scholarly journals decoupleR: Ensemble of computational methods to infer biological activities from omics data

2021 ◽  
Author(s):  
Pau Badia-i-Mompel ◽  
Jesús Vélez ◽  
Jana Braunger ◽  
Celina Geiss ◽  
Daniel Dimitrov ◽  
...  
Keyword(s):  
Computational Methods ◽  
Statistical Power ◽  
Linear Models ◽  
Biological Activities ◽  
Omics Data ◽  
Bioconductor Package ◽  
Unified Framework ◽  
Knowledge Resources ◽  
Consensus Score ◽  
Better Than

Summary: Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor package containing computational methods to extract these activities within a unified framework. decoupleR allows us to flexibly run any method with a given resource, including methods that leverage mode of regulation and weights of interactions. Using decoupleR, we evaluated the performance of methods on transcriptomic and phospho-proteomic perturbation experiments. Our findings suggest that simple linear models and the consensus score across methods perform better than other methods at predicting perturbed regulators. Availability and Implementation: decoupleR is open source available in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/decoupleR.html). The code to reproduce the results is in Github (https://github.com/saezlab/decoupleR_manuscript) and the data in Zenodo (https://zenodo.org/record/5645208).

scholarly journals Diffusion in CP Stars: The Quest for Accuracy

1998 ◽  
Vol 11 (2) ◽  
pp. 671-673
Author(s):  
G. Alecian
Keyword(s):  
Computational Methods ◽  
Stellar Evolution ◽  
Diffusion Processes ◽  
Stellar Atmospheres ◽  
Time Dependent ◽  
Data Bases ◽  
Self Consistent ◽  
Stellar Envelopes ◽  
Better Than

We present a brief review about recent progresses concerning the study of diffusion processes in CP stars. The most spectacular of them concerns the calculation of radiative accelerations in stellar envelopes for which an accuracy better than 30% can now be reached for a large number of ions. This improvement is mainly due to huge and accurate atomic and opacity data bases available since the beginning of the 90’s. Developments of efficient computational methods have been carried out to take advantage of these new data. These progresses have, in turn, led to a better understanding of how the element stratification is building up with time. A computation of self-consistent stellar evolution models, including time-dependent diffusion, can now be within the scope of the next few years. However, the progresses previously mentioned do not apply for stellar atmospheres and upper layers of envelopes.

Synthesis and Biological Activities of Hydroxytyrosol Ester Derivatives

2020 ◽  
Vol 42 (1) ◽  
pp. 109-109
Author(s):  
Hao Zang Hao Zang ◽  
Qian Xu Qian Xu ◽  
Luyun Zhang Luyun Zhang ◽  
Guangqing Xia Guangqing Xia ◽  
Jiaming Sun and Junyi Zhu Jiaming Sun and Junyi Zhu
Keyword(s):  
Ascorbic Acid ◽  
Antioxidant Activities ◽  
Biological Activities ◽  
Radical Scavenging Activity ◽  
Radical Scavenging ◽  
Acetylcholinesterase Inhibition ◽  
Hydroxyl Group ◽  
Scavenging Activity ◽  
Antioxidant Power ◽  
Better Than

A series of hydroxytyrosol (HT) derivatives were synthesized by modification of alcohol hydroxyl group of HT, twenty-five target compounds were obtained and characterized by NMR and HRMS. The antioxidant activities of those compounds were evaluated in three different assays. Except 3e and 3y, all other compounds demonstrated significant 2,2and#39;-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) free radical cation scavenging activity ranging from IC50 3.4 to 24.4 μM, which were more potent than L-ascorbic acid (IC50=24.8 μM). Compounds 3b-3d, 3f-3k, 3m-3x were better than Trolox (18.3 M). Moreover, the ferric reducing antioxidant power (FRAP) of all compounds were discovered to be more potent than L-ascorbic acid (40.7 mmol/g), except 3e, all other compounds (141.5-202.1 mmol/g) were better than Trolox (94.7 mmol/g). Compounds 3a-3d, 3f-3j, 3l-3m, 3o, 3q, 3t, 3v-3y exhibited more potent hydroxyl radical scavenging activity (IC50=245.1-475.1 M) than L-ascorbic acid (554.4 M) and Trolox (500.4 M). Compounds 3q, 3t and 3y exhibited more potent -Glucosidase inhibition activity (39.1-52.4 M) than Acarbose (60.9 M). Compounds 3a, 3d, 3f-3m, 3s-3t, 3v-3y showed some acetylcholinesterase inhibition activities, compounds 3a, 3d, 3f-3j, 3l-3m, 3o-3p, 3s-3t, 3w showed some butyrylcholinesterase inhibition activities.

