DeepEFE) framework that surpasses other benchmark ML and deep learning models with an accuracy of 98.15%. This study focuses on achieving high classification accuracy while prioritizing stable performance. By incorporating Grad-CAM, it enhances the transparency and interpretability of the overall classification process. This research identifies the most relevant and contributing parts of the input images toward accurate outcomes enhancing the reliability of the proposed pipeline. The significantly improved Precision, Sensitivity and F1-Score demonstrate the effectiveness of the proposed mechanism in handling class imbalance and improving the overall accuracy. Furthermore, the integration of explainability enhances the transparency of the classification process to establish a reliable model for brain tumor classification, encouraging their adoption in clinical practice promoting trust in decision-making processes.
| S-EPMC11432876 | biostudies-literature
Project description:BACKGROUND:Although in silico drug discovery is necessary for drug development, two major strategies, a structure-based and ligand-based approach, have not been completely successful. Currently, the third approach, inference of drug candidates from gene expression profiles obtained from the cells treated with the compounds under study requires the use of a training dataset. Here, the purpose was to develop a new approach that does not require any pre-existing knowledge about the drug-protein interactions, but these interactions can be inferred by means of an integrated approach using gene expression profiles obtained from the cells treated with the analysed compounds and the existing data describing gene-gene interactions. RESULTS:In the present study, using tensor decomposition-based unsupervised feature extraction, which represents an extension of the recently proposed principal-component analysis-based feature extraction, gene sets and compounds with a significant dose-dependent activity were screened without any training datasets. Next, after these results were combined with the data showing perturbations in single-gene expression profiles, genes targeted by the analysed compounds were inferred. The set of target genes thus identified was shown to significantly overlap with known target genes of the compounds under study. CONCLUSIONS:The method is specifically designed for large-scale datasets (including hundreds of treatments with compounds), not for conventional small-scale datasets. The obtained results indicate that two compounds that have not been extensively studied, WZ-3105 and CGP-60474, represent promising drug candidates targeting multiple cancers, including melanoma, adenocarcinoma, liver carcinoma, and breast, colon, and prostate cancers, which were analysed in this in silico study.
| S-EPMC7394334 | biostudies-literature
Project description:Protein methylation is one type of reversible post-translational modifications (PTMs), which plays vital roles in many cellular processes such as transcription activity, DNA repair. Experimental identification of methylation sites on proteins without prior knowledge is costly and time-consuming. In silico prediction of methylation sites might not only provide researches with information on the candidate sites for further determination, but also facilitate to perform downstream characterizations and site-specific investigations. In the present study, a novel approach based on Bi-profile Bayes feature extraction combined with support vector machines (SVMs) was employed to develop the model for Prediction of Protein Methylation Sites (BPB-PPMS) from primary sequence. Methylation can occur at many residues including arginine, lysine, histidine, glutamine, and proline. For the present, BPB-PPMS is only designed to predict the methylation status for lysine and arginine residues on polypeptides due to the absence of enough experimentally verified data to build and train prediction models for other residues. The performance of BPB-PPMS is measured with a sensitivity of 74.71%, a specificity of 94.32% and an accuracy of 87.98% for arginine as well as a sensitivity of 70.05%, a specificity of 77.08% and an accuracy of 75.51% for lysine in 5-fold cross validation experiments. Results obtained from cross-validation experiments and test on independent data sets suggest that BPB-PPMS presented here might facilitate the identification and annotation of protein methylation. Besides, BPB-PPMS can be extended to build predictors for other types of PTM sites with ease. For public access, BPB-PPMS is available at http://www.bioinfo.bio.cuhk.edu.hk/bpbppms.
