Genomic Computing. Explanatory Analysis of Plant Expression Profiling Data Using Machine Learning
نویسندگان
چکیده
منابع مشابه
Genomic computing. Explanatory analysis of plant expression profiling data using machine learning.
As with every other organism whose genome has been sequenced (Hinton, 1997; Bork et al., 1998), a chief finding in plants (Bevan et al., 1999; Somerville and Somerville, 1999) is the presence of a vast number of genes (many with no relatives in the databases) whose existence, let alone function, had previously gone unrecorded. The importance of finding the function of these genes has led to wha...
متن کاملExplanatory analysis of spectroscopic data using machine learning of simple, interpretable rules
Whole organism or tissue profiling by vibrational spectroscopy produces vast amounts of seemingly unintelligible data. However, the characterisation of the biological system under scrutiny is generally possible only in combination with modern supervised machine learning techniques, such as artificial neural networks (ANNs). Nevertheless, the interpretation of the calibration models from ANNs is...
متن کاملAnalysis of Microarray Gene Expression Data Using Machine Learning Techniques
The advent of DNA microarrays has facilitated a fundamental transition from gene science to genome science. By performing massively parallel experiments on thousands of genes at once, scientists have, for the first time, the capability of observing the complex relationships between genes under controlled experimental conditions. However, the immense volume of data being generated by microarray ...
متن کاملEnsemble Feature Learning of Genomic Data Using Support Vector Machine
The identification of a subset of genes having the ability to capture the necessary information to distinguish classes of patients is crucial in bioinformatics applications. Ensemble and bagging methods have been shown to work effectively in the process of gene selection and classification. Testament to that is random forest which combines random decision trees with bagging to improve overall f...
متن کاملOral Cancer Prediction Using Gene Expression Profiling and Machine Learning
Oral premalignant lesion (OPL) patients have a high risk of developing oral cancer. In this study we investigate using machine learning techniques with gene expression profiling to predict the possibility of oral cancer development in OPL patients. Four classification techniques were used: support vector machine (SVM), Regularized Least Squares (RLS), multi-layer perceptron (MLP) with back prop...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Plant Physiology
سال: 2001
ISSN: 1532-2548,0032-0889
DOI: 10.1104/pp.126.3.943