Prediction of cancer treatment response from histopathology images through imputed transcriptomics

Deep learning for drug response prediction in cancer

Baptista, Delora
Ferreira, Pedro G.
Rocha, Miguel

Predicting the sensitivity of tumors to specific anti-cancer treatments is a challenge of paramount ...

Deep learning approaches to predict drug responses in cancer using a multi-omics approach

Lyu, Xintong

January 2020

Cancers are genetically heterogeneous, and therefore the same anti-cancer drug may have varying degr...

Computational models for clinical drug response prediction: aligning transcriptomic data of patients and pre-clinical models

Mourragui, S.M.C.

February 2023

Extensive efforts in cancer research over the past decades have markedly improved diagnosis and trea...

Prediction of cancer treatment response from histopathology images through imputed transcriptomics

Hoang, Danh-Tai
Dinstag, Gal
Hermida, C. Leandro
Ben-Zvi, S. Doreen
Elis, Efrat
Caley, Katherine
Sammut, Stephen-John
Sinha, Sanju
Sinha, Neelam
Dampier, H. Christopher
Stossel, Chani
Patil, Tejas
Rajan, Arun
Lassoued, Wiem
Strauss, Julius
Bailey, Shania
Allen, Clint
Redman, Jason
Beker, Tuvik
Jiang, Peng
Golan, Talia
Wilkinson, Scott
Sowalsky, G. Adam
Pine, R. Sharon
Caldas, Carlos
Gulley, L. James
Aldape, Kenneth
Aharonov, Ranit
Stone, A. Eric
Ruppin, Eytan

August 2023

DeepPT codes in the manuscript "Prediction of cancer treatment response from histopathology images t...

Deep Learning of Histopathological Features for the Prediction of Tumour Molecular Genetics

Pierre Murchan
Cathal Ó’Brien
Shane O’Connell
Ciara S. McNevin
Anne-Marie Baird
Orla Sheils
Pilib Ó Broin
Stephen P. Finn

August 2021

Advanced diagnostics are enabling cancer treatments to become increasingly tailored to the individua...

Cancer prediction using RNA sequencing and deep learning

Dubey, Alok
Mujoo, Sheetal
Hannan, Shaikh Abdul
Satpathy, Gargibala
Arshad, Muhammad Waqas
Manikandan, E.

October 2022

Numerous areas of medical services, including as imaging diagnostics, advanced pathology, emergency ...

Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors

Sanju Sinha

April 2022

Data required to reproduce the results/figures of the "Predicting patient treatment response and res...

A deep learning model to predict RNA-Seq expression of tumours from whole slide images

Benoît Schmauch
Alberto Romagnoni
Elodie Pronier
Charlie Saillard
Pascale Maillé
Julien Calderaro
Aurélie Kamoun
Meriem Sefta
Sylvain Toldo
Mikhail Zaslavskiy
Thomas Clozel
Matahi Moarii
Pierre Courtiol
Gilles Wainrib

August 2020

RNA-sequencing of tumour tissue can provide important diagnostic and prognostic information but this...

Prediction of the outcome of preoperative chemotherapy in breast cancer using DNA probes that provide information on both complete and incomplete responses-0

René Natowicz (78755)
Roberto Incitti (78756)
Euler Guimarães Horta (78757)
Benoît Charles (78758)
Philippe Guinot (78759)
Kai Yan (78760)
Charles Coutant (78761)
Fabrice Andre (78762)
Lajos Pusztai (78763)
Roman Rouzier (78764)

December 2011

Copyright information:Taken from "Prediction of the outcome of preoperative chemoth...

Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review

Heather D. Couture

December 2022

Molecular and genomic properties are critical in selecting cancer treatments to target individual tu...

Predicting molecular phenotypes from histopathology images : A transcriptome-wide expression–morphology analysis in breast cancer

Wang, Yinxi
Kartasalo, Kimmo
Weitz, Philippe
Acs, Balazs
Valkonen, Masi
Larsson, Christer
Ruusuvuori, Pekka
Hartman, Johan
Rantalainen, Mattias

January 2021

Molecular profiling is central in cancer precision medicine but remains costly and is based on tumor...

Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions

Ahsan Bin Tufail
Yong-Kui Ma
Mohammed K. A. Kaabar
Francisco Martínez
A. R. Junejo
Inam Ullah
Rahim Khan

January 2021

Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied...

Deep Learning Prediction of Pathologic Complete Response in Breast Cancer Using MRI and Other Clinical Data: A Systematic Review

Nabeeha Khan
Richard Adam
Pauline Huang
Takouhie Maldjian
Tim Q. Duong

November 2022

Breast cancer patients who have pathological complete response (pCR) to neoadjuvant chemotherapy (NA...

Deep learning in cancer diagnosis, prognosis and treatment selection

Tran, Khoa A.
Kondrashova, Olga
Bradley, Andrew
Williams, Elizabeth D.
Pearson, John V.
Waddell, Nicola

September 2021

Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique c...

Deep learning in cancer diagnosis, prognosis and treatment selection.

Tran, KA
Kondrashova, O
Bradley, A
Williams, ED
Pearson, JV
Waddell, N

September 2021

Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique c...

Deep learning for drug response prediction in cancer

Baptista, Delora
Ferreira, Pedro G.
Rocha, Miguel

Predicting the sensitivity of tumors to specific anti-cancer treatments is a challenge of paramount ...

Deep learning approaches to predict drug responses in cancer using a multi-omics approach

Lyu, Xintong

January 2020

Cancers are genetically heterogeneous, and therefore the same anti-cancer drug may have varying degr...

