Metabolomics - CERM
Metabolomics - CERM Metabolomics - CERM
Remarks on pattern classification Intent in using these classification techniques not to identify specific compound Classify in specific categories, conditions or disease status Traditional clinical chemistry depended on identifying and quantifying specific compounds Chemometric profiling interested in looking at all metabolites at once and making a phenotypic classification of diagnosis
Targeted profiling Targeted metabolomic profiling is fundamentally different than most chemometric approaches. In targeted metabolomic profiling the compounds in a given biofluid or tissue extract identified and quantified by comparing the spectrum of interest to a library of reference spectra of pure compounds. Key advantage: Does not require collection of identical sets = More amenable to human studies or studies that require less day-to-day monitoring. Disadvantage: Relatively limited size of most current spectral libraries = bias metabolite identification and interpretation. A growing trend towards combining the best features of both chemometric and targeted methods.
- Page 5 and 6: Metabolomica: la nuova frontiera
- Page 7 and 8: Metabonomics “…measurement of t
- Page 9 and 10: Metabolomica: alcuni obiettivi Valu
- Page 11 and 12: Metabolomica: alcuni obiettivi Scop
- Page 13 and 14: The metabolome consists of what? Sm
- Page 15 and 16: C om p ound class N u m b er C om p
- Page 17 and 18: Why 1 µM? Equals ~200 ng/mL Limit
- Page 19 and 20: Medical Metabolomics Generate metab
- Page 21 and 22: Traditional Metabolite Analysis HPL
- Page 23 and 24: What’s the Difference Between Met
- Page 25 and 26: Advantages Measure multiple (10’s
- Page 27 and 28: 2 Routes to Metabolomics Two approa
- Page 29 and 30: Benefits of analyzing the metabolom
- Page 31 and 32: Advantages: Use of NMR in Non-destr
- Page 33 and 34: Raccolta e stoccaggio dei campioni
- Page 35 and 36: NMR A typical 950-MHz H NMR spectru
- Page 37 and 38: Profilo 1 H NMR di urina umana
- Page 39 and 40: Profilo di siero umano Proteins + L
- Page 41 and 42: Data analysis - approach Classify N
- Page 43: PCA Unsupervised Multivariate analy
- Page 51 and 52: PCA Loadings Plot • Loadings plot
- Page 53 and 54: SIMCA Supervised learning method ba
- Page 55: OPLS OPLS method is a recent modifi
- Page 59 and 60: Databases
- Page 61 and 62: Effetto della dieta Trimetilammina
- Page 63 and 64: Effetto delle patologie Campioni di
- Page 65 and 66: METabolomic REFerence The METREF pr
- Page 67 and 68: Ind 1 Ind 2 METabolomic REFerence G
- Page 69 and 70: METabolomic REFerence Dendrogram of
- Page 71 and 72: METabolomic REFerence An individual
- Page 73 and 74: METabolomic REFerence 2 Experimenta
- Page 75 and 76: METabolomic REFerence 2 2005 collec
- Page 77 and 78: Un “salto” metabolico Evoluzion
- Page 79 and 80: Celiac Disease Metabolomics Experim
- Page 81 and 82: Celiac Disease Metabolomics Note: b
- Page 83 and 84: Celiac Disease Metabolomics Celiac
- Page 85 and 86: Celiac Disease Metabolomics Cluster
- Page 87 and 88: Potential celiac disease Subjects:
- Page 89 and 90: Celiachia Potenziale Celiaci - Sani
- Page 91 and 92: ACIDO LINOLENICO REGIONE DEI METILI
- Page 93 and 94: PRODOTTI DI OSSIDAZIONE QUASI ASSEN
- Page 95 and 96: CARATTERIZZAZIONE GEOGRAFICA ( 1 H)
- Page 97 and 98: Metabolomica del latte Recente inte
- Page 99 and 100: Spettro 1 H NMR di marche diverse N
- Page 101 and 102: Formato Spettro 1 H NMR di latte di
- Page 103 and 104: formato Formato acetato e piruvato
- Page 105: Disponibilità di Tesi in METABOLOM
Remarks on pattern<br />
classification<br />
Intent in using these classification techniques not to identify<br />
specific compound<br />
Classify in specific categories, conditions or disease status<br />
Traditional clinical chemistry depended on identifying and<br />
quantifying specific compounds<br />
Chemometric profiling interested in looking at all<br />
metabolites at once and making a phenotypic<br />
classification of diagnosis
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