Matches in SemOpenAlex for { <https://semopenalex.org/work/W4363643567> ?p ?o ?g. }
- W4363643567 abstract "Development and homeostasis in multicellular systems both require exquisite control over spatial molecular pattern formation and maintenance. Advances in spatially-resolved and high-throughput molecular imaging methods such as multiplexed immunofluorescence and spatial transcriptomics (ST) provide exciting new opportunities to augment our fundamental understanding of these processes in health and disease. The large and complex datasets resulting from these techniques, particularly ST, have led to rapid development of innovative machine learning (ML) tools primarily based on deep learning techniques. These ML tools are now increasingly featured in integrated experimental and computational workflows to disentangle signals from noise in complex biological systems. However, it can be difficult to understand and balance the different implicit assumptions and methodologies of a rapidly expanding toolbox of analytical tools in ST. To address this, we summarize major ST analysis goals that ML can help address and current analysis trends. We also describe four major data science concepts and related heuristics that can help guide practitioners in their choices of the right tools for the right biological questions." @default.
- W4363643567 created "2023-04-11" @default.
- W4363643567 creator A5029714016 @default.
- W4363643567 creator A5048423959 @default.
- W4363643567 creator A5054846812 @default.
- W4363643567 date "2023-03-29" @default.
- W4363643567 modified "2023-10-17" @default.
- W4363643567 title "Machine Learning for Uncovering Biological Insights in Spatial Transcriptomics Data." @default.
- W4363643567 cites W1569946922 @default.
- W4363643567 cites W2003572485 @default.
- W4363643567 cites W2042789810 @default.
- W4363643567 cites W2055770801 @default.
- W4363643567 cites W2069089843 @default.
- W4363643567 cites W2086629747 @default.
- W4363643567 cites W2107018762 @default.
- W4363643567 cites W2156631105 @default.
- W4363643567 cites W2502949459 @default.
- W4363643567 cites W2591514928 @default.
- W4363643567 cites W2731142262 @default.
- W4363643567 cites W2768956845 @default.
- W4363643567 cites W2790675139 @default.
- W4363643567 cites W2797962115 @default.
- W4363643567 cites W2800392236 @default.
- W4363643567 cites W2805310212 @default.
- W4363643567 cites W2910705748 @default.
- W4363643567 cites W2915848182 @default.
- W4363643567 cites W2946901414 @default.
- W4363643567 cites W2964312306 @default.
- W4363643567 cites W2978551960 @default.
- W4363643567 cites W2986518719 @default.
- W4363643567 cites W2992151131 @default.
- W4363643567 cites W2994000519 @default.
- W4363643567 cites W2999977864 @default.
- W4363643567 cites W3001833132 @default.
- W4363643567 cites W3001838726 @default.
- W4363643567 cites W3007935255 @default.
- W4363643567 cites W3008695158 @default.
- W4363643567 cites W3020930839 @default.
- W4363643567 cites W3022836409 @default.
- W4363643567 cites W3024765177 @default.
- W4363643567 cites W3035327867 @default.
- W4363643567 cites W3083756487 @default.
- W4363643567 cites W3087998689 @default.
- W4363643567 cites W3091860120 @default.
- W4363643567 cites W3092450854 @default.
- W4363643567 cites W3101369110 @default.
- W4363643567 cites W3108118546 @default.
- W4363643567 cites W3113349968 @default.
- W4363643567 cites W3119379269 @default.
- W4363643567 cites W3126958162 @default.
- W4363643567 cites W3129476455 @default.
- W4363643567 cites W3132661792 @default.
- W4363643567 cites W3134482219 @default.
- W4363643567 cites W3152852090 @default.
- W4363643567 cites W3161009639 @default.
- W4363643567 cites W3165120573 @default.
- W4363643567 cites W3165800492 @default.
- W4363643567 cites W3177828909 @default.
- W4363643567 cites W3182041383 @default.
- W4363643567 cites W3204015056 @default.
- W4363643567 cites W3205339884 @default.
- W4363643567 cites W3208940117 @default.
- W4363643567 cites W3214367919 @default.
- W4363643567 cites W3217269355 @default.
- W4363643567 cites W4205817967 @default.
- W4363643567 cites W4210683641 @default.
- W4363643567 cites W4220675433 @default.
- W4363643567 cites W4224059281 @default.
- W4363643567 cites W4224218779 @default.
- W4363643567 cites W4225415043 @default.
- W4363643567 cites W4226192560 @default.
- W4363643567 cites W4256266823 @default.
- W4363643567 cites W4280551568 @default.
- W4363643567 cites W4281776605 @default.
- W4363643567 cites W4289956737 @default.
- W4363643567 cites W4291283682 @default.
- W4363643567 cites W4294053500 @default.
- W4363643567 cites W4296371910 @default.
- W4363643567 cites W4298125104 @default.
- W4363643567 cites W4300862018 @default.
- W4363643567 cites W4306406844 @default.
- W4363643567 cites W4307184360 @default.
- W4363643567 cites W4307497973 @default.
- W4363643567 cites W4308989306 @default.
- W4363643567 cites W4310937776 @default.
- W4363643567 cites W4311666191 @default.
- W4363643567 cites W4312056076 @default.
- W4363643567 cites W4313424250 @default.
- W4363643567 cites W4313893176 @default.
- W4363643567 cites W4317757307 @default.
- W4363643567 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37033464" @default.
- W4363643567 hasPublicationYear "2023" @default.
- W4363643567 type Work @default.
- W4363643567 citedByCount "0" @default.
- W4363643567 crossrefType "posted-content" @default.
- W4363643567 hasAuthorship W4363643567A5029714016 @default.
- W4363643567 hasAuthorship W4363643567A5048423959 @default.
- W4363643567 hasAuthorship W4363643567A5054846812 @default.
- W4363643567 hasConcept C107457646 @default.
- W4363643567 hasConcept C111919701 @default.