Systems genetics aims to understand complex traits by considering genetic variation, intermediate phenotypes like gene expression and metabolites, and their interactions across individuals. It links variations in molecules to clinical traits through correlation analysis and statistical modeling of interaction networks. While challenging, integrating multi-omics data through network approaches can provide a more comprehensive view of the molecular architecture underlying common diseases.
Robust Pathway-based Multi-Omics Data Integration using Directed Random Walk ...SOYEON KIM
17th Annual International Conference on Critical Assessment of Massive Data Analysis (CAMDA 2018)
Cancer Data Integration Challenge (http://camda.info/)
Deep learning based multi-omics integration, a surveySOYEON KIM
1. Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders, Pacific Symposium on Biocomputing, 2015
2. A deep learning approach for cancer detection and relevant gene identification, Pacific Symposium on Biocomputing, 2016
3. Deep Learning based multi-omics integrationrobustly predicts survival in liver cancer, preprint, 2017
A survey of heterogeneous information network analysisSOYEON KIM
A Survey of Heterogeneous Information Network Analysis
Chuan Shi, Member, IEEE,
Yitong Li, Jiawei Zhang, Yizhou Sun, Member, IEEE,
and Philip S. Yu, Fellow, IEEE
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2015
Metabolomic data analysis and visualization toolsDmitry Grapov
A description of data analysis and visualization tools for metabolomic and other high dimensional data sets, developed at the NIH West Coast Metabolomics Center.
Robust Pathway-based Multi-Omics Data Integration using Directed Random Walk ...SOYEON KIM
17th Annual International Conference on Critical Assessment of Massive Data Analysis (CAMDA 2018)
Cancer Data Integration Challenge (http://camda.info/)
Deep learning based multi-omics integration, a surveySOYEON KIM
1. Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders, Pacific Symposium on Biocomputing, 2015
2. A deep learning approach for cancer detection and relevant gene identification, Pacific Symposium on Biocomputing, 2016
3. Deep Learning based multi-omics integrationrobustly predicts survival in liver cancer, preprint, 2017
A survey of heterogeneous information network analysisSOYEON KIM
A Survey of Heterogeneous Information Network Analysis
Chuan Shi, Member, IEEE,
Yitong Li, Jiawei Zhang, Yizhou Sun, Member, IEEE,
and Philip S. Yu, Fellow, IEEE
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2015
Metabolomic data analysis and visualization toolsDmitry Grapov
A description of data analysis and visualization tools for metabolomic and other high dimensional data sets, developed at the NIH West Coast Metabolomics Center.
A Tenyu et al, ChainRank, a chain prioritisation method for contextualisation of biological networks, BMC Bioinformatics 2016 17:17, DOI: 10.1186/s12859-015-0864-x
Introduction
What is cheminformatics?
Why do we have to use informatics methods in chemistry?
Is it cheminformatics or chemoinformatics?
Emergence of cheminformations
Three major aspects of cheminformatics
Basics of cheminformatics
Topological representations
Tools used for cheminformatics
Application of cheminformatics
Role of cheminformatics in morden drug discovery
Conclusion
Bibliography
Validation is the process of checking that your model is consistent with stereochemical standards i.e., validation is the process of evaluating reliability
In this presentation various aspects of validation are discussed
A Tenyu et al, ChainRank, a chain prioritisation method for contextualisation of biological networks, BMC Bioinformatics 2016 17:17, DOI: 10.1186/s12859-015-0864-x
Introduction
What is cheminformatics?
Why do we have to use informatics methods in chemistry?
Is it cheminformatics or chemoinformatics?
Emergence of cheminformations
Three major aspects of cheminformatics
Basics of cheminformatics
Topological representations
Tools used for cheminformatics
Application of cheminformatics
Role of cheminformatics in morden drug discovery
Conclusion
Bibliography
Validation is the process of checking that your model is consistent with stereochemical standards i.e., validation is the process of evaluating reliability
In this presentation various aspects of validation are discussed
Prote-OMIC Data Analysis and VisualizationDmitry Grapov
Introductory lecture to multivariate analysis of proteomic data.
Material from the UC Davis 2014 Proteomics Workshop.
See more at: http://sourceforge.net/projects/teachingdemos/files/2014%20UC%20Davis%20Proteomics%20Workshop/
Extracting reaction networks from databases – opening Pandora’s boxDerek Wright
Presentation about the paper Extracting reaction networks from databases – opening Pandora’s box by Fearnley et al. (2013) http://bib.oxfordjournals.org/content/early/2013/08/14/bib.bbt058.full
Pathways-Driven Sparse Regression Identifies Pathways and Genes Associated wi...SOYEON KIM
Summary of paper "Pathways-Driven Sparse Regression Identifies Pathways and Genes Associated with High-Density Lipoprotein Cholesterol in Two Asian Cohorts",
Silver M, Chen P, Li R, Cheng C-Y, Wong T-Y, et al.
