Demystifying Multitask Deep Neural Networks for Quantitative Structure–Activity Relationships

Deep neural networks (DNNs) are complex computational models that have found great success in many artificial intelligence applications, such as computer vision, and natural language processing., In the past four years, DNNs have also generated promising results for quantitative structure–activity relationship (QSAR) tasks., Previous work showed that DNNs can routinely make better predictions than traditional methods, such as random forests, on a diverse collection of QSAR data sets. It was also found that multitask DNN modelsthose trained on and predicting multiple QSAR properties simultaneouslyoutperform DNNs trained separately on the individual data sets in many, but not all, tasks. To date there has been no satisfactory explanation of why the QSAR of one task embedded in a multitask DNN can borrow information from other unrelated QSAR tasks. Thus, using multitask DNNs in a way that consistently provides a predictive advantage becomes a challenge. In this work, we explored why multitask DNNs make a difference in predictive performance. Our results show that during prediction a multitask DNN does borrow “signal” from molecules with similar structures in the training sets of the other tasks. However, whether this borrowing leads to better or worse predictive performance depends on whether the activities are correlated. On the basis of this, we have developed a strategy to use multitask DNNs that incorporate prior domain knowledge to select training sets with correlated activities, and we demonstrate its effectiveness on several examples

Demystifying Multitask Deep Neural Networks for Quantitative Structure–Activity Relationships

Deep neural networks (DNNs) are complex computational models that have found great success in many artificial intelligence applications, such as computer vision, and natural language processing., In the past four years, DNNs have also generated promising results for quantitative structure–activity relationship (QSAR) tasks., Previous work showed that DNNs can routinely make better predictions than traditional methods, such as random forests, on a diverse collection of QSAR data sets. It was also found that multitask DNN modelsthose trained on and predicting multiple QSAR properties simultaneouslyoutperform DNNs trained separately on the individual data sets in many, but not all, tasks. To date there has been no satisfactory explanation of why the QSAR of one task embedded in a multitask DNN can borrow information from other unrelated QSAR tasks. Thus, using multitask DNNs in a way that consistently provides a predictive advantage becomes a challenge. In this work, we explored why multitask DNNs make a difference in predictive performance. Our results show that during prediction a multitask DNN does borrow “signal” from molecules with similar structures in the training sets of the other tasks. However, whether this borrowing leads to better or worse predictive performance depends on whether the activities are correlated. On the basis of this, we have developed a strategy to use multitask DNNs that incorporate prior domain knowledge to select training sets with correlated activities, and we demonstrate its effectiveness on several examples

Additional file 1 of The association between hepatitis B virus and semen quality: a systematic review and meta-analysis

Supplementary Material

Color-Stable Deep-Blue Light-Emitting Diodes Based on Perovskite Nanosheet Films Passivated with Zwitterionic Choline Chloride and Rubidium Bromide

Incorporating a chlorine ion into a bromine-based perovskite is a straightforward strategy to achieve blue perovskite light-emitting diodes (PeLEDs). However, efficient deep-blue PeLEDs suffer from ineffective chlorine ion supplementation due to the low solubility of chloride and high deep-level defects in perovskite. Here, we propose multifunctional zwitterion choline chloride (ChCl) and rubidium bromide (RbBr) molecules, which not only provide free chlorine ion to fill the halide vacancies but also serve as passivating agents to eliminate Pb0 defects in perovskite nanosheets. Consequently, deep-blue PeLEDs were achieved with peak external quantum efficiencies of 3.58%, 2.41%, and 1.10%, which coordinate at (0.134, 0.063), (0.139, 0.052), and (0.141, 0.041), respectively

DataSheet_1_Integrated transcriptomic and metabolomic analysis reveals the metabolic programming of GM-CSF- and M-CSF- differentiated mouse macrophages.docx

Macrophages play a critical role in the inflammatory response and tumor development. Macrophages are primarily divided into pro-inflammatory M1-like and anti-inflammatory M2-like macrophages based on their activation status and functions. In vitro macrophage models could be derived from mouse bone marrow cells stimulated with two types of differentiation factors: GM-CSF (GM-BMDMs) and M-CSF (M-BMDMs), to represent M1- and M2-like macrophages, respectively. Since macrophage differentiation requires coordinated metabolic reprogramming and transcriptional rewiring in order to fulfill their distinct roles, we combined both transcriptome and metabolome analysis, coupled with experimental validation, to gain insight into the metabolic status of GM- and M-BMDMs. The data revealed higher levels of the tricarboxylic acid cycle (TCA cycle), oxidative phosphorylation (OXPHOS), fatty acid oxidation (FAO), and urea and ornithine production from arginine in GM-BMDMs, and a preference for glycolysis, fatty acid storage, bile acid metabolism, and citrulline and nitric oxide (NO) production from arginine in M-BMDMs. Correlation analysis with the proteomic data showed high consistency in the mRNA and protein levels of metabolic genes. Similar results were also obtained when compared to RNA-seq data of human monocyte derived macrophages from the GEO database. Furthermore, canonical macrophage functions such as inflammatory response and phagocytosis were tightly associated with the representative metabolic pathways. In the current study, we identified the core metabolites, metabolic genes, and functional terms of the two distinct mouse macrophage populations. We also distinguished the metabolic influences of the differentiation factors GM-CSF and M-CSF, and wish to provide valuable information for in vitro macrophage studies.</p

Ordonnance du roi, concernant le régiment des dragons chasseurs de Conflans . Du 27 octobre 1761

[Acte royal. 1761-10-27. Versailles]Avec mode text