#biomarkerdiscovery — Public Fediverse posts

📢 Call for Papers: AI for Real-World Evidence Generation in Biomedicine

⏳ Submission Deadline: 31 January 2027

🔗 Submit your work: https://spj.science.org/journal/csbj/si/ai-real-world-evidence-generation

#CallForPapers #RealWorldEvidence #RWE #ArtificialIntelligence #AIinHealthcare #BiomedicalAI #MachineLearning #ClinicalInformatics #ComputationalBiology #CausalAI #Multiomics #BiomarkerDiscovery #PrecisionMedicine #DigitalHealth #RegulatoryScience #BiomedicalResearch

📢 Call for Papers: AI for Real-World Evidence Generation in Biomedicine

⏳ Submission Deadline: 31 January 2027

🔗 Submit your work: https://spj.science.org/journal/csbj/si/ai-real-world-evidence-generation

#CallForPapers #RealWorldEvidence #RWE #ArtificialIntelligence #AIinHealthcare #BiomedicalAI #MachineLearning #ClinicalInformatics #ComputationalBiology #CausalAI #Multiomics #BiomarkerDiscovery #PrecisionMedicine #DigitalHealth #RegulatoryScience #BiomedicalResearch

📢 Call for Papers: AI for Real-World Evidence Generation in Biomedicine

⏳ Submission Deadline: 31 January 2027

🔗 Submit your work: https://spj.science.org/journal/csbj/si/ai-real-world-evidence-generation

#CallForPapers #RealWorldEvidence #RWE #ArtificialIntelligence #AIinHealthcare #BiomedicalAI #MachineLearning #ClinicalInformatics #ComputationalBiology #CausalAI #Multiomics #BiomarkerDiscovery #PrecisionMedicine #DigitalHealth #RegulatoryScience #BiomedicalResearch

📢 Call for Papers: AI for Real-World Evidence Generation in Biomedicine

⏳ Submission Deadline: 31 January 2027

🔗 Submit your work: https://spj.science.org/journal/csbj/si/ai-real-world-evidence-generation

#CallForPapers #RealWorldEvidence #RWE #ArtificialIntelligence #AIinHealthcare #BiomedicalAI #MachineLearning #ClinicalInformatics #ComputationalBiology #CausalAI #Multiomics #BiomarkerDiscovery #PrecisionMedicine #DigitalHealth #RegulatoryScience #BiomedicalResearch

The Medicine Maker | HTC-19 Profile: What’s Cooking in the mRNA Lab?

Jonathan Maurer. Credit: Collage created using Adobe Stock images.
Please tell us a…
#France #FR #Europe #EU #Sanofi #analyticalchemistry #biomarkerdiscovery #HILIC #lipidnanoparticles #mRNA-LNP #pharmaceuticalsciences #proteinanalytics
https://www.europesays.com/france/31090/

Our paper (with Julie Cartier, Johanna Lagoas, Youmna Ayadi, Adeline Fermanian and @flomass) on the use of statistical knockoffs for the differential analysis of transcriptomics data just came out, very appropriately as it nicely illustrates my point:
https://academic.oup.com/bib/article/27/3/bbag148/8687371

Using simulated outcomes on real transcriptomics data, we've shown that KOs (and in particular, the KOPI approach) do retrieve important variables with better power than classical approaches (Wilcoxon, Lasso), while controlling FDR.

However, all methods perform poorly when the relationship between gene expressions and outcome is nonlinear.

On real outcomes, the method is overly conservative (having no discoveries is a surefire way of controlling your number of false discoveries), and we had to turn the false discovery rate threshold to 50% to select any gene at all.

#machineLearning #genomics #featureSelection #biomarkerDiscovery #transcriptomics

Our paper (with Julie Cartier, Johanna Lagoas, Youmna Ayadi, Adeline Fermanian and @flomass) on the use of statistical knockoffs for the differential analysis of transcriptomics data just came out, very appropriately as it nicely illustrates my point:
https://academic.oup.com/bib/article/27/3/bbag148/8687371

Using simulated outcomes on real transcriptomics data, we've shown that KOs (and in particular, the KOPI approach) do retrieve important variables with better power than classical approaches (Wilcoxon, Lasso), while controlling FDR.

