Wojciech Lesiński

• University of Białystok

Antimicrobial resistance (AMR) is a growing global health concern, driven by urbanization and anthropogenic activities. This study investigated AMR distribution and dynamics across microbiomes from six U.S. cities, focusing on resistomes, viromes, and mobile genetic elements (MGEs). Using metagenomic data from the CAMDA 2023 challenge, we applied t...

This study presents a comprehensive analysis of 145 isolates from metagenomic samples across six major U.S. cities, focusing on their resistome, virome, and mobile genetic elements. Using a variety of techniques and mathematical models, we correlated metagenomic data with isolates to predict their origin and map resistome profiles in urban environm...

Prostate cancer is one of the leading causes of cancer death in men in Western societies. Predicting patients’ survival using clinical descriptors is important for stratification in the risk classes and selecting appropriate treatment. Current work is devoted to developing a robust Machine Learning (ML) protocol for predicting the survival of patie...

Motivation: Drug-induced liver injury (DILI) is one of the primary problems in drug development. Early prediction of DILI, based on the chemical properties of substances and experiments performed on cell lines, would bring a significant reduction in the cost of clinical trials and faster development of drugs. The current study aims to build predict...

Motivation Drug-induced liver injury (DILI) is one of the primary problems in drug development. Early prediction of DILI can bring a significant reduction in the cost of clinical trials. In this work we examined whether occurrence of DILI can be predicted using gene expression profile in cancer cell lines and chemical properties of drugs. Methods...

Two categories of immune responses—innate and adaptive immunity—have both polygenic backgrounds and a significant environmental component. The goal of the reported study was to define candidate genes and mutations for the immune traits of interest in chickens using machine learning–based sensitivity analysis for single-nucleotide polymorphisms (SNP...

Current study aims at prediction of the onset of malignant cardiac arrhythmia in patients with Implantable Cardioverter-Defibrillators (ICDs) using Machine Learning algorithms. The input data consisted of 184 signals of RR-intervals from 29 patients with ICD, recorded both during normal heartbeat and arrhythmia. For every signal we generated 47 des...

Prostate cancer is the most common cancer among men in high-income countries. This study examines which clinical variables are useful for prediction of clinical end-point for patients with metastatic castration-resistant prostate cancer. First informative variables were found using several feature selection methods and then used to build Random For...

Motivation: Drug-induced liver injury (DILI) is one of the primary problems in drug development. Early prediction of DILI can bring a significant reduction in the cost of clinical trials. In this work we examined whether occurrence of DILI can be predicted using gene expression profile in cancer cell lines and chemical properties of drugs. Methods...

Background: Modern experimental techniques deliver data sets containing profiles of tens of thousands of potential molecular and genetic markers that can be used to improve medical diagnostics. Previous studies performed with three different experimental methods for the same set of neuroblastoma patients create opportunity to examine whether augme...

Purpose: Docetaxel has a demonstrated survival benefit for patients with metastatic castration-resistant prostate cancer (mCRPC); however, 10% to 20% of patients discontinue docetaxel prematurely because of toxicity-induced adverse events, and the management of risk factors for toxicity remains a challenge. Patients and methods: The comparator a...

Background: Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an ope...

The article is focused on a particular aspect of classification, namely the imbalance of recognized classes. The paper contains a comparative study of results of musical symbols classification using known algorithms: k-nearest neighbors, k-means, Mahalanobis minimal distance, and decision trees. Authors aim at addressing the problem of imbalanced p...

The paper introduces definitions of exclusion relations in spaces of features and concepts. Concepts correspond to phenomena and they are described with their features. The objective of our research is to investigate and describe possible structuring and relations in the feature and concept spaces. In this article, three types of exclusions: weak,...

The article is focused on a particular aspect of classification, namely the issue of class imbalance. Imbalanced data adversely affects the recognition ability and requires proper classifier’s construction. In this work we present a case of music notation as an example of imbalanced data. Three classification algorithms - random forest, standard SV...

The article presents an application of fuzzy sets with triangular norms and balanced fuzzy sets with balanced norms to decision making modelling. We elaborate on a vector-based method for decision problem representation, where each element of a vector corresponds to an argument analysed by a decision maker. Vectors gather information that influence...

The article presents an application of fuzzy sets with triangular norms and balanced fuzzy sets with balanced norms to decision making modelling. We elaborate on a vector-based method for decision problem representation, where each element of a vector corresponds to an argument analysed by a decision maker. Vectors gather information that influence...

Decision trees are considered to be among the best classifiers. In this work we use decision trees and its families to the problem of imbalanced data recognition. Considered are aspects of recognition without rejection and with rejection: it is assumed that all recognized elements belong to desired classes in the first case and that some of them ar...

The article is focused on a particular aspect of classification, namely the imbalance of recognized classes. Imbalanced data adversely affects the recognition ability and requires proper classifier’s construction. The aim of presented study is to explore the capabilities of classifier combining methods with such raised problem. In this paper author...

Proper image recognition depends on many factors. Features’ selection and classifiers are most important ones. In this paper we discuss a number of features and several classifiers. The study is focused on how features’ selection affects classifier efficiency with special attention given to random forests. Different construction methods of decision...