.css-1xsl8rf{width:100%;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;position:relative;overflow:hidden;background:var(--alert-bg);-webkit-padding-start:var(--chakra-space-4);padding-inline-start:var(--chakra-space-4);-webkit-padding-end:var(--chakra-space-4);padding-inline-end:var(--chakra-space-4);padding-top:var(--chakra-space-1);padding-bottom:var(--chakra-space-1);--alert-fg:var(--chakra-colors-orange-600);--alert-bg:var(--chakra-colors-orange-100);font-weight:var(--chakra-fontWeights-semibold);padding-left:var(--chakra-space-4);}.chakra-ui-dark .css-1xsl8rf:not([data-theme]),[data-theme=dark] .css-1xsl8rf:not([data-theme]),.css-1xsl8rf[data-theme=dark]{--alert-fg:var(--chakra-colors-orange-200);--alert-bg:rgba(251, 211, 141, 0.16);}@media screen and (min-width: 48em){.css-1xsl8rf{padding-left:var(--chakra-space-8);}}Bitte aktivieren Sie Javascript um alle Funktionen nutzen zu können und ihre Nutzererfahrung zu verbessern.

Junyan Lu

Heidelberg / DE

Heidelberg University

Vortrag

Di., 22.10.
Poster presentation
P-II-0476
MsBayesImpute: a versatile framework for handling missing values in mass-spectrometry data through a combined Bayesian factorization and probabilistic dropout model
New Technology: AI and Bioinformatics in Mass Spectrometry

Weitere Beteiligungen

Mo., 21.10.
Poster presentation
P-I-0016
Exploring EGFR and MET cross-talk in NSCLC: a pathway to improved therapy efficacy
Defining Signaling Networks - Functional PTMs
Mo., 21.10.
Poster presentation
P-I-0277
Identifying mass spectrometry-based biomarkers for predicting tumor recurrence in lung cancer
Clinical Proteomics
Di., 22.10.
Poster presentation
P-II-0476
MsBayesImpute: a versatile framework for handling missing values in mass-spectrometry data through a combined Bayesian factorization and probabilistic dropout model
New Technology: AI and Bioinformatics in Mass Spectrometry
Mi., 23.10.
Poster presentation
P-III-0792
Exploring dynamic behavior with SmartPhos: an integrated pipeline for low-input, high-throughput Phosphoproteomics in clinical samples
Data Integration: With Bioinformatics to Biological Knowledge