scholarly journals Antiaging, antioxidant flavonoids; synthesis, antimicrobial screening as well as 3D QSAR CoMFA models for the prediction of biological activity

2014 ◽  
Vol 39 (2) ◽  
pp. 191-199 ◽  
Author(s):  
Bilkis Jahan Lumbiny ◽  
Zhang Hui ◽  
M Azizul Islam
Keyword(s):  
Predictive Model ◽  
Biological Activities ◽  
3D Qsar ◽  
Biologically Active ◽  
Field Analysis ◽  
Contour Maps ◽  
Chemical Structures ◽  
Hydroxy Flavone ◽  
Bonding Characteristics ◽  
Better Than

Flavonoids, polyphenolic heteronuclear compounds which are naturally occurring antioxidants are widely used as antiaging substances. Synthesis of new naturally occuring organic compounds with basic skeleton of chalcones, flavones and oxygenated flavones and their antimicrobial activity were reported by this research group for long. Presently comparative molecular field analysis (CoMFA) implemented in Sybyl 7.3 was conducted on a series of substituted flavones. CoMFA is an effective computer implemented 3D QSAR technique deriving a correlation between set of the biologically active molecules and their 3D shape, electrostatic and hydrogen bonding characteristics employing both interactive graphics and statistical techniques. Evaluation of 38 compounds were served to establish the models with grid spacing (2.0 Å). CoMFA produced best predictive model for compound 1C (2 ? Phenyl ? 1,4 ? benzopyrone) and compound 2C (5 ? Fluoro ? 3?? hydroxy flavone ) among all. Model for compound 2C [r2 conv (no-validation) = 0.956, SEE = 0.211, F value = 111.054) is better than that of compound 1C [r2 conv (no-validation) = 0.955, SEE = 0.212, F value = 110.261) but comparing superimposed model 1C being suggested as the best predictive model. 3D contour maps were generated to correlate the biological activities with the chemical structures of the examined compounds and for further design. DOI: http://dx.doi.org/10.3329/jasbs.v39i2.17856 J. Asiat. Soc. Bangladesh, Sci. 39(2): 191-199, December 2013

scholarly journals Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4769 ◽  
Author(s):  
Jônatas Belotti ◽  
Hugo Siqueira ◽  
Lilian Araujo ◽  
Sérgio L. Stevan ◽  
Paulo S.G. de Mattos Neto ◽  
...  
Keyword(s):  
Linear Models ◽  
Moving Average ◽  
Elman Network ◽  
Average Model ◽  
Learning Machines ◽  
12 Steps ◽  
Moving Average Model ◽  
Hydroelectric Plants ◽  
Seasonal Streamflow ◽  
Better Than

Estimating future streamflows is a key step in producing electricity for countries with hydroelectric plants. Accurate predictions are particularly important due to environmental and economic impact they lead. In order to analyze the forecasting capability of models regarding monthly seasonal streamflow series, we realized an extensive investigation considering: six versions of unorganized machines—extreme learning machines (ELM) with and without regularization coefficient (RC), and echo state network (ESN) using the reservoirs from Jaeger’s and Ozturk et al., with and without RC. Additionally, we addressed the ELM as the combiner of a neural-based ensemble, an investigation not yet accomplished in such context. A comparative analysis was performed utilizing two linear approaches (autoregressive model (AR) and autoregressive and moving average model (ARMA)), four artificial neural networks (multilayer perceptron, radial basis function, Elman network, and Jordan network), and four ensembles. The tests were conducted at five hydroelectric plants, using horizons of 1, 3, 6, and 12 steps ahead. The results indicated that the unorganized machines and the ELM ensembles performed better than the linear models in all simulations. Moreover, the errors showed that the unorganized machines and the ELM-based ensembles reached the best general performances.