However, all methods perform poorly when the relationship between gene expressions and outcome is nonlinear.

On real outcomes, the method is overly conservative (having no discoveries is a surefire way of controlling your number of false discoveries), and we had to turn the false discovery rate threshold to 50% to select any gene at all.

#machineLearning #genomics #featureSelection #biomarkerDiscovery #transcriptomics

Our paper (with Julie Cartier, Johanna Lagoas, Youmna Ayadi, Adeline Fermanian and @flomass) on the use of statistical knockoffs for the differential analysis of transcriptomics data just came out, very appropriately as it nicely illustrates my point:
https://academic.oup.com/bib/article/27/3/bbag148/8687371

Using simulated outcomes on real transcriptomics data, we've shown that KOs (and in particular, the KOPI approach) do retrieve important variables with better power than classical approaches (Wilcoxon, Lasso), while controlling FDR.

However, all methods perform poorly when the relationship between gene expressions and outcome is nonlinear.

On real outcomes, the method is overly conservative (having no discoveries is a surefire way of controlling your number of false discoveries), and we had to turn the false discovery rate threshold to 50% to select any gene at all.

#machineLearning #genomics #featureSelection #biomarkerDiscovery #transcriptomics

Our paper (with Julie Cartier, Johanna Lagoas, Youmna Ayadi, Adeline Fermanian and @flomass) on the use of statistical knockoffs for the differential analysis of transcriptomics data just came out, very appropriately as it nicely illustrates my point:
https://academic.oup.com/bib/article/27/3/bbag148/8687371

Using simulated outcomes on real transcriptomics data, we've shown that KOs (and in particular, the KOPI approach) do retrieve important variables with better power than classical approaches (Wilcoxon, Lasso), while controlling FDR.

However, all methods perform poorly when the relationship between gene expressions and outcome is nonlinear.

On real outcomes, the method is overly conservative (having no discoveries is a surefire way of controlling your number of false discoveries), and we had to turn the false discovery rate threshold to 50% to select any gene at all.

#machineLearning #genomics #featureSelection #biomarkerDiscovery #transcriptomics

https://www.europesays.com/ie/282589/ AstraZeneca to acquire Modella AI to speed oncology drug research #AI #AradhanaSarin #ArtificialIntelligence #ArtificialIntelligence #astrazeneca #BiomarkerDiscovery #ClinicalData #ClinicalDevelopment #Companies #Éire #IE #Ireland #Modella #Technology

🧬 Is your metabolism quietly signaling the earliest signs of future disease?

🔗 Metabolic phenotypes: Molecular bridges between health homeostasis and disease imbalance. Computational and Structural Biotechnology Journal, DOI: https://doi.org/10.1016/j.csbj.2025.10.057

📚 CSBJ: https://www.csbj.org/

#MetabolicPhenotyping #Metabolomics #PrecisionMedicine #MultiOmics #BiomarkerDiscovery #TranslationalMedicine #DiseasePrevention #AIinHealthcare

🧬 Is your metabolism quietly signaling the earliest signs of future disease?

🔗 Metabolic phenotypes: Molecular bridges between health homeostasis and disease imbalance. Computational and Structural Biotechnology Journal, DOI: https://doi.org/10.1016/j.csbj.2025.10.057

📚 CSBJ: https://www.csbj.org/

#MetabolicPhenotyping #Metabolomics #PrecisionMedicine #MultiOmics #BiomarkerDiscovery #TranslationalMedicine #DiseasePrevention #AIinHealthcare

Nghiên cứu mới giới thiệu phương pháp "lựa chọn cảm biến động" dựa trên lý thuyết quan sát để khám phá dấu ấn sinh học (biomarker). Phương pháp này giúp xác định và diễn giải các tín hiệu sinh học một cách hiệu quả từ lượng lớn dữ liệu, tối ưu hóa việc chọn biomarker theo thời gian. Ứng dụng rộng rãi trong y tế, nông nghiệp, sản xuất sinh học và hệ thần kinh.
#BiomarkerDiscovery #DynamicSensorSelection #ObservabilityTheory #SinhHoc #NghienCuuKhoaHoc #YTe

https://www.reddit.com/r/singularity/com