Machine learning in cell biology – teaching computers to recognize phenotypes

Abstract

SummaryRecent advances in microscope automation provide new opportunities for high-throughput cell biology, such as image-based screening. High-complex image analysis tasks often make the implementation of static and predefined processing rules a cumbersome effort. Machine-learning methods, instead, seek to use intrinsic data structure, as well as the expert annotations of biologists to infer models that can be used to solve versatile data analysis tasks. Here, we explain how machine-learning methods work and what needs to be considered for their successful application in cell biology. We outline how microscopy images can be converted into a data representation suitable for machine learning, and then introduce various state-of-the-art machine-learning algorithms, highlighting recent applications in image-based screening. Our Commentary aims to provide the biologist with a guide to the application of machine learning to microscopy assays and we therefore include extensive discussion on how to optimize experimental workflow as well as the data analysis pipeline.

Shaping the Cell and the Future: Recent Advancements in Biophysical Aspects Relevant to Regenerative Medicine

“…Machine learning with novel algorithms are gradually increasing and being used to link cellular morphological features to biomarker measurements and to recognize cell phenotypes [22,26,55,56]. Machine learning uses pattern recognition and computational tools to find functional relationships from the training data with minimal intervention or bias [55].…”

“…Tuning the software to both cell types using defined regions of interest (ROIs) and pixel-based machine learning enhanced the system and resulted in higher accuracy in identification of both tested cell types [57]. Pixel-based machine learning using was accomplished with the usage of ilastik software [55] succeeded by model-based segmentation of the predefined ROIs using the software CellProfiler [57]. Whereas machine learning or other computational strategies are highly promising approaches, it is not within the scope nor the aim of this review to recommend a particular strategy/system, as almost no comparative data is available.…”

Automated Identification and Measurement of Haematopoietic Stem Cells in 3D Intravital Microscopy Data

“…Machine learning (ML) has become a valuable artificial intelligence tool, increasingly used for analysis of complex image data [23,24]. ML serves two main objectives: classification and regression.…”

Application of Artificial Neural Networks for Catalysis: A Review

“…As its name implies, machine learning is able to "learn" the highly complicated relationships between the independent and dependent variables via non-linear "black box" data processing. During the past decades, it has been widely used in many scientific and industrial areas, such as biology [7][8][9], medicine [10][11][12], energy [13][14][15][16][17][18][19], environment [20][21][22], engineering [23][24][25], and information technology (IT) [26,27]. These application studies indicate that machine learning techniques have dramatically boosted the development of many different areas.…”