Tweets, Pictures and Retweets at 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, MIT by @pharma_BI and @AVIVA1950 for #KIsymposium PharmaceuticalIntelligence.com and Social Media
Pictures taken in Real Time
Notification from Twitter.com on June 14, 2019 and in the 24 hours following the symposium
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It was an incredibly touching and “metzamrer” surprise to meet you at MIT
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Amazing event
#KIsymposium@MIT @avivregev@dana_peer @reginabarzilay 2pharma_BI@AVIVA1950#ML#Cancer Breakthrough in#Computation#Biology#Single#Cell#GenomicsTweets, Pictures and Retweets at 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, MIT by
@pharma_BI and@AVIVA1950 for#KIsymposium http://PharmaceuticalIntelligence.com and Social Media http://pharmaceuticalintelligence.com/2019/06/16/tweets-pictures-and-retweets-at-18th-annual-cancer-research-symposium-machine-learning-and-cancer-june-14-2019-mit-by-pharma_bi-and-aviva1950-for-kisymposium-pharmaceuticalintelligenc/ …
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#Verily‘s machine learning tool characterizes proteins, which are biomarkers of disease development and progression. Scientists can know more about their relationship to specific diseases and can interview earlier and precisely. https://tinyurl.com/y52aak8z@pharma_BI,@AVIVA1950 -
#Maching learning and#cancer are undergoing dramatic changes and hold great promise for cancer research, diagnostics, and therapeutics. @KIinstitute@MIT by@pharma_BI@AVIVA1950 -
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#variant identification in the#clinic will depend on highly#curated#databases -
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Tweets by @pharma_BI and by @AVIVA1950
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Retweets and replies by @pharma_BI and @AVIVA1950
eProceedings 18th Symposium 2019 #ML #AI #Cancer #KISymposium @MIT covered in #realTime @pharma_BI @AVIVA1950 Amazing event, #Frontier #Science #oncology #cell #biology Keynote @JimAllison best talks @avivregev @danape’er @olga @princeton @reginabarzelay
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Top lectures by @reginabarzilay
@MIT @avivaregev@MIT@Dana_peer@sloan_kettering@OlgaTroyanskaya@Princeton@AndrewBeck@Path_AI -
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eProceeding 2019 Koch Institute Symposium – 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PMET MIT Kresge Auditorium, 48 Massachusetts Ave, Cambridge, MA https://pharmaceuticalintelligence.com/2019/03/12/2019-koch-institute-symposium-machine-learning-and-cancer-june-14-2019-800-am-500-pmet-mit-kresge-auditorium-48-massachusetts-ave-cambridge-ma/ … via
@Pharma_BI@AVIVA1950#KISymposium -
eProceedings
#Cancer &#AI#ML@MIT eProceeding 2019 Koch Institute Symposium – 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PMET MIT Kresge Auditorium, 48 Massachusetts Ave, Cambridge, MA https://pharmaceuticalintelligence.com/2019/03/12/2019-koch-institute-symposium-machine-learning-and-cancer-june-14-2019-800-am-500-pmet-mit-kresge-auditorium-48-massachusetts-ave-cambridge-ma/ … via@Pharma_BIFeaturing
#Computational and#Systems#Biology Program at#Memorial#Sloan#Kettering#Cancer#Center,#Sloan#Kettering#Institute (SKI), The@DanaPe‘er#Lab https://pharmaceuticalintelligence.com/2019/06/16/featuring-computational-and-systems-biology-program-at-memorial-sloan-kettering-cancer-center-sloan-kettering-institute-ski-the-dana-peer-lab/ … via@Pharma_BI@AVIVA1950
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You Retweeted
eProceedings 18th Symposium 2019
#ML#AI#Cancer#KISymposium@MIT covered in#realTime@pharma_BI@AVIVA1950 Amazing event,#Frontier#Science#oncology#cell#biology Keynote@JimAllison best talks @avivregev@danape’er@olga@princeton @reginabarzelay -
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Top lectures by @reginabarzilay
@MIT @avivaregev@MIT@Danapeer@sloan_kettering@OlgaTroyanskaya@Princeton@AndrewBeck@Path_AI -
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eProceeding 2019 Koch Institute Symposium – 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PMET MIT Kresge Auditorium, 48 Massachusetts Ave, Cambridge, MA https://pharmaceuticalintelligence.com/2019/03/12/2019-koch-institute-symposium-machine-learning-and-cancer-june-14-2019-800-am-500-pmet-mit-kresge-auditorium-48-massachusetts-ave-cambridge-ma/ … via
@Pharma_BI@AVIVA1950#KISymposium -
eProceedings
#Cancer &#AI#ML@MIT eProceeding 2019 Koch Institute Symposium – 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PMET MIT Kresge Auditorium, 48 Massachusetts Ave, Cambridge, MA https://pharmaceuticalintelligence.com/2019/03/12/2019-koch-institute-symposium-machine-learning-and-cancer-june-14-2019-800-am-500-pmet-mit-kresge-auditorium-48-massachusetts-ave-cambridge-ma/ … via@Pharma_BI -