In PLOS Genetics, 2013
Translated Learning: Transfer learning across different feature spaces
Wenyuan Dai, Yuqiang Chen, Gui-Rong Xue, Qiang Yang, and Yong Yu.
In Proceedings of Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008)
Semi-automatic ground truth generation using unsupervised clustering and limi...SOYEON KIM
Semi-automatic ground truth generation using unsupervised clustering and limited manual labeling: Application to handwritten character recognition
Szilárd Vajda, Yves Rangoni, Hubert Cecotti
Pattern Recognition Letters, 2015
Evaluating color descriptors for object and scene recognitionSOYEON KIM
Van De Sande, Koen EA, Theo Gevers, and Cees GM Snoek. "Evaluating color descriptors for object and scene recognition." Pattern Analysis and Machine Intelligence, IEEE Transactions on 32.9 (2010): 1582-1596.
Outcome-guided mutual information networks for investigating gene-gene intera...SOYEON KIM
TBC2014 poster
"Outcome-guided mutual information networks for investigating gene-gene interaction effects on clinical outcomes", Hyun-hwan Jeong, So Yeon Kim, Kyubum Wee, Kyung-Ah Sohn
Investigating the Effectiveness of E-mail Spam Image Data for Phone Spam Imag...SOYEON KIM
K. So Yeon, B. Yenewondim, and S. Kyung-Ah, "Investigating the Effectiveness of E-mail Spam Image Data for Phone Spam Image Detection Using Scale Invariant Feature Transform Image Descriptor.", Information Science and Applications, LNEE 339, 2015, in press.
A study on the spacio temporal trend of brand index using twitter messages se...SOYEON KIM
Cho, Seung Woo, et al. "Investigating Temporal and Spatial Trends of Brand Images Using Twitter Opinion Mining." Information Science and Applications (ICISA), 2014 International Conference on. IEEE, 2014.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
2. Systems genetics?
• An approach to understand the flow of biological information that
underlies complex traits across individuals in a population
• Consider both underlying genetic variation and intermediate
phenotypes (e.g. gene expression, protein, metabolite) in addition to
gene-by-gene and gene-by-environment interactions.
* Image from Systems genetics of cancer 2018
3.
4. Analysis of systems genetics data
• Linking the variations in molecular phenotypes to clinical traits
• Correlation between molecular phenotypes and clinical traits
• Genetic mapping of molecular phenotypes and clinical traits
• Statistical modeling captures the interactions among the traits (e.g. Network
approaches to identify modules (groups) can be related to clinical trait)
5. The flow of biological information
• Genetic variants affect transcript/protein/metabolite levels (e.g.
eQTL, pQTL, mQTL)
• Comprehensive genotype–phenotype maps require analyses of
protein levels and their modifications
• Levels of many metabolites showed high heritability
6. Complexity of interactions
• Gene-by-gene (G x G), gene-by-environment (G x E) interactions
• Almost all common diseases result from a combination of genetic and
environmental factors (e.g. physical, chemical, biological, behavior
patterns or life events)
“Two different genotypes respond to environmental variation”
* Image from Wikipedia ‘Gene-environment interaction’
7. Network modelling
• Network approaches to understand how they interact with each
other and influence complex traits
8. Network modelling
1. Based on curated knowledge (e.g. metabolic pathways)
• Typically not comprehensive
2. Derived from experimental data on the basis of physical interactions
3. Inferred from high-throughput data
• Data-driven methods may uncover novel relationships and
interactions
• There is no experimental approach to create the 'true' network
structure
9. Network modelling
- No single network method
outperformed the others
- Different network connectivity
patterns by various approaches
to various levels of success
- Consensus network showed the
most robust performances
Marbach, D. et al. Wisdom of crowds for robust gene network inference. Nature Methods 9, 796–804 (2012).
10. Advanced topics for multi-omics data
• Multi-omics methods in the context
of metabolomics
• Univariate associations between
metabolites and other omics markers
• Further analyzed with networks, or
pathway enrichment analysis
• Multivariate methods exploit
covariation within / btw omics layers
using pathway/network approaches
Jan K. et al. “Computational approaches for systems metabolomics”, Current Opinion in Biotechnology (2016)
11. Advanced topics for multi-omics data
• Network approaches to
systems biology for multi-
omics data integration
• Networks as
outcomes/priors/features
• Epistasis
• Network inference
• Pathway enrichment/network
analysis
• Integrative analysis (e.g.
PARADIGM, ATHENA)
Jingwen Yan, et al. “Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data”, Briefings in Bioinformatics (2018)
12. Conclusions
• It aims to quantitate and integrate intermediate phenotypes, such as
transcript, protein or metabolite levels, in populations that vary for
traits of interest
• It provides a global view of the molecular architecture of complex
traits
• It is useful for the identification of genes, pathways and networks that
underlie common human diseases
• In the future, additional data modalities can be incorporated (e.g.
clinical imaging data, time course data, etc.)