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With the growing number of single-cell epigenomic methods, a key challenge in this field is the computational combination of various single-cell “omics” methods to enable integrated cellular regulatory models. Further advanced ML is required to decode high-dimensional single-cell data [123].
Feb 7, 2020 · Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward.
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Sep 3, 2016 · A challenging aspect of single cell data is that every sample (e.g. a patient) is represented by a “bag” of cells, each with its own properties.
Our goal is to provide an open source, community driven, extensible platform for continuously updated benchmarking of formalized tasks in single-cell analysis.
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Feb 10, 2023 · Technical Challenges · 1. Low RNA Input · 2. Amplification Bias · 3. Dropout Events: · 4. Batch Effects · 5. Cell Doublets · 6. Quality Control · 7.
In this review, we will summarize the latest progress of single-cell sequencing data analysis from a machine learning viewpoint.
Feb 23, 2022 · Deep learning has tremendous potential in single-cell data analyses, but numerous challenges and possible new developments remain to be explored ...
Single-cell data comes with various challenges that requires careful computational data preprocessing, denoising, batch correction, and (meta)feature selection.
May 9, 2024 · Hierarchical classifier: a machine-learning model that organizes and categorizes data into multiple levels or layers of nested classes, allowing ...
Sep 7, 2022 · A major informatic challenge in single cell RNA-sequencing analysis is the precise annotation of datasets where cells exhibit complex ...