You Retweeted
#voices of#Patients is key for continual improvement of#HealthCare#services. Our#ebook Voices of Patients@Amazon@bernstein_h@GailThornt@AVIVA1950@Pharma_BI#Authors and#Editors of#MONUMENTAL#volume#hospital#CEO TALKING#Patients#experiences told
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You Retweeted
Einstein, Curie, Bohr, Planck, Heisenberg, Schrödinger… was this the greatest meeting of minds, ever? Some of the world’s most notable physicists participated in the 1927 Solvay Conference. In fact, 17 of the 29 scientists attending were or became
#NobelPrize Laureates.
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#variant identification in the#clinic will depend on highly#curated#databases -
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eProceeding 2019 Koch Institute Symposium – 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PMET MIT Kresge Auditorium, Cambridge, MA https://pharmaceuticalintelligence.com/2019/03/12/2019-koch-institute-symposium-machine-learning-and-cancer-june-14-2019-800-am-500-pmet-mit-kresge-auditorium-48-massachusetts-ave-cambridge-ma/ … via
@Pharma_BI#KIsymposium@AVIVA1950@kochinstitute -
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#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@StephenFriend @UniversityOxford wearables to forecast symptom transitions Pattern recognition for prognosis NEJM 20 years took since Breast cancer started to benefit from pattern recognition Participants-Centered 10M smart watchs -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@StephenFriend @UniversityOxford Health Assessment – signals to Symptoms Deep Learning – multiple stages of non-linear feature transformation Vector Institute – data analysis was done [Pregnancy] Forecasting – binary labels -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute @BrianWolpin@DanaFarber longitudinal Cohorts: Risk Factors Diabetes related to pancreatis cancer risk for weight and Diabetes Medication: Start/Stopped: NSAIDs Tylenol, H2 Blocker PPI, anti- HTN, ML – altered systemic metabolism -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute @BrianWolpin@DanaFarber Early detection of pancreatic cancer ML for Pancretic Cancer Early detection Metabolic alterations: Muscle wasting, subcutaneous adipose Automated Peripheral Tissue Segmentation 30,000 scans: changes -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@AndrewBeck@PathAI AI – deep learning is driving major advances in computer vision Reduce errors training set vs validation set computer vision vs Human eye – machine learning is BETTER -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@AndrewBeck@PathAI AI for pathology AI-powered companion diagnostics Pathologic diagnosis informs all subsequent medical decisions Discordance among pathologists is common in interpretation of breast biopsy skin biopsy melanoma -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@TommiJaakkola@MIT @CS_AIL Machine learning for drug design Automation arises from data How much data is enough? assey size vs all spectrum MIT Consorsium on pharmaceutical discovery – 13 Pharma, chemistry, chem eng, CS -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@TommiJaakkola@MIT @CS_AIL multi-resolution representations substructures with precision attachment – what atoms are underlined predicting molecular properties programmatic optimization – machine translation problem in analogy -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute Clifford Hudis American Society of Clinical Oncology (ASCO) 75% of communication is by FAX still White cell data count – convert data structure on EMR -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute David Schenkein GOOGLE Venture (GV) Regeneron, Geisinger, Amgen – Big data repositories are built Clinical Trials and big data delivery side: AI: Pathology and Radiology, early diagnostics: AI in MDs Offices to record the entire -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@ConstanceLehman@MGHCancerCenter Screening mammorgam is runned via the algorithm, report generated, Radiologist accept or reject the modle presented more domains need AI and specialties of MedicineDevelopment and implementation -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute @AineHanly VP Amgen 17% improvement in survival rate in 50 years Data infrastructure investment paid off Partnerships, interoperability in EMR -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@JayBradner@Novartis AI is a Tool in all phases of drug discovery integrated analysis of epigenetics strategy for investment in 400 Scientists, big data monitoring of Clinical Trial around the World generative chemistry -
You Retweeted
Just blown away by the progress in AI/ML in cancer cell biology, en route to medicines, here at the Annual Koch Symposium at MIT. If you like MIBI (as I do), just wait till you see spatial transcriptomics.
