AIMC Topic: Oryza

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KPRR: a novel machine learning approach for effectively capturing nonadditive effects in genomic prediction.

Briefings in bioinformatics
Nonadditive genetic effects pose significant challenges to traditional genomic selection methods for quantitative traits. Machine learning approaches, particularly kernel-based methods, offer promising solutions to overcome these limitations. In this...

RiceSNP-BST: a deep learning framework for predicting biotic stress-associated SNPs in rice.

Briefings in bioinformatics
Rice consistently faces significant threats from biotic stresses, such as fungi, bacteria, pests, and viruses. Consequently, accurately and rapidly identifying previously unknown single-nucleotide polymorphisms (SNPs) in the rice genome is a critical...

Panicle Ratio Network: streamlining rice panicle measurement by deep learning with ultra-high-definition aerial images in the field.

Journal of experimental botany
The heading date and effective tiller percentage are important traits in rice, and they directly affect plant architecture and yield. Both traits are related to the ratio of the panicle number to the maximum tiller number, referred to as the panicle ...

iDHS-Deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network.

Briefings in bioinformatics
DNase I hypersensitive site (DHS) refers to the hypersensitive region of chromatin for the DNase I enzyme. It is an important part of the noncoding region and contains a variety of regulatory elements, such as promoter, enhancer, and transcription fa...

Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework.

Briefings in bioinformatics
DNA N6-methyladenine (6mA) represents important epigenetic modifications, which are responsible for various cellular processes. The accurate identification of 6mA sites is one of the challenging tasks in genome analysis, which leads to an understandi...

Prediction of Rice Transcription Start Sites Using TransPrise: A Novel Machine Learning Approach.

Methods in molecular biology (Clifton, N.J.)
As the interest in genetic resequencing increases, so does the need for effective mathematical, computational, and statistical approaches. One of the difficult problems in genome annotation is determination of precise positions of transcription start...

[From genome analysis to construction of an integrated omics knowledgebase for crops].

Yi chuan = Hereditas
The advances in high-throughput technologies have enabled high-speed accumulation of omics data, which contain a large amount of genetic variations and their functional information. The integration and deep utilization of those data will be a long-te...

TSSPlant: a new tool for prediction of plant Pol II promoters.

Nucleic acids research
Our current knowledge of eukaryotic promoters indicates their complex architecture that is often composed of numerous functional motifs. Most of known promoters include multiple and in some cases mutually exclusive transcription start sites (TSSs). M...

Estimating the leaf nitrogen content of paddy rice by using the combined reflectance and laser-induced fluorescence spectra.

Optics express
Paddy rice is one of the most important crops in China, and leaf nitrogen content (LNC) serves as a significant indictor for monitoring crop status. A reliable method is needed for precise and fast quantification of LNC. Laser-induced fluorescence (L...

[Identification of ustiloxins in false smut balls of rice based on high performance liquid chromatography-high resolution mass spectrometry].

Se pu = Chinese journal of chromatography
A sensitive method was developed for the simultaneous identification of five ustiloxins in the false smut balls of rice by high performance liquid chromatography-linear ion trap/orbitrap high resolution mass spectrometry (HPLC-LTQ/Orbitrap MS). The s...