Ms Inna Tishchenko

Ms Inna Tishchenko

Research Academic

School of Elect Engineering and Computer Science

Career Summary

Biography

Inna joined the University of Newcastle as research academic at the Priority Research Centre for Bioinformatics, Biomarker Discovery and Information-Based medicine (CIBM) in 2014, following her interests in data analysis and biomedical science.

Inna graduated with a Master of Science degree in Mechanical Engineering with a focus in Robotics, Systems and Control, from the Swiss Federal Institute of Technology Zurich (ETH Zurich), Switzerland, in 2012. In the years 2010 and 2011 she was employed by ETH Zurich as a teaching assistant in computational mathematics. Inna completed her master's thesis in collaboration with ABB Corporate Research Switzerland Ltd, during which she solved the optimisation problem of minimising fuel consumption of dual fuel turbocharged engines. Following the completion of her master's degree, she accomplished an internship with the Commonwealth Scientific and Industrial Research Organisation (CSIRO), where she worked on computer vision related problems.

At CIBM, Inna has been involved in data analysis for Breast Cancer and Alzheimer's Disease related data. She has been working on molecular subtyping and DNA copy number aberration problems within malignant breast tissue, specifically covering cancer tumours of basal-like, luminal and HER2-enriched types. Inna has also worked on the clustering problem of stratifying Alzheimer's Disease patients into distinct groups by their symptomatic characteristics. Her research brings together a number of disciplines and techniques, including clustering and classification problems, graph analysis, filtering techniques, survival analysis, statistical significance analysis and biomedical interpretation of the results.


Qualifications

  • Master of Science (Mechanical Engineering), Swiss Federal Institute of Technology - Zurich
  • Bachelor of Science (Mechanical Engineering), Swiss Federal Institute of Technology - Zurich

Keywords

  • Bioinformatics
  • Data Analysis
  • Machine Learning
  • Systems Analysis

Languages

  • German (Fluent)
  • Russian (Fluent)
  • French (Working)

Fields of Research

Code Description Percentage
010406 Stochastic Analysis and Modelling 30
170203 Knowledge Representation and Machine Learning 40
060102 Bioinformatics 30

Professional Experience

Academic appointment

Dates Title Organisation / Department
1/04/2014 -  Research Academic University of Newcastle
School of Elect Engineering and Computer Science
Australia
1/02/2010 - 1/06/2011 Tutor in Numerical Analysis and Mechanical Oscillations ETH Zurich (Swiss Federal Institute of Technology Zurich)
Department of Mechanical and Process Engineering
Switzerland

Professional appointment

Dates Title Organisation / Department
1/11/2012 - 31/08/2013 Research Internship in Computer Vision CSIRO (Commonwealth Scientific and Industrial Research Organisation)
ICT Centre
Australia
1/03/2012 - 1/09/2012 Master's Thesis in Optimisation Problems ABB Corporate Research Switzerland Ltd
Switzerland
1/09/2011 - 31/12/2011 Work Experience Internship in Control Systems ABB Switzerland Ltd
Turbocharging Systems
Switzerland
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Publications

For publications that are currently unpublished or in-press, details are shown in italics.


Journal article (3 outputs)

Year Citation Altmetrics Link
2016 Tishchenko I, Milioli HH, Riveros C, Moscato P, 'Extensive transcriptomic and genomic analysis provides new insights about luminal breast cancers', PLoS ONE, 11 (2016)

© 2016 Tishchenko et al.Despite constituting approximately two thirds of all breast cancers, the luminal A and B tumours are poorly classified at both clinical and molecular leve... [more]

