Effect of 3D parameters on Antifungal Activities of Some Heterocyclic CompoundsIOSR Journals
Quantitative Structure Activity Relationships (QSAR) of some heterocyclic compounds was studied using some 3D parameters. The QSAR models indicated that Dipole Y, Dipole mag., Y length and some indicator parameters are very effective in describing the antifungal activities of these compounds against Candida albicans in the training and external test set. The multiple regression analysis have produced well predictive statistically significant and cross validated QSAR models which help to explore some expectedly potent compounds.
Effect of 3D parameters on Antifungal Activities of Some Heterocyclic CompoundsIOSR Journals
Quantitative Structure Activity Relationships (QSAR) of some heterocyclic compounds was studied using some 3D parameters. The QSAR models indicated that Dipole Y, Dipole mag., Y length and some indicator parameters are very effective in describing the antifungal activities of these compounds against Candida albicans in the training and external test set. The multiple regression analysis have produced well predictive statistically significant and cross validated QSAR models which help to explore some expectedly potent compounds.
Let's get ready to rumble redux: Crossover versus mutation head to head on ex...kknsastry
This paper analyzes the relative advantages between crossover and mutation on a class of deterministic and stochastic additively separable problems with substructures of non-uniform salience. This study assumes that the recombination and mutation operators have the knowledge of the building blocks (BBs) and effectively exchange or search among competing BBs. Facetwise models of convergence time and population sizing have been used to determine the scalability of each algorithm. The analysis shows that for deterministic exponentially-scaled additively separable, problems, the BB-wise mutation is more efficient than crossover yielding a speedup of Θ(<em>l</em> log<em>l</em>), where <em>l</em> is the problem size. For the noisy exponentially-scaled problems, the outcome depends on whether scaling on noise is dominant. When scaling dominates, mutation is more efficient than crossover yielding a speedup of Θ(<em>l</em> log<em>l</em>). On the other hand, when noise dominates, crossover is more efficient than mutation yielding a speedup of Θ(<em>l</em>).
Antibacterial Activity of Schiff Bases Derived from OrthoDiaminocyclohexane, ...inventionjournals
Schiff bases (SBs) are known to possess many biological activities. In this paper we will be interested in nine SBs derived from ortho-diaminocyclohexane, meta-phenylenediamine, 1,6-diaminohexane and benzaldehydes variously substituted by nitro group. We had synthesized, characterized and tested these molecules for their antibacterial properties. Herein our study focuses in particular on the determination of quantum descriptors on which observed antibacterial activity depends, in order to be able to predict biological activities in analogue molecule series. Using quantum chemistry methods at B3LYP / 6-31G (d, p) level, we determined for each molecules, theoretical antibacterial potentials that we correlated to the experimental ones. Calculation results showed that, the energy of the Highest Occupied Molecular Orbital (EHOMO), electronegativity (χ) and electronic energy (E), are the best quantum descriptors related to the antibacterial activity values of studied molecules. The correlation coefficient R 2 indicates that 92.1% of the molecular descriptors defining this model are taken into account with a standard deviation of 0.152.The model significance is reflected by Fischer coefficient F = 7.721: Correlation coefficient of cross-validation = 0.88. This model is acceptable with . The values of the pCE50theo/pCE50exp values of the validation set tend to unity
IPG (Immobilized pH Gradient) based separations are frequently
used as the first step in shotgun proteomics methods; it yields an
increase in both the dynamic range and resolution of peptide
separation prior to the LC-MS analysis. Experimental isoelectric
point (pI) values can improve peptide identifications in conjunction
with MS/MS information. Our group has previously reported the
possibility of identifying theoretically peptides and proteins based
on different experimental properties. Thus, accurate estimation
of the pI value based on the amino acid sequence becomes critical
to perform these kinds of experiments. Nowadays, pI is commonly
predicted using the charge-state model [3], and/or the co-factor
algorithm. However, none of these methods is capable of
calculating the pI value for basic peptides accurately. In this
manuscript, we present an new approach that can significant
improve the pI estimation, by using Support Vector Machines
(SVM), an experimental amino acid descriptor taken from the
AAIndex database and the isoelectric point predicted by the
charge-state model.
large data set is not available for some disease such as Brain Tumor. This and part2 presentation shows how to find "Actionable solution from a difficult cancer dataset
When spatial data are distributed across multiple servers, there is an obvious difficulty with computing the likelihood function without combining all the data onto one server. Therefore, it would be of interest to compute estimates of the spatial parameters based on decompositions of the spatial held into blocks, each block corresponding to one server. Two methods suggest themselves, a \between blocks" approach in which each block is reduced to a single observation (or a low dimensional summary) to facilitate calculation of a likelihood across blocks, or a within blocks" approach in which the likelihood is calculated for each block and then combined into an overall likelihood for the full process. In fact, I argue that a hybrid approach that combines both ideas is best. Theoretical calculations are provided for the statistical efficiency of each approach. In conclusion, I will present some thoughts for optimal sampling designs with distributed data.