scholarly journals Inception in visual cortex: in vivo-silico loops reveal most exciting images

2018 ◽  
Author(s):  
Edgar Y. Walker ◽  
Fabian H. Sinz ◽  
Emmanouil Froudarakis ◽  
Paul G. Fahey ◽  
Taliah Muhammad ◽  
...  
Keyword(s):  
Linear Models ◽  
Sensory Information ◽  
Target Cells ◽  
New Approach ◽  
Spatial Features ◽  
Sensory Information Processing ◽  
Response Modeling ◽  
The Brain ◽  
Better Than

Much of our knowledge about sensory processing in the brain is based on quasi-linear models and the stimuli that optimally drive them. However, sensory information processing is nonlinear, even in primary sensory areas, and optimizing sensory input is difficult due to the high-dimensional input space. We developed inception loops, a closed-loop experimental paradigm that combines in vivo recordings with in silico nonlinear response modeling to identify the Most Exciting Images (MEIs) for neurons in mouse V1. When presented back to the brain, MEIs indeed drove their target cells significantly better than the best stimuli identified by linear models. The MEIs exhibited complex spatial features that deviated from the textbook ideal of V1 as a bank of Gabor filters. Inception loops represent a widely applicable new approach to dissect the neural mechanisms of sensation.

scholarly journals proDA: Probabilistic Dropout Analysis for Identifying Differentially Abundant Proteins in Label-Free Mass Spectrometry

2020 ◽  
Author(s):  
Constantin Ahlmann-Eltze ◽  
Simon Anders
Keyword(s):  
Mass Spectrometry ◽  
Quantitative Proteomics ◽  
Statistical Power ◽  
Missing Values ◽  
Ad Hoc ◽  
Linear Models ◽  
Statistical Tests ◽  
High Sensitivity ◽  
Small Sample ◽  
Label Free

Abstract Protein mass spectrometry with label-free quantification (LFQ) is widely used for quantitative proteomics studies. Nevertheless, well-principled statistical inference procedures are still lacking, and most practitioners adopt methods from transcriptomics. These, however, cannot properly treat the principal complication of label-free proteomics, namely many non-randomly missing values. We present proDA, a method to perform statistical tests for differential abundance of proteins. It models missing values in an intensity-dependent probabilistic manner. proDA is based on linear models and thus suitable for complex experimental designs, and boosts statistical power for small sample sizes by using variance moderation. We show that the currently widely used methods based on ad hoc imputation schemes can report excessive false positives, and that proDA not only overcomes this serious issue but also offers high sensitivity. Thus, proDA fills a crucial gap in the toolbox of quantitative proteomics.

scholarly journals Differential expression of single-cell RNA-seq data using Tweedie models

2021 ◽  
Author(s):  
Himel Mallick ◽  
Suvo Chatterjee ◽  
Shrabanti Chowdhury ◽  
Saptarshi Chatterjee ◽  
Ali Rahnavard ◽  
...  
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Statistical Power ◽  
Heavy Tails ◽  
Linear Models ◽  
Dynamic Range ◽  
Expression Profiles ◽  
R Package ◽  
Tweedie Distribution ◽  
Probability Mass