@KochInstitute#KISymposium -
You Retweeted
For the shape of things to come,
@sloan_kettering’s@dana_peer on “Manifolds underlying plasticity in development, regeneration and cancer.”#KIsymposium
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute @PeterSorger@Harvardmed Highly multiplexed highresolution image of FFPE specimens Assaying the effects of multiple possible Cancer Precision Medicine per NCI Precision medicine based on therapeutic response at single cell level -
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#KIsymposium@pharma_BI@AVIVA1950@kochinstitute @PeterSorger@harvardmed Redigitalization of 300 clinical trials combination immunotherapy: Assumption each patient benefits from both = synergy vs the assumption of NO interaction: each Patient benefit from ONE or the Other -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute @PeterSorger@harvardmed@Novartis PDX models Gastric Cancer PDXs Three way combination drug therapy – a deeper look at RAF and MEK inhibitors in BRAF mutant melanoma -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@Dana_Peer@MSK Pancreatic Ductal Adenocarcinoma – ductal metaplagiea Immune ecosystem reprogrammed Leptomeningeal niche — iron binding – adopt environment and prevent iron from the immune systemTumor plasticity – Mutation Kras -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@Princeton specific Interpreting a genome: predicting the effect of any mutation: coding variant vs non-coding variance – regulatory code, transcription marks, DeepSEA – model learns from one genome interactions of biochemical -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@OlgaTroyanskaya@Princeton Interpreting a Genime: predicting the effect of any mutation: Noncoding variant effect in ASD from WGS dataDenovo mutations: How impactful are these mutations in AUtism patients – to % not explained by -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@OlgaTroyanskaya@Princeton Decoding cancer genomes with deep learning genome to phynotype – BIG genomics data to understand human disease exosome non coding region gene regulation affected by genome for gene expression sequence -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@AngeloLab Single cell: imaging data trained by nuclear interior ( nuclei only, , segmentation channel using DeepCell v1.0 TB granulomas, Cell type identification with deep learning – No downstream 3D Segmentation Melanoma -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute Heiden Michael Angelo@Stanford immune cells in solid tumors using Multiplexed ion beam imaging (MIBI) Tissue imaging – high dimensional, multiplexing: high sensitivity, dynamic range, GI Tissue selection – immuno-florescence -
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You Retweeted
Incredible overview of work in Aviv Regev’s lab developing
#machinelearning approaches to make causal inferences in#cancer that can be functionally validated@kochinstitute#KIsymposium
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#KIsymposium@pharma_BI@AVIVA1950@kochinstitute @reginabarzilay Myth #1: DEMOCRATIZATION OF AI – deep learning and customization Myth #2: Bias – Deep Learning allows across population studies Myth #3: Black-Box Architecture 0@MIT Interpretable models J-Clinic@MITML and Health -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute @ReginaBarzilay MIT How machine learning changes cancer research ML & Clinical Oncology Data Science perspective – Raw Data to Cure: Extract information NLP – penetration of deepLearning@MGH @MITDrug discovery – New molecules -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute @avivregev Insi2Vec: Define a cell by intrinsic and extrinsic and spatial features: generalize across patient samples in melanoma Identifying key patio-molecular archetypes Expanding full Transcriptome toolbox for spatial genome -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute @avivregev Synovial sarcome has anti-tumor activity in its immune environment Melanoma vs Synovial sarcome (SS18-SSX fusion directly controls core oncogenic program Target the core ongogenic program therapeutically IFN CD8 TNF -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@jimallison FDA Approved CTLA-4 and Anti-PD-1 for a dozen indication CD4 CD8 NKT CHeckpoint blockage modulates MC38 infiltrating T cell population frequency Cellular mechanism interaction B16 engaging the ICOS T cell phenotyping -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@jimallison Nobel Laureate FOR CURE OF CANCER long survival on ipilimumab – Lung cancer pleural effusion CTLA-4Blockade – attenuated or Terminate Proliferation vs unrestrained Proliferation – APC PD-1 – Ipi/Nivo vs. Ipi Melanoma
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#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@StephenFriend @UniversityOxford wearables to forecast symptom transitions Pattern recognition for prognosis NEJM 20 years took since Breast cancer started to benefit from pattern recognition Participants-Centered 10M smart watchs -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@StephenFriend @UniversityOxford Health Assessment – signals to Symptoms Deep Learning – multiple stages of non-linear feature transformation Vector Institute – data analysis was done [Pregnancy] Forecasting – binary labels -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute @BrianWolpin@DanaFarber longitudinal Cohorts: Risk Factors Diabetes related to pancreatis cancer risk for weight and Diabetes Medication: Start/Stopped: NSAIDs Tylenol, H2 Blocker PPI, anti- HTN, ML – altered systemic metabolism -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute @BrianWolpin@DanaFarber Early detection of pancreatic cancer ML for Pancretic Cancer Early detection Metabolic alterations: Muscle wasting, subcutaneous adipose Automated Peripheral Tissue Segmentation 30,000 scans: changes -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@AndrewBeck@PathAI AI – deep learning is driving major advances in computer vision Reduce errors training set vs validation set computer vision vs Human eye – machine learning is BETTER -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@AndrewBeck@PathAI AI for pathology AI-powered companion diagnostics Pathologic diagnosis informs all subsequent medical decisions Discordance among pathologists is common in interpretation of breast biopsy skin biopsy melanoma -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@TommiJaakkola@MIT @CS_AIL Machine learning for drug design Automation arises from data How much data is enough? assey size vs all spectrum MIT Consorsium on pharmaceutical discovery – 13 Pharma, chemistry, chem eng, CS -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@TommiJaakkola@MIT @CS_AIL multi-resolution representations substructures with precision attachment – what atoms are underlined predicting molecular properties programmatic optimization – machine translation problem in analogy -
You Retweeted
Jay emphasizes how
@NovartisScience is striving to use petabytes of data (millions of patient years, billions of compounds) as a catalyst to overcome slow steps in drug discov & development.Highlights also clinical trial monitoring@kochinstitute@jaybradner#KIsymposium -
You Retweeted
Aine Hanly explains how
@Amgen is training their scientists & relies on external collaboration to increase data science. David@schenkein from@GVteam adds that he is most impressed with increasing cross-training in medical/life science and comp sci.@kochinstitute#KIsymposium -
You Retweeted
Aine Hanley
@Amgen’s remarks are music to my ears— she says it’s great to be close to@mit and our focus on AI, and she mentions the promise of convergence to really impact cancer#KIsymposium -
You Retweeted
Do we need innovative computational tools or be better at applying them?
