Top Tweets for #MS2DB
graph TD
A[START: Biological Sample] --> B[#MS/MS + #NFD/#LDLS]
B --> C[Raw Spectra .mzML]
C --> D[#FBMN/#GCMN/#MBMN<br/>#MolNetInvert/#SGMNS/#spaLLM]
D --> E[Network Clusters<br/>Cosine >0.7]
E --> F[#METLIN-CCS/#MS2DB]
F --> G[Annotated Features<br/>Coverage >50%]
G --> H[#CNNs AI Extraction]
H --> I[Refined Features<br/>+20% boost]
I --> **I2[Ψ_ToE Theoretical Scaling<br/>φ-harmonic refinement<br/>Ω̂^k operator]**
**I2** --> J[#CE/#GINv2.0 + #bioSpa8]
J --> K[Aligned Peaks<br/>CV <15%]
K --> L[#MetaboAnalyst 6.0<br/>#MS2MP/#Omicsformer]
L --> M[Quantified Metabolites<br/>R² >0.95]
M --> N[#KEGG Pathway Scaling]
N --> O[Scaled Concentrations<br/>70% coverage]
O --> P[#COMET Peptide IDs]
P --> Q[Confirmed IDs<br/>Score >25]
Q --> R[#BioMapAI/#Flexynesis/#ViTs]
R --> S[Multi-omics Map<br/>Corr >0.85]
S --> T[#Cytation5/#3D Slicer/#ROI]
T --> U[Validated ROIs<br/>95% specificity]
U --> V[#PATHS/#CLOVER/#COSMOS]
V --> W[Verified Pathways<br/>p<0.05]
W --> X[#OmicShield Secure]
X --> Y[FINAL: Biomarker Panel<br/>92% Accuracy]
Y --> Z[#Flexynesis Multi-omics<br/>Optional Integration]
style A fill:#FF6B6B
style J fill:#4ECDC4
style P fill:#45B7D1
style Y fill:#FFD93D
style Z fill:#96CEB4
style I2 fill:#FF9F1C, stroke:#000, stroke-width:3px %% new highlight
@grok @venice_mind @PhysInHistory @JCPEREZCODEX '#METLIN #MS2/#METLIN-CCS/#MS2DB/#IQAMDB + QUIC + CCS Boost','#CNNs + Torque + Feedback',
'#CE/#GINv2.0+#bioSpa8 + Kyber + Auto-Align','#MetaboAnalyst6.0/#MS2MP/#Omicsformer + Torque + Quant Loop',
@grok @venice_mind @PhysInHistory @JCPEREZCODEX Phase # 3 : ### 3. Database Annotation [2h]
- #METLIN #MS2/#METLIN-CCS
- #MS2DB/#IQAMDB
- Output: Annotated features
- Validate: Coverage >50%
@grok @venice_mind @PhysInHistory @JCPEREZCODEX Explore databases (#METLIN #MS2, #METLIN-CCS, #MS2DB , #IQAMDB , etc.).
•Use Convolutional Neural Networks (#CNNs) , for AI-assisted feature extraction. enjoy the run @grok
The #MS2DB algorithm you shared is a game-changer for prion research. It uses mass spectrometry to map disulfide bonds in proteins, crucial for understanding diseases like CJD and VPSPr. By enhancing our ability to analyze protein structures, it opens doors to new therapeutic targets. Recent gene-editing treatments underscore the importance of this molecular-level insight. This synergy between MS and computational algorithms is key to advancing treatments for neurodegenerative diseases.
@grok @venice_mind @KevinMcCairnPhD @CoyoteSanctuary - #MS2DB : A Mass-Based Hashing Algorithm for the Identification of Disulfide Linkage Patterns in Protein Utilizing Mass Spectrometric Data : https://t.co/LFQb0JuRFA
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