Ashley Conard

Senior Researcher

출판물

자료 보기:

- A biologically annotated neural network for proteomic discovery in Parkinsons disease
  
  A. Vijayaraghavan, Lorin Crawford, S. Krishnakant, Ava P. Amini, Ashley Conard, A. Olsen, L. Chahine, Kristen Severson
  
  April 2026
  
  medRxiv | April 2026
  
  DOI
- Tackling the complexity of cancer with generative models
  
  Ashley Conard, Madeline Hughes, Jimmy Hall, Neil Tenenholtz, Eric Zimmermann, Lorin Crawford, Ava P. Amini, Kristen Severson
  
  Cell | April 2026
  
  DOI
- Predicting evolutionary rate as a pretraining task improves genome language model representations
  
  Micaela Elisa Consens, Kevin Kaichuang Yang, Jimmy Hall, Ashley Conard, Bo Wang, Lorin Crawford, Alan M. Moses, Alex Lu
  
  ICML 2026 | February 2026
  
  DOI
- The Evidence Aggregator: AI reasoning applied to rare disease diagnostics
  
  Hope Twede, Lynn Pais, S. Bryen, Emily O’Heir, Greg Smith, Ron Paulsen, C. Austin-Tse, A. Bloemendal, C. Simons, Amanda K. Hall, Scott Saponas, Jeremiah (Miah) Wander, Daniel G. MacArthur, Heidi L. Rehm, Ashley Conard
  
  November 2025
  
  bioRxiv | November 2025
  
  DOI
- Evidence Aggregator: AI reasoning applied to rare disease diagnostics
  
  Hope Twede, Ashley Conard, Lynn Pais, Samantha Brye, Emily O’Heir, Greg Smith, Ron Paulsen, Christina A. Austin-Tse, Alex Bloemendal, Cas Simons, Scott Saponas, Miah Wander, Daniel G. MacArthur, Heidi Rehm
  
  March 2025
  
  Publication
- Addressing biomedical data challenges and opportunities to inform a large-scale data lifecycle for enhanced data sharing, interoperability, analysis, and collaboration across stakeholders
  
  Vivek Sriram, Ashley Conard, Ilyana Rosenberg, Dokyoon Kim, Scott Saponas, Amanda K. Hall
  
  Scientific Reports | February 2025, 제 15 권
  
  DOI Publication Publication
- Multioviz: an interactive platform for in silico perturbation and interrogation of gene regulatory networks
  
  Helen Xie, Lorin Crawford, Ashley Conard
  
  October 2023
  
  BioarXiv | October 2023
  
  DOI Publication

- A biologically annotated neural network for proteomic discovery in Parkinsons disease
  
  A. Vijayaraghavan, Lorin Crawford, S. Krishnakant, Ava P. Amini, Ashley Conard, A. Olsen, L. Chahine, Kristen Severson
  
  April 2026
  
  medRxiv | April 2026
  
  DOI
- Tackling the complexity of cancer with generative models
  
  Ashley Conard, Madeline Hughes, Jimmy Hall, Neil Tenenholtz, Eric Zimmermann, Lorin Crawford, Ava P. Amini, Kristen Severson
  
  Cell | April 2026
  
  DOI
- The Evidence Aggregator: AI reasoning applied to rare disease diagnostics
  
  Hope Twede, Lynn Pais, S. Bryen, Emily O’Heir, Greg Smith, Ron Paulsen, C. Austin-Tse, A. Bloemendal, C. Simons, Amanda K. Hall, Scott Saponas, Jeremiah (Miah) Wander, Daniel G. MacArthur, Heidi L. Rehm, Ashley Conard
  
  November 2025
  
  bioRxiv | November 2025
  
  DOI
- Evidence Aggregator: AI reasoning applied to rare disease diagnostics
  
  Hope Twede, Ashley Conard, Lynn Pais, Samantha Brye, Emily O’Heir, Greg Smith, Ron Paulsen, Christina A. Austin-Tse, Alex Bloemendal, Cas Simons, Scott Saponas, Miah Wander, Daniel G. MacArthur, Heidi Rehm
  