@jaybradner says the crux is organizing data for already available tools. Constance Lehman@MGHImaging adds that translation is another bottleneck rather than model development.@kochinstitute#KIsymposium -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute Clifford Hudis American Society of Clinical Oncology (ASCO) 75% of communication is by FAX still White cell data count – convert data structure on EMR -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute David Schenkein GOOGLE Venture (GV) Regeneron, Geisinger, Amgen – Big data repositories are built Clinical Trials and big data delivery side: AI: Pathology and Radiology, early diagnostics: AI in MDs Offices to record the entire -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@ConstanceLehman@MGHCancerCenter Screening mammorgam is runned via the algorithm, report generated, Radiologist accept or reject the modle presented more domains need AI and specialties of MedicineDevelopment and implementation -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute @AineHanly VP Amgen 17% improvement in survival rate in 50 years Data infrastructure investment paid off Partnerships, interoperability in EMR -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@JayBradner@Novartis AI is a Tool in all phases of drug discovery integrated aanalysis of epigenetics strategy for investment in 400 Scientists, big data monitoring of Clinical Trial around the World generative chemistry -
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Aviv Regev
@broadinstitute@kochinstitute predicts which patients are responsive to immune blockade inhibitors and reverses resistance with abemaciclib https://www.ncbi.nlm.nih.gov/pubmed/30388455#KIsymposium
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You Retweeted
“Tumor plasticity is the driver… Cancer cells have access to new programs, new combinations of programs.”
@dana_peer@kochinstitute#KIsymposium -
You Retweeted
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Just blown away by the progress in AI/ML in cancer cell biology, en route to medicines, here at the Annual Koch Symposium at MIT. If you like MIBI (as I do), just wait till you see spatial transcriptomics.
@KochInstitute#KISymposium -
You Retweeted
For the shape of things to come,
@sloan_kettering’s@dana_peer on “Manifolds underlying plasticity in development, regeneration and cancer.”#KIsymposium -
You Retweeted
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You Retweeted
Dear
@FDACommissioner, the#vapor industry shares many of your goals, and wants to ensure a future with#VapingForAdults only.#ecigs#vaping https://www.linkedin.com/pulse/open-letter-acting-fda-commissioner-sharpless-chris-howard… via@LinkedIn -
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You Retweeted
Fascinating talk by Regina Barzilay. “Current clinical decisions in the US today are based on only 3% of the population …
#machinelearning can overcome some of the existing bias by data curation, anti bias training and human machine validation”@kochinstitute#KIsymposium – at MIT Kresge Auditorium (Building W16)
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You RetweetedMore
Awesome talk: “Manifolds underlying plasticity in development, regeneration and cancer.” by
@dana_peer@kochinstitute#KIsymposium https://twitter.com/kochinstitute/status/1139562868926504960… -
You Retweeted
Patient tumor Biopsies reveal the heterogeneity in cellular response – need better stratification for better combination therapies
#KIsymposium https://twitter.com/daniel_reker/status/1139571800881258496…
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You Retweeted
MIBI it’s time to get down to business. First up,
@Stanford@stanfordpath’s@MikeAngeloLab with “Comprehensive enumeration of immune cells in solid tumors using MIBI.”#KIsymposium
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You Retweeted
Now
@sloan_kettering‘s@dana_peer showcases how she harnesses Markov Chains to curate RNA seq data (MAGIC, https://www.ncbi.nlm.nih.gov/pubmed/29961576) & predict cell differentiation (Palantir, https://www.biorxiv.org/content/10.1101/385328v1…) w striking applications over broad range of biology.@kochinstitute#KIsymposium
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You Retweeted
Does everyone have their secret decoder rings ready?
