Artificial Intelligence & Cell Analysis
Artificial Intelligence and Machine learning became part of many breakthroughs in the medical and research fields as AI technologies enable researchers to extract more information than the human factor can. As the machines can sometimes spot things the human eye can miss. One example of those complex processes is cell count and analysis.
Artificial intelligence is widely used nowadays in many researches, as it is capable of achieving and acquiring results efficiently. Cell analysis normally is a very Laborious, tedious and tiring process, as it needs lots of time, focus and efficiency, that’s why researchers have been developing software which aims to help them achieve the required procedures. During those trials, researchers have realized that, in some cases, the machine learning tools and AI software spots Complex and hidden information, which they themselves couldn’t extract during the experiment, so after careful consideration, it was obvious that AI can be quite beneficial to researches as it can spot details which the human eye does not notice.
Cellular structures are very complex, so to be able to analyse them is very complicated and sensitive, as you have to be extremely focused for many hours bent over a microscope. Both the complexity of the cellular structure and the amount of data required is too much, which ultimately creates challenges for the researcher. To gather such huge amounts of data and analysis its details thoroughly by the researcher can be very problematic, that’s why inputting this information into a software makes the process much easier and more accurate It also enables researchers to analyse complex data sets which cannot be done manually. This can be done by integration analysis; which is a method that aims to compare multiple data sets. When integrating data sets from various batches, the primary goal is to remove technical bias, and doing this without distorting the original biological data. Machine learning enables correlation process occurring between multiple data sets, which increases the number of samples; since it’s a computer and can take any amount of data given, therefore enabling researchers to work with better and much accurate statistics.
Moreover, machine learning is also used to identify cell types, and it can be done using unsupervised machine learning tools. Cell-type classification is done through different approaches; partitioning, hierarchical clustering, and graph based clustering. All these approaches determine cell-type through the patterns of marker-genes. Patterns of cells and, tissues that are similar can be leveraged through algorithms by incorporating prior knowledge into the data analysis. Shared variations in genes can be transferred from the source data to the target data. This procedure can be done by an auto-encoder, meanwhile it ensures the preservation of relevant biological differences. This approach has drastically improved clustering results.
Researchers have recognised that cell-imaging contains much more than what can be seen by the human eye, therefore using AI in cell analysis is very accurate and useful. Cell Image analysis has a high potential to make complex analysis information more accessible and reliable.