Andrey Alekseenko

Expert in systems biology and biostatistics, developed methods and tools for integration of heterogeneous large-scale datasets using global network 
context. Andrey can guide in relevant methodologies and assist in interpretating analysis results. He can also help with high-throughput data management, statistics, functional interpretation, and bridging gaps between different sides of analysis. Andrey received extensive training in higher education and has vast experience in teaching biostatistics and systems biology at both undergraduate and graduate levels.

The activity is focused on creating and applying biostatistics, data
integration, and systems biology methods to biomedicine and clinical projects
http://www.evistat.se/ [1].
This work includes statistical analysis of preclinical and clinical datasets,
such as candidate drug analysis and evaluation of radio-, chemo-, immuno-,
and targeted therapies. The data analysis methods are used for development of
e.g. companion diagnostics for anti-cancer therapies and early markers of
autoimmune diseases. It requires evaluation of existing and development of
novel tools and pipelines for genomics, transcriptomics, and cancer
immunology. A major achievement in the past was FunCoup: a machine learning
framework for reconstructing gene networks via systematic integration of
large public datasets[1]. Due to its robust design, comprehensive data
collection and analytic web interface, FunCoup became a biologically sound
and useful resource for both online and offline usage. Next followed the
development of a new methodology for Network Enrichment Analysis, NEA[2]
which served functional exploration and impact evaluation of experimental
gene lists. The method was demonstrated to be superior to existing
alternatives in e.g. finding molecular determinants of anti-cancer drug
response[3] and was applied in a number of collaborative efforts. This was
complemented with further development of NEA software and online tools, such
as R package NEArender[4] for network analysis in automated and parallelized
data pipelines as well as fully functional analytic web suits EviNet[5]
https://www.evinet.org [2, 3] and EviCor https://www.evicor.org that 
facilitate machine learning and predictive modelling using public databases
and in-house data[21]. We develop methods of network analysis in order to
investigate high-throughput data with information on drug response and
further combine systems biology profiles with clinical covariates to find
informative and prognostic markers for patient subsets. Particular focus lays
within such areas as:
* raising molecular landscape investigation to the pathway level;
* discovery of novel functional modules in the interactome;
* distinguishing between driver and passenger mutations in cancer genomes;
* inference of causative regulatory networks;
* comparative network analysis under contrast (e.g. pathological vs. normal)
conditions;
* evaluation of functional relevance of candidate markers;
* cross-validation of predictive signatures using novel, independent
datasets;
* breaking the patient population into sub-types to enable efficient
prognostication.
Our contribution to clinical interpretation of tumor sequencing data has been
a pipeline for driver mutation analysis (Merid et al., 2014[6]; Petrov and
Alexeyenko, 2022[7]). A promising avenue has been the development of 
marker-based diagnostics for cancer immunotherapy together with researchers
of Karolinska Institutet and Istituto Nazionale Tumori IRCCS Pascale
(Napoli)[8]. Another example of a large team effort was our work in Norway
spruce genome project[9] at SciLifeLab.
Otherwise, we use large and complex datasets in order to solve concrete
problems, such as the identification of early markers of autoimmune
diseases[10], development of companion diagnostics for checkpoint and
targeted therapies, evaluation of candidate disease genes in common and rare
diseases[11, 12, 13, 14, 22] as well as creation of novel tools for genomics,
transcriptomics, and immunology[15, 16, 17, 18, 19, 20, 21].
-------- REFERENCES ----------------------------------------------------------
1. Alexeyenko, A. & Sonnhammer, E. L. L. Global
networks of functional coupling in eukaryotes from comprehensive data
integration. /Genome Res./ *19*, 1107–1116 (2009).
http://www.ncbi.nlm.nih.gov/pubmed/19246318 [4]
2. Alexeyenko, A. /et al./ Network enrichment
analysis: extension of gene-set enrichment analysis to gene networks. /BMC
Bioinformatics/ *13*, 226 (2012). http://www.ncbi.nlm.nih.gov/pubmed/22966941
[5]
3. Franco, M. /et al./ Prediction of response to
anti-cancer drugs becomes robust via network integration of molecular data.
/Sci Rep/ *9*, 2379 (2019). http://dx.doi.org/10.1038/s41598-019-39019-2 [6]
4. Jeggari, A. & Alexeyenko, A. NEArender: an R
package for functional interpretation of ‘omics’ data via network
enrichment analysis. /BMC Bioinformatics/ *18*, (2017).
https://www.ncbi.nlm.nih.gov/pubmed/28361684 [7]
5. Jeggari, A. /et al./ EviNet: a web platform
for network enrichment analysis with flexible definition of gene sets.
/Nucleic Acids Res/ *46*, W163–W170 (2018).
https://doi.org/10.1093/nar/gky485 [8]
6. Merid, S. K., Goranskaya, D. & Alexeyenko, A.
Distinguishing between driver and passenger mutations in individual cancer
genomes by network enrichment analysis. /BMC Bioinformatics/ *15*, 308
(2014). http://www.ncbi.nlm.nih.gov/pubmed/25236784 [9]
7. Petrov, I. & Alexeyenko, A. Individualized
discovery of rare cancer drivers in global network context. /eLife/ *11*,
e74010 (2022). https://doi.org/10.7554/eLife.74010 [10]
8. Mallardo, D. /et al./ Toward
transcriptomics-based prediction of response to immune checkpoint inhibitor
in patients with malignant melanoma. in /JOURNAL OF TRANSLATIONAL MEDICINE/
vol. 18 (BMC CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND, 2020).
9. Nystedt, B. /et al./ The Norway spruce genome
sequence and conifer genome evolution. /Nature/ *497*, 579–584 (2013).
10. Brink, M., Lundquist, A., Alexeyenko, A., Lejon,
K. & Rantapää-Dahlqvist, S. Protein profiling and network enrichment
analysis in individuals before and after the onset of rheumatoid arthritis.
/Arthritis Research & Therapy/ *21*, 288 (2019).
11. Reynolds, C. A. /et al./ Analysis of lipid
pathway genes indicates association of sequence variation near
SREBF1/TOM1L2/ATPAF2 with dementia risk. /Hum. Mol. Genet./ *19*, 2068–2078
(2010).
12. Bennet, A. M. /et al./ Genetic association of
sequence variants near AGER/NOTCH4 and dementia. /J. Alzheimers Dis./ *24*,
475–484 (2011).
13. Hong, M.-G., Alexeyenko, A., Lambert, J.-C.,
Amouyel, P. & Prince, J. A. Genome-wide pathway analysis implicates
intracellular transmembrane protein transport in Alzheimer disease. /J. Hum.
Genet./ *55*, 707–709 (2010).
14. Brownstein, C. A. /et al./ An international
effort towards developing standards for best practices in analysis,
interpretation and reporting of clinical genome sequencing results in the
CLARITY Challenge. /Genome Biol/ *15*, R53 (2014).
15. Franzén, B. /et al./ A fine‐needle
aspiration‐based protein signature discriminates benign from malignant
breast lesions. /Mol Oncol/ *12*, 1415–1428 (2018).
16. Franzén, B. /et al./ Protein profiling of
fine‐needle aspirates reveals subtype‐associated immune signatures and
involvement of chemokines in breast cancer. /Mol Oncol/ *13*, 376–391
(2019).
17. Bersani, C. /et al./ Genome-wide identification
of Wig-1 mRNA targets by RIP-Seq analysis. /Oncotarget/ *7*, 1895–1911
(2016).
18. Lee, W. /et al./ Identifying and Assessing
Interesting Subgroups in a Heterogeneous Population. /Biomed Res Int/ *2015*,
462549 (2015).
19. Akan, P. /et al./ Comprehensive analysis of the
genome transcriptome and proteome landscapes of three tumor cell lines.
/Genome Med/ *4*, 86 (2012).
20. Giacomello, S. /et al./ Spatially resolved
transcriptome profiling in model plant species. /Nat Plants/ *3*, 17061
(2017).
21. Petrov, I. & Alexeyenko, A. EviCor: Interactive
Web Platform for Exploration of Molecular Features and Response to
Anti-cancer Drugs. /Journal of Molecular Biology/ *434*, 167528 (2022).
https://doi.org/10.1016/j.jmb.2022.167528 [11]
22. [12]* *Alexeyenko A., ... Hydbring, P., and
Ekman, S. Plasma RNA profiling unveils transcriptional signatures associated
with resistance to osimertinib in EGFR T790M positive non-small cell lung
cancer patients /Transl Lung Cancer Res/*. 11*(10):2064-2078. (2022)
https://dx.doi.org/10.21037/tlcr-22-236 [13]
[1] http://www.evistat.se/
[2] https://www.evinet.org/
[3] https://www.evicor.org/
[4] http://www.ncbi.nlm.nih.gov/pubmed/19246318
[5] http://www.ncbi.nlm.nih.gov/pubmed/22966941
[6] http://dx.doi.org/10.1038/s41598-019-39019-2
[7] https://www.ncbi.nlm.nih.gov/pubmed/28361684
[8] https://doi.org/10.1093/nar/gky485
[9] http://www.ncbi.nlm.nih.gov/pubmed/25236784
[10] https://doi.org/10.7554/eLife.74010
[11] https://doi.org/10.1016/j.jmb.2022.167528
[12] https://doi.org/10.1016/j.jmb.2022.167528
[13] https://dx.doi.org/10.21037/tlcr-22-236