@Princeton’s@OlgaTroyanskaya is next with, “Decoding cancer genomes with deep learning.”#KIsymposium
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You Retweeted
Slick new Mass Spectrometer designed by
@Stanford@StanfordPath Prof.@MikeAngeloLab for multiplexed imaging#KIsymposium
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You Retweeted
No Neural Networks here. Instead,
@dana_peer of@sloan_kettering uses MAGIC: ideas similar to Principal Component Analysis to identify gene interactions from single cell data#KIsymposium – at MIT Kresge Auditorium (Building W16) -
You Retweeted
@Stanford’s@MikeAngeloLab uses mas spec (MIBI) instead of classic IHC to incr resolution of tissue analysis, large data for “DeepCell” to understand tumor-immune microenvironment. https://www.ncbi.nlm.nih.gov/pubmed/30193111 Moving to 3D segmentation &organization now.@kochinstitute#Kisymposium
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#KIsymposium@pharma_BI@AVIVA1950@kochinstitute @PeterSorger@Harvardmed Highly multiplexed highresolution image of FFPE specimens Assaying the effects of multiple possible Cancer Precision Medicine per NCI Precision medicine based on therapeutic response at single cell level -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute @PeterSorger@harvardmed Redigitalization of 300 clinical trials combination immunotherapy: Assumption each patient benefits from both = synergy vs the assumption of NO interaction: each Patient benefit from ONE or the Other -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute @PeterSorger@harvardmed@Novartis PDX models Gastric Cancer PDXs Three way combination drug therapy – a deeper look at RAF and MEK inhibitors in BRAF mutant melanoma -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@DanaPe’er@MSK Pancreatic Ductal Adenocarcinoma – ductal metaplagiea Immune ecosystem reprogrammed Leptomeningeal niche — iron binding – adopt environment and prevent iron from the immune systemTumor plasticity – Mutation Kras -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@Princeton specific Interpreting a genome: predicting the effect of any mutation: coding variant vs non-coding variance – regulatory code, transcription marks, DeepSEA – model learns from one genome interactions of biochemical -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@OlgaTroyanskaya@Princeton Interpreting a Genime: predicting the effect of any mutation: Noncoding variant effect in ASD from WGS dataDenovo mutations: How impactful are these mutations in AUtism patients – to % not explained by -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@OlgaTroyanskaya@Princeton Decoding cancer genomes with deep learning genome to phynotype – BIG genomics data to understand human disease exosome non coding region gene regulation affected by genome for gene expression sequence -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@AngeloLab Single cell: imaging data trained by nuclear interior (nuclei only, segmentation channel using DeepCell v1.0 TB granulomas, Cell type identification with deep learning – No downstream 3D Segmentation Melanoma -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute Heiden Michael Angelo@Stanford immune cells in solid tumors using Multiplexed ion beam imaging (MIBI) Tissue imaging – high dimensional, multiplexing: high sensitivity, dynamic range, GI Tissue selection – immuno-florescence -
You Retweeted
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You Retweeted
“Drug discovery is totally a
#MachineLearning type of problem… Probably the most exciting is using#MachineLearning to create new molecules.” –Dr.Barzilay@kochinstitute#KIsymposium
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You Retweeted
Good morning, MIT! We have a full day of
#MachineLearning and#Cancer ahead of us at our annual symposium, but we’re ready! Get your
and join the conversation at #KIsymposium.
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You Retweeted
Dr. Regina Barzilay talks about the potential of using
#MachineLearning to predict#breastcancer before the signs appear#KIsymposium
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You Retweeted
Now for a (slight) change of subject: KI and
@MIT_CSAIL faculty member Regina Barzilay on “How machine learning changes cancer research.”#KIsymposium -
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And we’re off, with an introduction from KI director Tyler Jacks!
#KIsymposium
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You Retweeted
RB: Improved prediction of breast cancer risk after deep learning from 1000’s of breast images
#KIsymposium
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You Retweeted
James P. Allison shares the breakthrough story of immune checkpoint inhibitors, AB such as ipilimumab extending medium survival from months to decades, first nobel prize for cancer treatments.