| S-EPMC2654709 | biostudies-literature
Project description:MotivationIt becomes widely accepted that human cancer is a disease involving dynamic changes in the genome and that the missense mutations constitute the bulk of human genetic variations. A multitude of computational algorithms, especially the machine learning-based ones, has consequently been proposed to distinguish missense changes that contribute to the cancer progression ('driver' mutation) from those that do not ('passenger' mutation). However, the existing methods have multifaceted shortcomings, in the sense that they either adopt incomplete feature space or depend on protein structural databases which are usually far from integrated.ResultsIn this article, we investigated multiple aspects of a missense mutation and identified a novel feature space that well distinguishes cancer-associated driver mutations from passenger ones. An index (DX score) was proposed to evaluate the discriminating capability of each feature, and a subset of these features which ranks top was selected to build the SVM classifier. Cross-validation showed that the classifier trained on our selected features significantly outperforms the existing ones both in precision and robustness. We applied our method to several datasets of missense mutations culled from published database and literature and obtained more reasonable results than previous studies.AvailabilityThe software is available online at http://www.methodisthealth.com/software and https://sites.google.com/site/drivermutationidentification/.Contactxzhou@tmhs.org.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC3496432 | biostudies-literature
Project description:Accurate drug-target binding affinity (DTA) prediction is crucial in drug discovery. Recently, deep learning methods for DTA prediction have made significant progress. However, there are still two challenges: (1) recent models always ignore the correlations in drug and target data in the drug/target representation process and (2) the interaction learning of drug-target pairs always is by simple concatenation, which is insufficient to explore their fusion. To overcome these challenges, we propose an end-to-end sequence-based model called BTDHDTA. In the feature extraction process, the bidirectional gated recurrent unit (GRU), transformer encoder, and dilated convolution are employed to extract global, local, and their correlation patterns of drug and target input. Additionally, a module combining convolutional neural networks with a Highway connection is introduced to fuse drug and protein deep features. We evaluate the performance of BTDHDTA on three benchmark data sets (Davis, KIBA, and Metz), demonstrating its superiority over several current state-of-the-art methods in key metrics such as Mean Squared Error (MSE), Concordance Index (CI), and Regression toward the mean (R m 2). The results indicate that our method achieves a better performance in DTA prediction. In the case study, we use the BTDHDTA model to predict the binding affinities between 3137 FDA-approved drugs and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication-related proteins, validating the model's effectiveness in practical scenarios.
| S-EPMC11755178 | biostudies-literature
Project description:BACKGROUND:What is a healthy microbiome? The pursuit of this and many related questions, especially in light of the recently recognized microbial component in a wide range of diseases has sparked a surge in metagenomic studies. They are often not simply attributable to a single pathogen but rather are the result of complex ecological processes. Relatedly, the increasing DNA sequencing depth and number of samples in metagenomic case-control studies enabled the applicability of powerful statistical methods, e.g. Machine Learning approaches. For the latter, the feature space is typically shaped by the relative abundances of operational taxonomic units, as determined by cost-effective phylogenetic marker gene profiles. While a substantial body of microbiome/microbiota research involves unsupervised and supervised Machine Learning, very little attention has been put on feature selection and engineering. RESULTS:We here propose the first algorithm to exploit phylogenetic hierarchy (i.e. an all-encompassing taxonomy) in feature engineering for microbiota classification. The rationale is to exploit the often mono- or oligophyletic distribution of relevant (but hidden) traits by virtue of taxonomic abstraction. The algorithm is embedded in a comprehensive microbiota classification pipeline, which we applied to a diverse range of datasets, distinguishing healthy from diseased microbiota samples. CONCLUSION:We demonstrate substantial improvements over the state-of-the-art microbiota classification tools in terms of classification accuracy, regardless of the actual Machine Learning technique while using drastically reduced feature spaces. Moreover, generalized features bear great explanatory value: they provide a concise description of conditions and thus help to provide pathophysiological insights. Indeed, the automatically and reproducibly derived features are consistent with previously published domain expert analyses.
| S-EPMC6003080 | biostudies-literature
Project description:As IoT devices' adoption grows rapidly, security plays an important role in our daily lives. As part of the effort to counter these security threats in recent years, many IoT intrusion detection datasets were presented, such as TON_IoT, BoT-IoT, and Aposemat IoT-23. These datasets were used to build many machine learning-based IoT intrusion detection models. In this research, we present an explainable and efficient method for selecting the most effective universal features from IoT intrusion detection datasets that can help in producing highly-accurate and efficient machine learning-based intrusion detection systems. The proposed method was applied to TON_IoT, Aposemat IoT-23, and IoT-ID datasets and resulted in the selection of six universal network-flow features. The proposed method was tested and produced a high accuracy of 99.62% with a prediction time reduced by up to 70%. To provide better insight into the operation of the classifier, a Shapley additive explanation was used to explain the selected features and to prove the alignment of the explanation with current attack techniques.
| S-EPMC9371123 | biostudies-literature