  March 2025
  
  Publication
- Addressing biomedical data challenges and opportunities to inform a large-scale data lifecycle for enhanced data sharing, interoperability, analysis, and collaboration across stakeholders
  
  Vivek Sriram, Ashley Conard, Ilyana Rosenberg, Dokyoon Kim, Scott Saponas, Amanda K. Hall
  
  Scientific Reports | February 2025, 제 15 권
  
  DOI Publication Publication
- Multioviz: an interactive platform for in silico perturbation and interrogation of gene regulatory networks
  
  Helen Xie, Lorin Crawford, Ashley Conard
  
  October 2023
  
  BioarXiv | October 2023
  
  DOI Publication
- A biologically annotated neural network for proteomic discovery in Parkinsons disease
  
  A. Vijayaraghavan, Lorin Crawford, S. Krishnakant, Ava P. Amini, Ashley Conard, A. Olsen, L. Chahine, Kristen Severson
  
  April 2026
  
  medRxiv | April 2026
  
  DOI
- Tackling the complexity of cancer with generative models
  
  Ashley Conard, Madeline Hughes, Jimmy Hall, Neil Tenenholtz, Eric Zimmermann, Lorin Crawford, Ava P. Amini, Kristen Severson
  
  Cell | April 2026
  
  DOI
- Predicting evolutionary rate as a pretraining task improves genome language model representations
  
  Micaela Elisa Consens, Kevin Kaichuang Yang, Jimmy Hall, Ashley Conard, Bo Wang, Lorin Crawford, Alan M. Moses, Alex Lu
  
  ICML 2026 | February 2026
  
  DOI
- Evidence Aggregator: AI reasoning applied to rare disease diagnostics
  
  Hope Twede, Ashley Conard, Lynn Pais, Samantha Brye, Emily O’Heir, Greg Smith, Ron Paulsen, Christina A. Austin-Tse, Alex Bloemendal, Cas Simons, Scott Saponas, Miah Wander, Daniel G. MacArthur, Heidi Rehm
  
  March 2025
  
  Publication
- Addressing biomedical data challenges and opportunities to inform a large-scale data lifecycle for enhanced data sharing, interoperability, analysis, and collaboration across stakeholders
  
  Vivek Sriram, Ashley Conard, Ilyana Rosenberg, Dokyoon Kim, Scott Saponas, Amanda K. Hall
  
  Scientific Reports | February 2025, 제 15 권
  
  DOI Publication Publication
- Addressing biomedical data challenges and opportunities to inform a large-scale data lifecycle for enhanced data sharing, interoperability, analysis, and collaboration across stakeholders
  
  Vivek Sriram, Ashley Conard, Ilyana Rosenberg, Dokyoon Kim, Scott Saponas, Amanda K. Hall
  
  Scientific Reports | February 2025, 제 15 권
  
  DOI Publication Publication

- A biologically annotated neural network for proteomic discovery in Parkinsons disease
  
  A. Vijayaraghavan, Lorin Crawford, S. Krishnakant, Ava P. Amini, Ashley Conard, A. Olsen, L. Chahine, Kristen Severson
  
  April 2026
  
  medRxiv | April 2026
  
  DOI
- The Evidence Aggregator: AI reasoning applied to rare disease diagnostics
  
  Hope Twede, Lynn Pais, S. Bryen, Emily O’Heir, Greg Smith, Ron Paulsen, C. Austin-Tse, A. Bloemendal, C. Simons, Amanda K. Hall, Scott Saponas, Jeremiah (Miah) Wander, Daniel G. MacArthur, Heidi L. Rehm, Ashley Conard
  
  November 2025
  
  bioRxiv | November 2025
  
  DOI
- Multioviz: an interactive platform for in silico perturbation and interrogation of gene regulatory networks
  
