Dataset Information

Improvement of Neoantigen Identification Through Convolution Neural Network.

ABSTRACT: Accurate prediction of neoantigens and the subsequent elicited protective anti-tumor response are particularly important for the development of cancer vaccine and adoptive T-cell therapy. However, current algorithms for predicting neoantigens are limited by in vitro binding affinity data and algorithmic constraints, inevitably resulting in high false positives. In this study, we proposed a deep convolutional neural network named APPM (antigen presentation prediction model) to predict antigen presentation in the context of human leukocyte antigen (HLA) class I alleles. APPM is trained on large mass spectrometry (MS) HLA-peptides datasets and evaluated with an independent MS benchmark. Results show that APPM outperforms the methods recommended by the immune epitope database (IEDB) in terms of positive predictive value (PPV) (0.40 vs. 0.22), which will further increase after combining these two approaches (PPV = 0.51). We further applied our model to the prediction of neoantigens from consensus driver mutations and identified 16,000 putative neoantigens with hallmarks of 'drivers'.

SUBMITTER: Hao Q

PROVIDER: S-EPMC8186784 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Improvement of Neoantigen Identification Through Convolution Neural Network.

Hao Qing Q Wei Ping P Shu Yang Y Zhang Yi-Guan YG Xu Heng H Zhao Jun-Ning JN

Frontiers in immunology 20210525

Accurate prediction of neoantigens and the subsequent elicited protective anti-tumor response are particularly important for the development of cancer vaccine and adoptive T-cell therapy. However, current algorithms for predicting neoantigens are limited by <i>in vitro</i> binding affinity data and algorithmic constraints, inevitably resulting in high false positives. In this study, we proposed a deep convolutional neural network named APPM (antigen presentation prediction model) to predict anti ...[more]

PMID: 34113354

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Project description:The Bemisia tabaci species complex is a group of tropical-subtropical hemipterans, some species of which have achieved global distribution over the past 150 years. Several species are regarded currently as among the world's most pernicious agricultural pests, causing a variety of damage types via direct feeding and plant-disease transmission. Long considered a single variable species, genetic, molecular and reproductive compatibility analyses have revealed that this "species" is actually a complex of between 24 and 48 morphologically cryptic species. However, determinations of which populations represent distinct species have been hampered by a failure to integrate genetic/molecular and morphological species-diagnoses. This, in turn, has limited the success of outbreak-control and eradication programs. Previous morphological investigations, based on traditional and geometric morphometric procedures, have had limited success in identifying genetic/molecular species from patterns of morphological variation in puparia. As an alternative, our investigation focused on exploring the use of a deep-learning convolution neural network (CNN) trained on puparial images and based on an embedded, group-contrast training protocol as a means of searching for consistent differences in puparial morphology. Fifteen molecular species were selected for analysis, all of which had been identified via DNA barcoding and confirmed using more extensive molecular characterizations and crossing experiments. Results demonstrate that all 15 species can be discriminated successfully based on differences in puparium morphology alone. This level of discrimination was achieved for laboratory populations reared on both hairy-leaved and glabrous-leaved host plants. Moreover, cross-tabulation tests confirmed the generality and stability of the CNN discriminant system trained on both ecophenotypic variants. The ability to identify B. tabaci species quickly and accurately from puparial images has the potential to address many long-standing problems in B. tabaci taxonomy and systematics as well as playing a vital role in ongoing pest-management efforts. [Aleyrodidae; entomology; Hemiptera; machine learning; morphometrics; pest control; systematics; taxonomy; whiteflies.].

Dataset Information

Improvement of Neoantigen Identification Through Convolution Neural Network.

Publications

Improvement of Neoantigen Identification Through Convolution Neural Network.

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