Computational models for clinical drug response prediction: aligning transcriptomic data of patients and pre-clinical models

Mourragui, S.M.C.

February 2023

Extensive efforts in cancer research over the past decades have markedly improved diagnosis and trea...

Prediction of cancer treatment response from histopathology images through imputed transcriptomics

Hoang, Danh-Tai
Dinstag, Gal
Hermida, C. Leandro
Ben-Zvi, S. Doreen
Elis, Efrat
Caley, Katherine
Sammut, Stephen-John
Sinha, Sanju
Sinha, Neelam
Dampier, H. Christopher
Stossel, Chani
Patil, Tejas
Rajan, Arun
Lassoued, Wiem
Strauss, Julius
Bailey, Shania
Allen, Clint
Redman, Jason
Beker, Tuvik
Jiang, Peng
Golan, Talia
Wilkinson, Scott
Sowalsky, G. Adam
Pine, R. Sharon
Caldas, Carlos
Gulley, L. James
Aldape, Kenneth
Aharonov, Ranit
Stone, A. Eric
Ruppin, Eytan

August 2023

DeepPT codes in the manuscript "Prediction of cancer treatment response from histopathology images t...

Deep Learning of Histopathological Features for the Prediction of Tumour Molecular Genetics

Pierre Murchan
Cathal Ó’Brien
Shane O’Connell
Ciara S. McNevin
Anne-Marie Baird
Orla Sheils
Pilib Ó Broin
Stephen P. Finn

August 2021

Advanced diagnostics are enabling cancer treatments to become increasingly tailored to the individua...

Cancer prediction using RNA sequencing and deep learning

Dubey, Alok
Mujoo, Sheetal
Hannan, Shaikh Abdul
Satpathy, Gargibala
Arshad, Muhammad Waqas
Manikandan, E.

October 2022

Numerous areas of medical services, including as imaging diagnostics, advanced pathology, emergency ...

Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors

Sanju Sinha

April 2022

Data required to reproduce the results/figures of the "Predicting patient treatment response and res...

A deep learning model to predict RNA-Seq expression of tumours from whole slide images

Benoît Schmauch
Alberto Romagnoni
Elodie Pronier
Charlie Saillard
Pascale Maillé
Julien Calderaro
Aurélie Kamoun
Meriem Sefta
Sylvain Toldo
Mikhail Zaslavskiy
Thomas Clozel
Matahi Moarii
Pierre Courtiol
Gilles Wainrib

August 2020

RNA-sequencing of tumour tissue can provide important diagnostic and prognostic information but this...

Prediction of the outcome of preoperative chemotherapy in breast cancer using DNA probes that provide information on both complete and incomplete responses-0

René Natowicz (78755)
Roberto Incitti (78756)
Euler Guimarães Horta (78757)
Benoît Charles (78758)
Philippe Guinot (78759)
Kai Yan (78760)
Charles Coutant (78761)
Fabrice Andre (78762)
Lajos Pusztai (78763)
Roman Rouzier (78764)

December 2011

Copyright information:Taken from "Prediction of the outcome of preoperative chemoth...

Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review

Heather D. Couture

December 2022

Molecular and genomic properties are critical in selecting cancer treatments to target individual tu...

Predicting molecular phenotypes from histopathology images : A transcriptome-wide expression–morphology analysis in breast cancer

Wang, Yinxi
Kartasalo, Kimmo
Weitz, Philippe
Acs, Balazs
Valkonen, Masi
Larsson, Christer
Ruusuvuori, Pekka
Hartman, Johan
Rantalainen, Mattias

January 2021

Molecular profiling is central in cancer precision medicine but remains costly and is based on tumor...

Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions

Ahsan Bin Tufail
Yong-Kui Ma
Mohammed K. A. Kaabar
Francisco Martínez
A. R. Junejo
Inam Ullah
Rahim Khan

January 2021

Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied...

Deep Learning Prediction of Pathologic Complete Response in Breast Cancer Using MRI and Other Clinical Data: A Systematic Review

Nabeeha Khan
Richard Adam
Pauline Huang
Takouhie Maldjian
Tim Q. Duong

November 2022

Breast cancer patients who have pathological complete response (pCR) to neoadjuvant chemotherapy (NA...

Deep learning in cancer diagnosis, prognosis and treatment selection

Tran, Khoa A.
Kondrashova, Olga
Bradley, Andrew
Williams, Elizabeth D.
Pearson, John V.
Waddell, Nicola

September 2021

Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique c...

Deep learning in cancer diagnosis, prognosis and treatment selection.

Tran, KA
Kondrashova, O
Bradley, A
Williams, ED
Pearson, JV
Waddell, N

September 2021

Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique c...

Deep learning for drug response prediction in cancer

Baptista, Delora
Ferreira, Pedro G.
Rocha, Miguel

Predicting the sensitivity of tumors to specific anti-cancer treatments is a challenge of paramount ...

Deep learning approaches to predict drug responses in cancer using a multi-omics approach

Lyu, Xintong

January 2020

Cancers are genetically heterogeneous, and therefore the same anti-cancer drug may have varying degr...

Computational models for clinical drug response prediction: aligning transcriptomic data of patients and pre-clinical models

Mourragui, S.M.C.

February 2023

Extensive efforts in cancer research over the past decades have markedly improved diagnosis and trea...

Prediction of cancer treatment response from histopathology images through imputed transcriptomics

Abstract

Extracted data

Prediction of cancer treatment response from histopathology images through imputed transcriptomics

Abstract

Extracted data

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