Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study
Not provided.
Biochemistry & Molecular Biology↗
Engineering topics
Gong, Ping
Publications and source records attributed to Gong, Ping.
Sandwich Hybridization Assay for In Situ Real-Time Cyanobacterial Detection and Monitoring: A Review
As cyanobacterial harmful algal bloom (cHAB) events increase in scale, severity, frequency, and duration around the world, rapid and accurate monitoring and characterization tools have become critically essential for regulatory and management decision-making. The composition of cHAB-forming cyanobacteria community can change significantly over time and space and be altered by sample preservation and transportation, making in situ monitoring necessary to obtain real-time and localized information. Sandwich hybridization assay (SHA) utilizes capture oligonucleotide probes for sensitive detection of target-specific nucleic acid sequences. As an amplification-free molecular biology technology, SHA can be adapted for in-situ, real-time or near real-time detection and qualitatively or semi-quantitatively monitoring of cHAB-forming cyanobacteria, owing to its characteristics such as being rapid, portable, inexpensive, and amenable to automation, high sensitivity, specificity and robustness, and multiplexing (i.e., detecting multiple targets simultaneously). Despite its successful application in the monitoring of marine and freshwater phytoplankton, there is still room for improvement. The ability to identify a cHAB community rapidly would decrease delays in cyanotoxin analyses, reduce costs, and increase sample throughput, allowing for timely actions to improve environmental and human health and the understanding of short- and long-term bloom dynamics. Real-time detection and quantitation of HAB-forming cyanobacteria is essential for improving environmental and public health and reducing associated costs. We review and propose to apply SHA for in situ cHABs monitoring.
59 BASIC BIOLOGICAL SCIENCES↗
A Right Transfer Access Control Model of Internet of Things Based on Smart Contract
Sensor nodes play a crucial role in the promotion of development of Internet of Things (IoT). Through this transaction, RO defines access control policies in script form based on ABAC's access control model to grant access right. The identity of all users in the model is identified by address. This paper builds a more flexible right transfer access control model by means of combining the Attribute-Based Access Control model (ABAC) and blockchain technology. Owing to the characteristics of ABAC’s attributes and right association, the massive problems of some sensor nodes can be solved. At the same time, for the sake of addressing the dynamic problems such as node access and right transfer, right transfer contract (TS) and access control contract (CS) are employed on the chain to ensure efficient and safe transmission of rights. To solve on-chain storage problems and ensure transparency of the operation, the idea of Rollup in Ethereum expansion is used to upload the final state of protocol policy and right exchange to the chain. Any user can know the policy and current right transfer status at any time. Finally, comparative and security analysis show that the model presented here can solve IoT devices’ massive and dynamic problems more effectively and it is more secure than the traditional models.
Wang, Jiuru↗
Structure–activity relationship-based chemical classification of highly imbalanced Tox21 datasets
Abstract The specificity of toxicant-target biomolecule interactions lends to the very imbalanced nature of many toxicity datasets, causing poor performance in Structure–Activity Relationship (SAR)-based chemical classification. Undersampling and oversampling are representative techniques for handling such an imbalance challenge. However, removing inactive chemical compound instances from the majority class using an undersampling technique can result in information loss, whereas increasing active toxicant instances in the minority class by interpolation tends to introduce artificial minority instances that often cross into the majority class space, giving rise to class overlapping and a higher false prediction rate. In this study, in order to improve the prediction accuracy of imbalanced learning, we employed SMOTEENN, a combination of Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbor (ENN) algorithms, to oversample the minority class by creating synthetic samples, followed by cleaning the mislabeled instances. We chose the highly imbalanced Tox21 dataset, which consisted of 12 in vitro bioassays for > 10,000 chemicals that were distributed unevenly between binary classes. With Random Forest (RF) as the base classifier and bagging as the ensemble strategy, we applied four hybrid learning methods, i.e., RF without imbalance handling (RF), RF with Random Undersampling (RUS), RF with SMOTE (SMO), and RF with SMOTEENN (SMN). The performance of the four learning methods was compared using nine evaluation metrics, among which F 1 score, Matthews correlation coefficient and Brier score provided a more consistent assessment of the overall performance across the 12 datasets. The Friedman’s aligned ranks test and the subsequent Bergmann-Hommel post hoc test showed that SMN significantly outperformed the other three methods. We also found that a strong negative correlation existed between the prediction accuracy and the imbalance ratio (IR), which is defined as the number of inactive compounds divided by the number of active compounds. SMN became less effective when IR exceeded a certain threshold (e.g., > 28). The ability to separate the few active compounds from the vast amounts of inactive ones is of great importance in computational toxicology. This work demonstrates that the performance of SAR-based, imbalanced chemical toxicity classification can be significantly improved through the use of data rebalancing.
Idakwo, Gabriel↗