Johan Lindberg

Karolinska University Hospital

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Articles (13)

Collaborators (7)

Martin Eklund

Associate professor

Karolinska Institutet

Jørgen Bjerggaard Jensen

Aarhus University

Jacob Fredsøe

Aarhus University

Karina Dalsgaard Sørensen

Aarhus University

Kimmo Kartasalo

Karolinska Institutet

Gabriel Renaud

Technical University of Denmark

Søren Vang

DVIP-external, Head of Clinical Bioinformatics

Aarhus University

Johan Lindberg

Research Interests

Explore related searches

Contact this professor

Articles (13)

Collaborators (7)

Martin Eklund

Jørgen Bjerggaard Jensen

Jacob Fredsøe

Karina Dalsgaard Sørensen

Kimmo Kartasalo

Gabriel Renaud

Søren Vang

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Johan Lindberg

Research Interests

Explore related searches

Contact this professor

Articles (13)

Comprehensive Genomic Profiling Alters Clinical Diagnoses in a Significant Fraction of Tumors Suspicious of Sarcoma.

Prediction of plasma ctDNA fraction and prognostic implications of liquid biopsy in advanced prostate cancer.

Prognostic Value of Low-Pass Whole Genome Sequencing of Circulating Tumor DNA in Metastatic Castration-Resistant Prostate Cancer

<i>AR</i> and <i>PI3K</i> Genomic Profiling of Cell-free DNA Can Identify Poor Responders to Lutetium-177-PSMA Among Patients with Metastatic Castration-resistant Prostate Cancer.

Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression-based convolutional neural networks

Ductal and acinar components of mixed prostatic adenocarcinoma frequently have a common clonal origin

Circulating tumor DNA (ctDNA) in adjuvant therapy of early stage colon cancer: current status and future perspectives

Clinical Trial Protocol for ProBio: An Outcome-adaptive and Randomised Multiarm Biomarker-driven Study in Patients with Metastatic Prostate Cancer

ESMO recommendations on the use of circulating tumour DNA assays for patients with cancer: a report from the ESMO Precision Medicine Working Group

Fusion Gene Detection Using Whole-Exome Sequencing Data in Cancer Patients

Unsupervised detection of fragment length signatures of circulating tumor DNA using non-negative matrix factorization

Using deep learning to detect patients at risk for prostate cancer despite benign biopsies

Artificial Intelligence for Diagnosis and Gleason Grading of Prostate Cancer in Biopsies-Current Status and Next Steps

Collaborators (7)

Martin Eklund

Jørgen Bjerggaard Jensen

Jacob Fredsøe

Karina Dalsgaard Sørensen

Kimmo Kartasalo

Gabriel Renaud

Søren Vang

How do I reach out?

Meet Kite AI