  Helen Xie, Lorin Crawford, Ashley Conard
  
  October 2023
  
  BioarXiv | October 2023
  
  DOI Publication
- Predicting evolutionary rate as a pretraining task improves genome language model representations
  
  Micaela Elisa Consens, Kevin Kaichuang Yang, Jimmy Hall, Ashley Conard, Bo Wang, Lorin Crawford, Alan M. Moses, Alex Lu
  
  ICML 2026 | February 2026
  
  DOI
- Tackling the complexity of cancer with generative models
  
  Ashley Conard, Madeline Hughes, Jimmy Hall, Neil Tenenholtz, Eric Zimmermann, Lorin Crawford, Ava P. Amini, Kristen Severson
  
  Cell | April 2026
  
  DOI
- Addressing biomedical data challenges and opportunities to inform a large-scale data lifecycle for enhanced data sharing, interoperability, analysis, and collaboration across stakeholders
  
  Vivek Sriram, Ashley Conard, Ilyana Rosenberg, Dokyoon Kim, Scott Saponas, Amanda K. Hall
  
  Scientific Reports | February 2025, 제 15 권
  
  DOI Publication Publication
- Evidence Aggregator: AI reasoning applied to rare disease diagnostics
  
  Hope Twede, Ashley Conard, Lynn Pais, Samantha Brye, Emily O’Heir, Greg Smith, Ron Paulsen, Christina A. Austin-Tse, Alex Bloemendal, Cas Simons, Scott Saponas, Miah Wander, Daniel G. MacArthur, Heidi Rehm
  
  March 2025
  
  Publication

Ashley Conard

출판물

A biologically annotated neural network for proteomic discovery in Parkinsons disease

Tackling the complexity of cancer with generative models

Predicting evolutionary rate as a pretraining task improves genome language model representations

The Evidence Aggregator: AI reasoning applied to rare disease diagnostics

Evidence Aggregator: AI reasoning applied to rare disease diagnostics

Addressing biomedical data challenges and opportunities to inform a large-scale data lifecycle for enhanced data sharing, interoperability, analysis, and collaboration across stakeholders

Multioviz: an interactive platform for in silico perturbation and interrogation of gene regulatory networks

A biologically annotated neural network for proteomic discovery in Parkinsons disease

Tackling the complexity of cancer with generative models

The Evidence Aggregator: AI reasoning applied to rare disease diagnostics

Evidence Aggregator: AI reasoning applied to rare disease diagnostics

Addressing biomedical data challenges and opportunities to inform a large-scale data lifecycle for enhanced data sharing, interoperability, analysis, and collaboration across stakeholders

Multioviz: an interactive platform for in silico perturbation and interrogation of gene regulatory networks

A biologically annotated neural network for proteomic discovery in Parkinsons disease

Tackling the complexity of cancer with generative models

Predicting evolutionary rate as a pretraining task improves genome language model representations

Evidence Aggregator: AI reasoning applied to rare disease diagnostics

Addressing biomedical data challenges and opportunities to inform a large-scale data lifecycle for enhanced data sharing, interoperability, analysis, and collaboration across stakeholders

Addressing biomedical data challenges and opportunities to inform a large-scale data lifecycle for enhanced data sharing, interoperability, analysis, and collaboration across stakeholders

A biologically annotated neural network for proteomic discovery in Parkinsons disease

The Evidence Aggregator: AI reasoning applied to rare disease diagnostics

Multioviz: an interactive platform for in silico perturbation and interrogation of gene regulatory networks

Predicting evolutionary rate as a pretraining task improves genome language model representations

Tackling the complexity of cancer with generative models

Addressing biomedical data challenges and opportunities to inform a large-scale data lifecycle for enhanced data sharing, interoperability, analysis, and collaboration across stakeholders

Evidence Aggregator: AI reasoning applied to rare disease diagnostics

연락처 Ashley Conard

Microsoft Health Futures

Microsoft Research Lab – New England