Network analysis of cancer metabolism: A novel route to precision medicineVarshit Dusad
Masters project presentation for MRes Systems and Synthetic Biology 2017-18 Imperial College London.
Study of cancer metabolism using constraint-based modeling and graph theory.
Let's get ready to rumble redux: Crossover versus mutation head to head on ex...kknsastry
This paper analyzes the relative advantages between crossover and mutation on a class of deterministic and stochastic additively separable problems with substructures of non-uniform salience. This study assumes that the recombination and mutation operators have the knowledge of the building blocks (BBs) and effectively exchange or search among competing BBs. Facetwise models of convergence time and population sizing have been used to determine the scalability of each algorithm. The analysis shows that for deterministic exponentially-scaled additively separable, problems, the BB-wise mutation is more efficient than crossover yielding a speedup of Θ(<em>l</em> log<em>l</em>), where <em>l</em> is the problem size. For the noisy exponentially-scaled problems, the outcome depends on whether scaling on noise is dominant. When scaling dominates, mutation is more efficient than crossover yielding a speedup of Θ(<em>l</em> log<em>l</em>). On the other hand, when noise dominates, crossover is more efficient than mutation yielding a speedup of Θ(<em>l</em>).
Antibacterial Activity of Schiff Bases Derived from OrthoDiaminocyclohexane, ...inventionjournals
Schiff bases (SBs) are known to possess many biological activities. In this paper we will be interested in nine SBs derived from ortho-diaminocyclohexane, meta-phenylenediamine, 1,6-diaminohexane and benzaldehydes variously substituted by nitro group. We had synthesized, characterized and tested these molecules for their antibacterial properties. Herein our study focuses in particular on the determination of quantum descriptors on which observed antibacterial activity depends, in order to be able to predict biological activities in analogue molecule series. Using quantum chemistry methods at B3LYP / 6-31G (d, p) level, we determined for each molecules, theoretical antibacterial potentials that we correlated to the experimental ones. Calculation results showed that, the energy of the Highest Occupied Molecular Orbital (EHOMO), electronegativity (χ) and electronic energy (E), are the best quantum descriptors related to the antibacterial activity values of studied molecules. The correlation coefficient R 2 indicates that 92.1% of the molecular descriptors defining this model are taken into account with a standard deviation of 0.152.The model significance is reflected by Fischer coefficient F = 7.721: Correlation coefficient of cross-validation = 0.88. This model is acceptable with . The values of the pCE50theo/pCE50exp values of the validation set tend to unity
IPG (Immobilized pH Gradient) based separations are frequently
used as the first step in shotgun proteomics methods; it yields an
increase in both the dynamic range and resolution of peptide
separation prior to the LC-MS analysis. Experimental isoelectric
point (pI) values can improve peptide identifications in conjunction
with MS/MS information. Our group has previously reported the
possibility of identifying theoretically peptides and proteins based
on different experimental properties. Thus, accurate estimation
of the pI value based on the amino acid sequence becomes critical
to perform these kinds of experiments. Nowadays, pI is commonly
predicted using the charge-state model [3], and/or the co-factor
algorithm. However, none of these methods is capable of
calculating the pI value for basic peptides accurately. In this
manuscript, we present an new approach that can significant
improve the pI estimation, by using Support Vector Machines
(SVM), an experimental amino acid descriptor taken from the
AAIndex database and the isoelectric point predicted by the
charge-state model.
large data set is not available for some disease such as Brain Tumor. This and part2 presentation shows how to find "Actionable solution from a difficult cancer dataset
When spatial data are distributed across multiple servers, there is an obvious difficulty with computing the likelihood function without combining all the data onto one server. Therefore, it would be of interest to compute estimates of the spatial parameters based on decompositions of the spatial held into blocks, each block corresponding to one server. Two methods suggest themselves, a \between blocks" approach in which each block is reduced to a single observation (or a low dimensional summary) to facilitate calculation of a likelihood across blocks, or a within blocks" approach in which the likelihood is calculated for each block and then combined into an overall likelihood for the full process. In fact, I argue that a hybrid approach that combines both ideas is best. Theoretical calculations are provided for the statistical efficiency of each approach. In conclusion, I will present some thoughts for optimal sampling designs with distributed data.
