Esa Pitkänen

This paper introduces Brieno, a self-supervised model for raw nanopore signals that improves read classification and methylation detection without reference genomes.

petVAE identifies biologically meaningful subgroups along the Alzheimer's disease continuum using a deep learning model trained on amyloid PET scans.

The paper introduces muat, a transformer-based tool for tumour classification using somatic variants, achieving high accuracy across different cohorts without retraining.

MuAt2, a Transformer model, classifies tumour types and subtypes from somatic variants, improving accuracy and calibration across cancer types and cohorts.

The paper shows classical enhancers influence 3D genome structure by linking DNA sequence to transcriptional condensates and chromatin organization through specific transcription factors.

This study develops IonStats, a toolkit for profiling DNA adducts using nanopore sequencing, revealing compound-specific effects on sequencing data.

The paper describes a pipeline for collecting high-quality blood samples from donors for multi-omics research, demonstrating their suitability for genetic and phenotypic studies.

The paper compares whole exome sequencing and single-cell DNA sequencing in AML to assess clonal heterogeneity and evolution, finding both methods have strengths and limitations in variant detection and clonal analysis.

The paper evaluates germline variants in hematological malignancies using a computational pipeline, identifying pathogenic alleles and enriched variants in CHEK2 and other genes.

The paper develops a machine learning model to identify patient-specific multi-targeting therapies using single-cell transcriptomic data for hematological cancers and solid tumors.

The paper develops Blood Cell Painting (BCP) to profile immune cell morphology in healthy donors, identifying genetic and clinical associations with immune cell features.

ArcheD predicts cerebrospinal fluid amyloid-beta levels from amyloid PET images with strong correlation, showing distinct brain region contributions across AD stages.

The paper demonstrates the feasibility of conducting clinical recall studies from the FinnGen biobank to collect customized clinical data from participants with Alzheimer's disease or mild cognitive disorder.

The paper introduces MuAt, a deep learning model that learns representations of somatic mutations to predict tumour types and subtypes with high accuracy.

DeepIFC uses deep learning to generate fluorescent marker images from imaging flow cytometry data, enabling accurate cell typing and recognition of unseen cell types.

The paper compares rule-based and data-driven molecular representations for predicting drug combination effects using standardized screening data from 112 cancer cell lines.

This study identifies retrotransposon insertions in colorectal tumors, linking them to cancer initiation and poor patient survival.

BasePlayer is a software tool for analyzing large-scale NGS data, enabling variant analysis, visualization, and integration of genomic data.

The paper enhances variation detection by using pan-genome indexing to create a recombined reference sequence from multiple alignments, improving detection on real datasets.