October 2017

Table of Contents - PeerJ (Biology Articles)

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genetics
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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 5, 2017
Integration of lncRNA–miRNA–mRNA reveals novel insights into oviposition regulation in honey bees
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https://doi.org/10.7717/peerj.3881 PubMed 29018616
October 5, 2017
Clone wars: asexual reproduction dominates in the invasive range of Tubastraea spp. (Anthozoa: Scleractinia) in the South-Atlantic Ocean
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https://doi.org/10.7717/peerj.3873 PubMed 29018611
October 10, 2017
Complete mitochondrial genome sequences of the northern spotted owl (Strix occidentalis caurina) and the barred owl (Strix varia; Aves: Strigiformes: Strigidae) confirm the presence of a duplicated control region
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https://doi.org/10.7717/peerj.3901 PubMed 29038757
October 2, 2017
Harnessing mtDNA variation to resolve ambiguity in ‘Redfish’ sold in Europe
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https://doi.org/10.7717/peerj.3746 PubMed 29018597
October 11, 2017
Evidence for continual hybridization rather than hybrid speciation between Ligularia duciformis and L. paradoxa (Asteraceae)
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https://doi.org/10.7717/peerj.3884 PubMed 29038755
October 17, 2017
MGST2 and WNT2 are candidate genes for comitant strabismus susceptibility in Japanese patients
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https://doi.org/10.7717/peerj.3935 PubMed 29062608
October 5, 2017
The Plasmodium berghei RC strain is highly diverged and harbors putatively novel drug resistance variants
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https://doi.org/10.7717/peerj.3766 PubMed 29018598
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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