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Nikolai Russkikh

Nikolai Russkikh
Independent Researcher

Leading AI/ML applications in life sciences, specializing in protein and aptamer engineering. Open to collaborations.

About

12
Publications
916
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70
Citations
Introduction
I specialize in AI-driven methods in life sciences. Currently, my main focus is on AI-driven protein and aptamer design. My research includes developing enhancements in protein functionality and stability, validated through rigorous wet lab procedures. Additionally, I have a broad expertise in applications of AI to biological imaging and single-cell data, aiming to advance our understanding and diagnostic capabilities in these areas.

Publications

Publications (12)
Article
Full-text available
Introduction Modification of natural enzymes to introduce new properties and enhance existing ones is a central challenge in bioengineering. This study is focused on the development of Taq polymerase mutants that show enhanced reverse transcriptase (RTase) activity while retaining other desirable properties such as fidelity, 5′- 3′ exonuclease acti...
Preprint
Full-text available
Modification of natural enzymes to introduce new properties and enhance existing ones is a central challenge in bioengineering. This study is focused on the development of Taq polymerase mutants that show enhanced reverse transcriptase (RTase) activity while retaining other desirable properties such as fidelity, 5′-3′ exonuclease activity, effectiv...
Article
In this paper, we discuss applications of neural networks to recognizing knots and, in particular, to the unknotting problem. One of the motivations for this study is to understand how neural networks work on the example of a problem for which rigorous mathematical algorithms for its solution are known. We represent knots by rectangular Dynnikov di...
Preprint
Full-text available
Biology has become a data-intensive science. Recent technological advances in single-cell genomics have enabled the measurement of multiple facets of cellular state, producing datasets with millions of single-cell observations. While these data hold great promise for understanding molecular mechanisms in health and disease, analysis challenges aris...
Chapter
Full-text available
Representative Elementary Volume (REV) at which the material properties do not vary with change in volume is an important quantity for making measurements or simulations which represent the whole. We discuss the geometrical method to evaluation of REV based on the quantities coming in the Steiner formula from convex geometry. For bodies in three-di...
Article
Deep learning is an actively developing technology of machine learning. Its medical applications are the subject of ever increasing research worldwide. The algorithm for detection of enlargement of the ventricular system of the brain was tested using 200 series of digital MRI brain scan images captured in T2-weighted mode in the axial plane. The ob...
Preprint
Full-text available
In this article we discuss applications of neural networks to recognising knots and, in particular, to the unknotting problem. One of motivations for this study is to understand how neural networks work on the example of a problem for which rigorous mathematical algorithms for its solution are known. We represent knots by rectangular Dynnikov diagr...
Preprint
Representative Elementary Volume (REV) at which the material properties do not vary with change in volume is an important quantity for making measurements or simulations which represent the whole. We discuss the geometrical method to evaluation of REV based on the quantities coming in the Steiner formula from convex geometry. For bodies in the thre...
Article
Full-text available
Motivation: The transcriptomic data are being frequently used in the research of biomarker genes of different diseases and biological states. The most common tasks there are the data harmonization and treatment outcome prediction. Both of them can be addressed via the style transfer approach. Either technical factors or any biological details abou...
Article
Full-text available
In vitro cellular models are promising tools for studying normal and pathological conditions. One of their important applications is the development of genetically engineered biosensor systems to investigate, in real time, the processes occurring in living cells. At present, there are fluorescence, protein-based, sensory systems for detecting vario...
Preprint
Full-text available
The in vitro cellular models are promising tools for studying normal and pathological conditions. One of their important applications is the development of genetically engineered biosensor systems to investigate the processes occurring in living cells in real time. Today, there are fluorescence protein based sensory systems for detecting various su...
Preprint
Full-text available
Motivation The transcriptomic data is being frequently used in the research of biomarker genes of different diseases and biological states. The most common tasks there are data harmonization and treatment outcome prediction. Both of them can be addressed via the style transfer approach. Either technical factors or any biological details about the s...

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