October 2017

Table of Contents - PeerJ (General Articles)

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Machine learning to analyze colon glands

Malignant colorectal cancer

October 3, 2017

Machine learning to analyze colon glands

Due to the large variability of biological tissue, machine learning techniques have shown superior performance over conventional image processing methods. Here, researchers present a deep neural network-based approach for segmentation and classification of glands in tissue of benign and malignant colorectal cancer.

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October 3, 2017
Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization
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https://doi.org/10.7717/peerj.3874 PubMed 29018612
October 31, 2017
Record breaking achievements by spiders and the scientists who study them
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https://doi.org/10.7717/peerj.3972 PubMed 29104823
October 3, 2017
Development and validation of near-infrared spectroscopy for the prediction of forage quality parameters in Lolium multiflorum
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https://doi.org/10.7717/peerj.3867 PubMed 29018608
October 17, 2017
How to normalize metatranscriptomic count data for differential expression analysis
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https://doi.org/10.7717/peerj.3859 PubMed 29062598
October 10, 2017
Illegal use of natural resources in federal protected areas of the Brazilian Amazon
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https://doi.org/10.7717/peerj.3902 PubMed 29038758
October 6, 2017
Enhanced conflict monitoring via a short-duration, video-assisted deep breathing in healthy young adults: an event-related potential approach through the Go/NoGo paradigm
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https://doi.org/10.7717/peerj.3857 PubMed 29018605
October 26, 2017
Constructing stage-structured matrix population models from life tables: comparison of methods
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https://doi.org/10.7717/peerj.3971 PubMed 29085763
October 6, 2017
voomDDA: discovery of diagnostic biomarkers and classification of RNA-seq data
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https://doi.org/10.7717/peerj.3890 PubMed 29018623
October 12, 2017
Nests of red wood ants (Formica rufa-group) are positively associated with tectonic faults: a double-blind test
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https://doi.org/10.7717/peerj.3903 PubMed 29038759
Machine learning to analyze colon glands

Malignant colorectal cancer

October 3, 2017

Machine learning to analyze colon glands

Due to the large variability of biological tissue, machine learning techniques have shown superior performance over conventional image processing methods. Here, researchers present a deep neural network-based approach for segmentation and classification of glands in tissue of benign and malignant colorectal cancer.

- read article

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I told my colleagues that PeerJ is a journal where they need to publish if they want their paper to be published quickly and with the strict peer review expected from a good journal.
Sohath Vanegas,
PeerJ Author
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