@CancerCureDoc@kochinstitute#KIsymposium
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You Retweeted
Inspired by the recent progress in melanoma treatment listening to
@CancerCureDoc of@MDAndersonNews at the@kochinstitute#KIsymposium
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You Retweeted
Prof. Aviv Regev of
@humancellatlas@broadinstitute@kochinstitute lays out the case for “Design for Inference”, to generate large datasets from experimental tools on which to train#MachineLearning#KIsymposium – at MIT Kresge Auditorium (Building W16)
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You Retweeted
Regina Barzilay
@MIT_CSAIL@kochinstitute highlights how clinical decisions are currently based on trials focusing on a subset of the population, and how automated text mining can help alliviate this challenge.#KIsymposium
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You Retweeted
Incredible overview of work in Aviv Regev’s lab developing
#machinelearning approaches to make causal inferences in#cancer that can be functionally validated@kochinstitute#KIsymposium -
You Retweeted
Two of my favorite people on stage for a great start to the day— Phil Sharp and RBG.
#KIsymposium kicks off for its 18th year
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You Retweeted
Phil Sharp introduces Jim Allison as the only cancer scientist who ever won a Nobel prize for a cure for cancer. And the only one who has played harmonica on stage with Billie Nelson. Also the subject of Bill Haney’s latest film “Breakthrough” by
@uncommonprod#KIsymposium -
You Retweeted
Jim Allison update on CTLA-4 includes suggestion that “CTLA-4 actually creates cells that go out and kill tumor cells.”
#KIsymposium
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You Retweeted
“All clinical decisions made today are based on the 3% of patients who participated in clinical trials”. Regina on the use of NLP, DL and ML to better predict response and match patients to treatment in a very personalized way
#KIsymposium -
You Retweeted
AI can help handle bias and decrease healthcare inequity. Regina Barzilay
@kochinstitute#KIsymposium
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#KIsymposium@pharma_BI@AVIVA1950@kochinstitute @reginabarzilay Myth #1: DEMOCRATIZATION OF AI – deeplearning and customization Myth@2: Bias – Deep Learning allows across population studies Myth #3: Black-Box Architecture 0@MIT Interpretable models J-Clinic@MITML and Health -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute @ReginaBarzilay MIT How machine learning changes cancer research ML & Clinical Oncology Data Science perspective – Raw Data to Cure: Extract information NLP – penetration of deepLearning@MGH @MITDrug discovery – New molecules -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute @avivregev Insi2Vec: Define a cell by intrinsic and extrinsic and spatial features: generalize across patient samples in melanoma Identifying key patio-molecular archetypes Expanding full Transcriptome toolbox for spatial genome -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute @avivregev Synovial sarcome has anti-tumor activity in its immune environment Melanoma vs Synovial sarcome (SS18-SSX fusion directly controls core oncogenic program Target the core ongogenic program therapeutically IFN CD8 TNF -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@jimallison FDA Approved CTLA-4 and Anti-PD-1 for a dozen indication CD4 CD8 NKT CHeckpoint blockage modulates MC38 infiltrating T cell population frequency Cellular mechanism interaction B16 engaging the ICOS T cell phenotyping -
#KIsymposium@pharma_BI@AVIVA1950@kochinstitute@jimallison Nobel Laureate FOR CURE OF CANCER long survival on ipilimumab – Lung cancer pleural effusion CTLA-4Blockade – attenuated or Terminate Proliferation vs unrestrained Proliferation – APC PD-1 – Ipi/Nivo vs. Ipi Melanoma2 retweets3 likesReplyRetweetLike
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week: _atanas_, Pardoe_AI, DNAed_tech. Thank you! via https://sumall.com/thankyou?utm_source=twitter&utm_medium=publishing&utm_campaign=thank_you_tweet&utm_content=text_and_media&utm_term=982cd4ccfc5b4b8fbd4f691a …
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eProceedings 18th Symposium 2019
#ML#AI#Cancer#KISymposium@MIT covered in#realTime@pharma_BI@AVIVA1950 Amazing event,#Frontier#Science#oncology#cell#biology Keynote@JimAllison best talks @avivregev@danape’er@olga@princeton @reginabarzelay -
Top lectures by @reginabarzilay
@MIT @avivaregev@MIT@Danapeer@sloan_kettering@OlgaTroyanskaya@Princeton@AndrewBeck@Path_AI -
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