Artem Lysenko

QBayMic: Quantum-coupled variational Bayes for clustering and feature selection in low-signal microbiome data

QBayMic improves clustering and feature selection in low-signal microbiome data by using quantum-coupled variational Bayes, outperforming classical methods in stability and accuracy.

A hyperspherical deep Bayesian model for interpretable clustering and relationship prediction in microbiome multi-omics integration

DBayesCM integrates multi-omics microbiome data using a deep Bayesian model to enable interpretable clustering and relationship prediction across diseases.

vDeepInsight: an injective three-dimensional voxel carrier for tabular-feature neighborhood learning

vDeepInsight introduces a 3D voxel carrier for tabular data that improves feature neighborhood representation, leading to better performance in gene expression tasks.

Training Strategy Optimization to Mitigate Shortcut Learning in Pan-Cancer Drug Response Prediction

This paper improves pan-cancer drug response prediction by addressing class imbalance and shortcut learning in domain adaptation models, achieving better performance in external patient cohorts.

Dynamic multimodal survival prediction in multiple myeloma integrating gene expression, longitudinal laboratories, and treatment history

This paper develops a dynamic multimodal model for survival prediction in multiple myeloma using gene expression, lab data, and treatment history, achieving higher accuracy than existing methods.

scHDeepInsight: A Hierarchical Deep Learning Framework for Precise Immune Cell Annotation in Single-Cell RNA-seq Data

scHDeepInsight improves immune cell classification in scRNA-seq by using a hierarchical deep learning framework with adaptive loss and image-based features, achieving 93.2% accuracy across seven datasets.

VBayesMM: Variational Bayesian neural network to prioritize important relationships of high-dimensional microbiome multiomics data

VBayesMM improves identification of microbial-metabolite relationships in high-dimensional microbiome data by using a Bayesian neural network with spike-and-slab priors.

Multi-representation DeepInsight: an improvement on tabular data analysis

The paper introduces MRep-DeepInsight, a method that improves tabular data analysis by using multiple representations, showing better accuracy than traditional methods on various datasets.

scDeepInsight: a supervised cell-type identification method for scRNA-seq data with deep learning

scDeepInsight improves cell-type annotation in scRNA-seq by using supervised deep learning, achieving higher accuracy than existing methods.

DeepInsight-3D for precision oncology: an improved anti-cancer drug response prediction from high-dimensional multi-omics data with convolutional neural networks

DeepInsight-3D predicts patient-specific anticancer drug response using multi-omics data and convolutional neural networks, outperforming existing methods.

DeepInsight-FS: Selecting features for non-image data using convolutional neural network

DeepInsight-FS uses a convolutional neural network to select clinically relevant gene subsets from non-image data by converting samples into image-form.

An integrative machine learning approach for prediction of toxicity-related drug safety

This paper develops a machine learning method to predict drug toxicity by integrating drug targets, functional impact scores, and biological networks, showing it can identify toxic drugs and support safe combination design.