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Machine learning’s role in life sciences research As we move towards more generalised AI models, neural networks and natural language interfaces, we’re starting to see machine learning take the place of higher order reasoning and data analysis “sense making.” It’s too early to tell but I’m optimistic.
Previous Next To assess LipocyteProfiler, the researchers analyzed white and brown fat cells and liver cells, focusing on traits such as the size, the number, and the location of lipid droplets in a cell. Credit: Phil Kubitz, Claussnitzer lab A merged image showing all of the stained cellular components together.
It’s now time to get back to chemical probes and I’ll be taking a look at S2023 (Systematic literature review reveals suboptimal use of chemical probes in cell-based biomedical research) which has already been reviewed in blog posts from Practical Fragments , In The Pipeline and the Institute of Cancer Research.
My main criticism of BW2014 that is that the rhetoric is not supported by data and I’ve always seen the article as something of a propaganda piece. I was rather surprised that BW2014 had not cited a single article about how colloidal aggregation can foil drug discovery. Given that M2003 had been published over a decade previously.
I’ll be taking a look at H2023 (Expanding Chemical Probe Space: Quality Criteria for Covalent and Degrader Probes) in this post and this article has also been discussed In The Pipeline. Subscripting doesn't work well in blogger and so I'll use K.i As measurement of k.inact/K.i
Data-driven discovery As with many early-stage biotech companies, Five Alarm Bio is highly data-driven, producing vast amounts of experimental data through cell-basedassays and phenotypic screening. As a business executive at IBM Research in New York, Dr Singh led the go-to-market for IBM Watson Genomics Analytics.
Cell-basedassays , while more complex to develop and use, offer another option for characterizing product activity at a slightly higher level, allowing sponsors to measure the further downstream effects of products (e.g., morphological cell changes or alterations in protein production or expression).
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