SummaryThe performance of computational methods and software to identify differentially expressed genes in single-cell RNA-sequencing (scRNA-seq) has been shown to be influenced by several factors, including the choice of the normalization method used and the choice of the experimental platform (or library preparation protocol) to profile gene expression in individual cells. Currently, it is up to the practitioner to choose the most appropriate differential expression (DE) method out of over 100 DE tools available to date, each relying on their own assumptions to model scRNA-seq data. Here, we propose to use generalized linear models with the Tweedie distribution that can flexibly capture a large dynamic range of observed scRNA-seq data across experimental platforms induced by heavy tails, sparsity, or different count distributions to model the technological variability in scRNA-seq expression profiles. We also propose a zero-inflated Tweedie model that allows zero probability mass to exceed a traditional Tweedie distribution to model zero-inflated scRNA-seq data with excessive zero counts. Using both synthetic and published plate- and droplet-based scRNA-seq datasets, we performed a systematic benchmark evaluation of more than 10 representative DE methods and demonstrate that our method (Tweedieverse) outperforms the state-of-the-art DE approaches across experimental platforms in terms of statistical power and false discovery rate control. Our open-source software (R package) is available at https://github.com/himelmallick/Tweedieverse.

scholarly journals Metal Complexes Derived of Diosmin with Biological Activities in vitro

2019 ◽  
Vol 9 (1) ◽  
pp. 10
Author(s):  
Maéli M. F. Civa ◽  
Dirceu G. de Souza ◽  
Renata G. Silva ◽  
Dayany da S. A. Maciel ◽  
Ricardo L. Tranquilin ◽  
...  
Keyword(s):  
Metal Complexes ◽  
Biological Activities ◽  
Coordination Complexes ◽  
Human Leukemia ◽  
Human Leukemia Cells ◽  
Ft Ir ◽  
Xrd Techniques ◽  
Better Than

The coordination of metal ions with flavonoids is applied to improve its pharmacological properties. To evaluate the role of ions on diosmin new complexes with Fe(II), Cu(II) and Co(II) ions were synthetized and characterized by UV, FT-IR and XRD techniques and surface morphology by SEM. The biological activity of coordination complexes in vitro, the antioxidant (ABTS), antibacterial (disc diffusion and MIC) and antitumoral activities (MTT) were analyzed. Diosmin when reacting with Fe(II) at 50ºC loses the sugar molecule becoming diosmetin (D) coordinated at 1D:1Fe ratio. In presence of Cu(II) and Co(II) at the same conditions besides losing the sugar, diosmin loses the methyl group at C4’ and H at C3’, producing a new ligand and complexes at 1D:2Cu or Co ratio, to produce DCu and DCo, respectively. The coordination of Cu and Fe improve the antioxidant activity of diosmin. DCo was the only presented antibacterial activity. Additionally, a specific antitumor effect of diosmin and metal complexes upon human leukemia cells was demonstrated, suggesting an immune regulatory action. The anti-melanoma activity of DCo is 10 times better than diosmin. Metal coordination could be used to improve drug activity and to give direction to a new possibility of clinical use for diosmin.

scholarly journals Measuring change in biological communities: multivariate analysis approaches for temporal datasets with low sample size

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11096
Author(s):  
Hannah L. Buckley ◽  
Nicola J. Day ◽  
Bradley S. Case ◽  
Gavin Lear
Keyword(s):  
Time Series ◽  
Sample Size ◽  
Statistical Power ◽  
Structural Changes ◽  
Linear Models ◽  
Environmental Changes ◽  
Short Time Series ◽  
Biological Communities ◽  
Extinction Events ◽  
Short Time

Effective and robust ways to describe, quantify, analyse, and test for change in the structure of biological communities over time are essential if ecological research is to contribute substantively towards understanding and managing responses to ongoing environmental changes. Structural changes reflect population dynamics, changes in biomass and relative abundances of taxa, and colonisation and extinction events observed in samples collected through time. Most previous studies of temporal changes in the multivariate datasets that characterise biological communities are based on short time series that are not amenable to data-hungry methods such as multivariate generalised linear models. Here, we present a roadmap for the analysis of temporal change in short-time-series, multivariate, ecological datasets. We discuss appropriate methods and important considerations for using them such as sample size, assumptions, and statistical power. We illustrate these methods with four case-studies analysed using the R data analysis environment.

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