#sysbio4cancerresearch search results

Thank you all! I really learned a lot from you about exciting progress and opportunities in #CancerResearch using #SystemsBiology! #SysBio4CancerResearch

A9: As a science communicator, I would be interested in conversations about ways to improve outreach to the public about the importance of #CancerResearch using systems-level approaches. #SysBio4CancerResearch

A9: I think it would be great to have more discussions/conversations involving patient advocates (like @christeeny513) and cancer researchers using #SystemsBiology approaches to help build collaborations with multiple perspectives. #SysBio4CancerResearch

A9: I would be interested in discussions related to getting the next generation of scientists (e.g., middle school, high school, and undergraduate students) interested in #CancerResearch using #SystemsBiology. #SysBio4CancerResearch

Thank you for the opportunity to be part of the panel and join these insightful discussions! It was great to share and hear views on advancing cancer #SystemsBiology research. Looking forward to future conversations! #SysBio4CancerResearch

I totally agree! Everyone is talking about #AI! It’s everywhere and the technology is rapidly progressing. As you said, in #CancerResearch it’s also important to think about interpretable AI. cancer.gov/research/resou… #SysBio4CancerResearch #AI4CancerResearch @NCIDataSci

A9: I am just starting to understand how important it is to engage patient advocates in #CancerResearch. I would love to see more conversations with patients, where we learn from each other and get inspired about impactful #SystemsBiology work. #SysBio4CancerResearch

#SysBio4CancerResearch: Another important future direction is the integration of AI/ML approaches with mechanistic modeling. Combining highly predictive black-box approaches with parameterized math models to gain mechanistic insight into that black-box:…

A8: I also think there may be opportunities for more translational #SystemsBiology initiatives like @ARPA_H ADAPT, which aims to harness advanced technologies and a deep understanding of tumor biology to build cancer #biomarkers. arpa-h.gov/research-and-f… #SysBio4CancerResearch

#SysBio4CancerResearch: Mathematical modeling has made a significant recent impact in the design of treatment scheduling protocols that are evolution-based strategies to mitigate the evolution of resistance. But there is still many unanswered questions, and #MathOnco or…

A8: Future #CancerResearch directions: Integrative Single-cell Spatial Multi-Omics for a comprehensive view of cancer biology; AI & ML to analyze complex datasets and predict outcomes; Promote data sharing & collaboration; Systematic genetic perturbations. #SysBio4CancerResearch

A7: modeling of adaptive therapy from Bob Gatenby @MoffittNews has already led to clinical trials showing good outcomes (doi.org/10.7554/eLife.…) #SysBio4CancerResearch 2/2

A7: tumors are constantly evolving and adapting to their environment. it is exciting to see #SystemsBiology models that exploit that evolution, leading to adaptive therapy. #SysBio4CancerResearch 1/2

A7: Dr. Forest White shares preclinical #SystemsBiology approaches that revealed potential new therapeutic strategies for incurable cancers in a @MIT seminar: youtu.be/81Rp5tIU8Ms?si… #SysBio4CancerResearch

#SysBio4CancerResearch: The best way to bring preclinical data to bear on clinical translation is the integration of mathematical models to interpolate or extrapolate these data, and to test & generate hypotheses about underlying biological mechanisms. A great example of this…

A7: In a @CD_AACR study, Mundi et al. developed tumor-agnostic #SystemsBiology tools (i.e., OncoTarget and OncoTreat) to predict drug responses, which are scalable for the design of #ClinicalTrials.aacrjournals.org/cancerdiscover… #SysBio4CancerResearch

#SysBio4CancerResearch: The best way to bring preclinical data to bear on clinical translation is the integration of mathematical models to interpolate or extrapolate these data, and to test & generate hypotheses about underlying biological mechanisms. A recent great example of…

A7: @xubin_li, @anilkorkut, et al. developed REFLECT (a predictive #CombinationTherapy #bioinformatics platform) & found that REFLECT-selected treatments showed improvements in efficacy & survival in preclinical & clinical settings. aacrjournals.org/cancerdiscover… #SysBio4CancerResearch

On new technologies/approaches in #SysBio4CancerResearch: Mistic: an open-source multiplexed image t-SNE viewer created by @sandhya212 t-SNE or UMAP is a nice way to visualize relationships in high dimensional data in a reduced dimension diagram, but you lose the visual,…

On new technologies/approaches in #SysBio4CancerResearch: A neat example of how @S_Marzban incorporated VALIS into our workflow for Lenia-based spatial models to determine the effect of ECM on immune infiltration (joint work w/ @AmelioLab) Our preprint is here:…

📌 Please join one our chat on #SystemsBiology and cancer research on July 18 at 7 pm ET or on July 23 at 8 pm ET. Use #SysBio4CancerResearch to follow a discussion of recent advances and opportunities to use systems-level approaches in #CancerResearch. @NCIsysbio

#SysBio4CancerResearch: Mathematical modeling has made a significant recent impact in the design of treatment scheduling protocols that are evolution-based strategies to mitigate the evolution of resistance. But there is still many unanswered questions, and #MathOnco or…

#SysBio4CancerResearch: The best way to bring preclinical data to bear on clinical translation is the integration of mathematical models to interpolate or extrapolate these data, and to test & generate hypotheses about underlying biological mechanisms. A recent great example of…

Take our recent review in Annals of Oncology for example, showing that mathematics was involved in major advances in treatment scheduling every single decade since 1960’s! #SysBio4CancerResearch 2/2 annalsofoncology.org/article/S0923-… #OpenAccess

#SysBio4CancerResearch: Another important future direction is the integration of AI/ML approaches with mechanistic modeling. Combining highly predictive black-box approaches with parameterized math models to gain mechanistic insight into that black-box:…

On the question of new technologies #SysBio4CancerResearch, I’d like to point out a few software packages we’ve been using to analyze spatial data: First, VALIS (Virtual Alignment of pathoLogy Image Series), published by @cgatenbee has been invaluable in our workflow, aligning…

I am most excited about Systems Bio approaches to better understand cancer as an evolutionary & ecological process. Math modeling is key to driving advances in treatment scheduling in this complex, multiscale disease. #SysBio4CancerResearch 1/2

On new technologies/approaches in #SysBio4CancerResearch: Mistic: an open-source multiplexed image t-SNE viewer created by @sandhya212 t-SNE or UMAP is a nice way to visualize relationships in high dimensional data in a reduced dimension diagram, but you lose the visual,…