Network analysis of cancer metabolism: A novel route to precision medicineVarshit Dusad
Masters project presentation for MRes Systems and Synthetic Biology 2017-18 Imperial College London.
Study of cancer metabolism using constraint-based modeling and graph theory.
Integrative analysis of transcriptomics and proteomics data with ArrayMining ...Natalio Krasnogor
These slides are part of a presentation I gave on March 2010 at the BioInformatics and Genome Research Open Club at the Weizmann Institute of Science, Israel.
In these slides my student and I describe two web-applications for microarray and gene/protein set analysis,
ArrayMining.net and TopoGSA. These use ensemble and consensus methods as well as the
possibility of modular combinations of different analysis techniques for an integrative view of
(microarray-based) gene sets, interlinking transcriptomics with proteomics data sources. This integrative process uses tools from different fields, e.g. statistics, optimisation and network
topological studies. As an example for these integrative techniques, we use a microarray
consensus-clustering approach based on Simulated Annealing, which is part of the ArrayMining.net
Class Discovery Analysis module, and show how this approach can be combined in a modular
fashion with a prior gene set analysis. The results reveal that improved cluster validity indices can be obtained by merging the two methods, and provide pointers to distinct sub-classes within pre-defined tumour categories for a breast cancer dataset by the Nottingham Queens Medical Centre.
In the second part of the talk, I show how results from a supervised
microarray feature selection analysis on ArrayMining.net can be investigated in further detail with
TopoGSA, a new web-tool for network topological analysis of gene/protein sets mapped on a
comprehensive human protein-protein interaction network. I discuss results from a TopoGSA
analysis of the complete set of genes currently known to be mutated in cancer.
Predicting breast cancer: Adrian VallesAdrián Vallés
Performed and compared predictive modelling approaches (classification tree, logistic regression and random forest) to predict benign vs malignant breast cancers using R for the Data mining class (BANA 4080)
Mastering RNA-Seq (NGS Data Analysis) - A Critical Approach To Transcriptomic...Elia Brodsky
This workshop will address critical issues related to Transcriptomics data:
Processing raw Next Generation Sequencing (NGS) data:
1. Next Generation Sequencing data preprocessing:
Trimming technical sequences
Removing PCR duplicates
2. RNA-seq based quantification of expression levels:
Conventional pipelines (looking at known transcripts)
Identification of novel isoforms
Analysis of Expression Data Using Machine Learning:
3. Unsupervised analysis of expression data:
Principal Component Analysis
Clustering
4. Supervised analysis:
Differential expression analysis
Classification, gene signature construction
5. Gene set enrichment analysis
The workshop will include hands-on exercises utilizing public domain datasets:
breast cancer cell lines transcriptomic profiles (https://genomebiology.biomedcentral.com/articles/10.1186/gb-2013-14-10-r110),
patient-derived xenograft (PDX) mouse model of tumor and stroma transcriptomic profiles (http://www.oncotarget.com/index.php?journal=oncotarget&page=article&op=view&path[]=8014&path[]=23533), and
processed data from The Cancer Genome Atlas samples (https://cancergenome.nih.gov/).
Team: The workshops are designed by the researchers at the Tauber Bioinformatics Research Center at University of Haifa, Israel in collaboration with academic centers across the US. Technical support for the workshops is provided by the Pine Biotech team. https://edu.t-bio.info/a-critical-approach-to-transcriptomic-data-analysis/
1. Gene Interaction Analysis Using k-way Interaction Loglinear Model: A Case Study on Yeast Data Xintao Wu UNC Charlotte Daniel Barbara George Mason Univ. Liying Zhang Memorial Sloan Kettering Cancer Center Yong Ye UNC Charlotte
2.
3.
4.
5.
6.
7.
8.
9.
10.
11. Saturated log-linear model main effect 1-factor effect 2-factor effect which shows the dependency within the distributions of A,B.
12.
13.
14.
15.
16.
17.
18.
19. Experimental Results The frequencies and estimates from all k-way interactions ORF naming 28 24 0 54 YGL117W,YER175C,YMR096W,YMR095C 32 17 0 56 YJR109C,YMR094W,YMR096W,YMR095C 23 15 0 54 YJR109C, YGL117W, YMR096W,YMR095C 26 15 0 56 YHR029C ,YMR094W,YMR096W,YMR095C 3-way 2-way 1-way Frequency Gene Set Whether the open reading frame is on the Watson or Crick strand W or C C the order of the open reading frame on the chromosome arm, starting from the centromere and counting out to the telomere 3-digit 029 for the left or right arm L or R R for the chromosome upon which the ORF resides (16) A-P H for Yeast Y Y