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ANN belongs to a new kind of artificial intelligence technique, which can surmount difficulties in data analysis and model development, so it is suitable for non-linear and unstructured information processing in materials research.
Although this technology has been used extensively, limitations still persist; including limited probe coverage, cross-hybridization artifacts, requirement of previously known gene structures and difficulties in data analysis, etc.
Nevertheless, to deal with biological data, there are not only a lot of issues in machine learning algorithms but also a lot of difficulties in data analysis.
The EST approach provides only partial sequences of individual cDNA clones, is sensitive to cloning biases, and is associated with high costs and difficulties in data analysis.
Current limitations of "-omics" include low reproducibility across laboratories, high intra-individual variability that hampers inter-individual comparisons, high costs, difficulties in data analysis and uncertainties in biological interpretation (see table 2).
Given the plethora of equipment and protocols currently available, a new investigator could conceivably become overwhelmed by alternatives and subsequently make a poor choice that could lead to downstream difficulties in data analysis and interpretation.
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First, a major limitation of moving into 3D combinatorial encoding approaches is the difficulty in data analysis.
Current paper-based evaluation instruments have numerous limitations: difficulty in data analysis, significant delays in identifying problem trends and poor user compliance.
Many researches have demonstrated that widely used paper-based evaluation systems are costly and time-consuming, with difficulty in data analysis and significant delays in identifying problem trends [ 4].
Regularization techniques are widely employed in the solution of inverse problems in data analysis and scientific computing due to their effectiveness in addressing difficulties due to ill-posedness.
participated in data analysis.
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