© 2016 Tishchenko et al.Despite constituting approximately two thirds of all breast cancers, the luminal A and B tumours are poorly classified at both clinical and molecular levels. There are contradictory reports on the nature of these subtypes: some define them as intrinsic entities, others as a continuum. With the aim of addressing these uncertainties and identifying molecular signatures of patients at risk, we conducted a comprehensive transcriptomic and genomic analysis of 2,425 luminal breast cancer samples. Our results indicate that the separation between the molecular luminal A and B subtypes-per definition-is not associated with intrinsic characteristics evident in the differentiation between other subtypes. Moreover, t- SNE and MST-kNN clustering approaches based on 10,000 probes, associated with luminal tumour initiation and/or development, revealed the close connections between luminal A and B tumours, with no evidence of a clear boundary between them. Thus, we considered all luminal tumours as a single heterogeneous group for analysis purposes. We first stratified luminal tumours into two distinct groups by their HER2 gene cluster co-expression: HER2-amplified luminal and ordinary-luminal. The former group is associated with distinct transcriptomic and genomic profiles, and poor prognosis; it comprises approximately 8% of all luminal cases. For the remaining ordinary-luminal tumours we further identified the molecular signature correlated with disease outcomes, exhibiting an approximately continuous gene expression range from low to high risk. Thus, we employed four virtual quantiles to segregate the groups of patients. The clinico-pathological characteristics and ratios of genomic aberrations are concordant with the variations in gene expression profiles, hinting at a progressive staging. The comparison with the current separation into luminal A and B subtypes revealed a substantially improved survival stratification. Concluding, we suggest a review of the definition of luminal A and B subtypes. A proposition for a revisited delineation is provided in this study.

DOI 10.1371/journal.pone.0158259
Co-authors Carlos Riveros
2016 Milioli HH, Vimieiro R, Tishchenko I, Riveros C, Berretta R, Moscato P, 'Iteratively refining breast cancer intrinsic subtypes in the METABRIC dataset.', BioData Min, 9 2 (2016)
DOI 10.1186/s13040-015-0078-9
Citations Scopus - 1
Co-authors Pablo Moscato, Regina Berretta, Carlos Riveros
2015 Milioli HH, Vimieiro R, Riveros C, Tishchenko I, Berretta R, Moscato P, 'The Discovery of Novel Biomarkers Improves Breast Cancer Intrinsic Subtype Prediction and Reconciles the Labels in the METABRIC Data Set.', PLoS One, 10 e0129711 (2015) [C1]
DOI 10.1371/journal.pone.0129711
Citations Scopus - 1
Co-authors Regina Berretta, Pablo Moscato, Carlos Riveros

Conference (6 outputs)

Year Citation Altmetrics Link
2015 Milioli H, Tishchenko I, Riveros C, Berretta R, Moscato P, 'BASAL-LIKE BREAST CANCER SUBGROUPS UNCOVERED BY GENOMIC AND TRANSCRIPTOMIC PROFILES AND OVERALL SURVIVAL OUTCOMES', ASIA-PACIFIC JOURNAL OF CLINICAL ONCOLOGY (2015) [E3]
Co-authors Pablo Moscato, Regina Berretta
2015 Tishchenko I, Milioli H, Riveros C, Moscato P, 'HOW INTRINSIC ARE LUMINAL BREAST CANCER SUBTYPES?', ASIA-PACIFIC JOURNAL OF CLINICAL ONCOLOGY (2015) [E3]
Co-authors Pablo Moscato, Carlos Riveros
2015 Tishchenko I, Riveros C, Moscato P, 'REVISION OF MOLECULAR BREAST CANCER SUBTYPES', ASIA-PACIFIC JOURNAL OF CLINICAL ONCOLOGY (2015) [E3]
Co-authors Pablo Moscato
2015 Milioli HH, Tishchenko I, Riveros C, Berretta R, Moscato P, 'Molecular classification of basal-like breast cancer subtypes based on predictive survival markers', ANNALS OF ONCOLOGY (2015) [E3]
DOI 10.1093/annonc/mdv117.11
Co-authors Carlos Riveros, Pablo Moscato, Regina Berretta
2015 Milioli HH, Tishchenko I, Riveros C, Sakoff J, Berretta R, Moscato P, 'Consensus on breast cancer cell lines classification for an effective and efficient clinical decision-making', ANNALS OF ONCOLOGY (2015) [E3]
DOI 10.1093/annonc/mdv121.8
Co-authors Regina Berretta, Jennette Sakoff, Pablo Moscato, Carlos Riveros
2015 Milioli HH, Vimieiro R, Tishchenko I, Riveros C, Berretta R, Moscato P, 'Refining the breast cancer molecular subtypes in the METABRIC data set' (2015) [O1]
Co-authors Pablo Moscato, Regina Berretta
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Ms Inna Tishchenko

Position

Research Academic
School of Elect Engineering and Computer Science
Faculty of Engineering and Built Environment

Contact Details

Email inna.tishchenko@newcastle.edu.au
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