Leandro C. Hermida

National Institutes of Health — United States

Medicine · Health Sciences

20h-index7.6kcitations72works51.02yr avg
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Research Topics

Acute Myeloid Leukemia Research(31), Cancer Genomics and Diagnostics(16), Radiomics and Machine Learning in Medical Imaging(11), Pancreatic and Hepatic Oncology Research(8), Protein Degradation and Inhibitors(8)

Publications72 total

Retraction Note: Predicting cancer prognosis and drug response from the tumor microbiome
Nature Communications·2025Open Access
A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics
Nature Cancer·2024· 102 citedOpen Access
Prediction of cancer treatment response from histopathology images through imputed transcriptomics
2023· 5 citedOpen Access
Prediction of cancer treatment response from histopathology images through imputed transcriptomics.
Journal of Clinical Oncology·2023· 6 cited
Abstract 4355: Prediction of cancer treatment response from histopathology images for a broad set of treatments and indications
Cancer Research·2023
Supplemental Figure 4 from Genomic Profiling of Pediatric Acute Myeloid Leukemia Reveals a Changing Mutational Landscape from Disease Diagnosis to Relapse
2023Open Access
Data from Genomic Profiling of Pediatric Acute Myeloid Leukemia Reveals a Changing Mutational Landscape from Disease Diagnosis to Relapse
2023Open Access
Supplemental Table 1 from Genomic Profiling of Pediatric Acute Myeloid Leukemia Reveals a Changing Mutational Landscape from Disease Diagnosis to Relapse
2023Open Access
Supplemental Figure 1 from Genomic Profiling of Pediatric Acute Myeloid Leukemia Reveals a Changing Mutational Landscape from Disease Diagnosis to Relapse
2023Open Access
Supplemental Table 2 from Genomic Profiling of Pediatric Acute Myeloid Leukemia Reveals a Changing Mutational Landscape from Disease Diagnosis to Relapse
2023Open Access
Supplemental Figure 4 from Genomic Profiling of Pediatric Acute Myeloid Leukemia Reveals a Changing Mutational Landscape from Disease Diagnosis to Relapse
2023Open Access
Supplemental Figure 1 from Genomic Profiling of Pediatric Acute Myeloid Leukemia Reveals a Changing Mutational Landscape from Disease Diagnosis to Relapse
2023Open Access
Supplemental Figure 3 from Genomic Profiling of Pediatric Acute Myeloid Leukemia Reveals a Changing Mutational Landscape from Disease Diagnosis to Relapse
2023Open Access
Supplemental Table 3 from Genomic Profiling of Pediatric Acute Myeloid Leukemia Reveals a Changing Mutational Landscape from Disease Diagnosis to Relapse
2023Open Access
Data from Genomic Profiling of Pediatric Acute Myeloid Leukemia Reveals a Changing Mutational Landscape from Disease Diagnosis to Relapse
2023Open Access
Supplemental Table 2 from Genomic Profiling of Pediatric Acute Myeloid Leukemia Reveals a Changing Mutational Landscape from Disease Diagnosis to Relapse
2023Open Access
Supplemental Figure 2 from Genomic Profiling of Pediatric Acute Myeloid Leukemia Reveals a Changing Mutational Landscape from Disease Diagnosis to Relapse
2023Open Access
Supplemental Figure 2 from Genomic Profiling of Pediatric Acute Myeloid Leukemia Reveals a Changing Mutational Landscape from Disease Diagnosis to Relapse
2023Open Access
Supplemental Figure 3 from Genomic Profiling of Pediatric Acute Myeloid Leukemia Reveals a Changing Mutational Landscape from Disease Diagnosis to Relapse
2023Open Access
Supplemental Table 1 from Genomic Profiling of Pediatric Acute Myeloid Leukemia Reveals a Changing Mutational Landscape from Disease Diagnosis to Relapse
2023Open Access
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Frequent Co-authors

Daniela S. Gerhard(22), Tanja M. Davidsen(20), Malcolm A. Smith(19), Patee Gesuwan(19), Soheil Meshinchi(18), Rhonda E. Ries(17), Jaime M. Guidry Auvil(16), Jason E. Farrar(16), Todd A. Alonzo(16), Alan S. Gamis(16), Marco A. Marra(14), Yussanne Ma(13), Andrew J. Mungall(13), Eytan Ruppin(11), E. Anders Kolb(9), Richard A. Moore(8), Oliver Hampton(7), Michael Primig(7), Heather L. Schuback(6), Daniel Wai(6)