1. Data Analysis: from Concepts to Results (Free University - Brīvā universitāte, 2023-2024): organizer and lecturer.
2. Computational System Biology (Linköping University, 2023-2024): lecturer.
3. Artificial Intelligence and Machine Learning for Biomedical and Clinical Research (Karolinska Institutet (2020-2025): 2 weeks.
4. Molecular Oncology and Biostatistics; bachelor program in Biomedicine (2015): lecturer, tutor.
5. Summer School in Computational and Systems Biology of Cancer,  StratCan-KI-BILS (2014): co-organizer and director, lecturer, tutor, examiner.
6. Omics Data Analysis: from raw data to biological information,  Karolinska Institute (2012, 2013, 2014): 1-hour lectures.
7. Bioinformatics; master program in Biomedicine, Karolinska Institute (2009, 2014): lecturer, tutor, examiner.
8. NatiOn: research school for clinical cancer research (2011, 2013): lecturer, examiner. 
9. Practical Proteomics at Tumor biology / Oncology program,  Karolinska Institute (2010, 2011): 1-hour lectures.
10. Cancer Systems Biology at Tumor biology/oncology program, Karolinska Institute (2010): co-organizer, lecturer, tutor, examiner.
11. Experimental Design and Statistical Analysis at Cell Biology and Genetics PhD program, Karolinska Institute (2006): organizer,  director, lecturer, tutor, examiner.
12. "Applied statistics" for researchers of Northern Caucasus Research Institute for Horticulture and Viticulture, Krasnodar (2002): organizer and lecturer.