Bioinformatics - The Machine Learning Approach

By Gregory R. Grant. 2000

793 citations recorded by the source corpus.

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Citations in the corpus

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2016, background): “, in bio-informatics [7] and climate science [8], [9]).”
Analysis of the IJCNN 2007 agnostic learning vs. prior knowledge challenge (2008, background): “This may be exploited in specialized kernels used with kernel machines [3].”
Reliable prediction of T‐cell epitopes using neural networks with novel sequence representations (2003, methodology): “The neural network architecture used is a conventional feed-forward feed-forward network (Baldi and Brunak 2001) with an input layer with 180 neurons, one hidden layer with 2–10 neurons, and a single neuron output layer.”
Predicting protein-protein interactions from primary structure (2001, methodology): “…function approximation, signal processing and regression, SVM has several advantages as applied in the present context: (1) SVM generates a representation of the non-linear mapping from residue sequence to protein fold space (Baldi and Brunak, 1998) using relatively few adjustable model parameters.”
Improved Statistical Inference from DNA Microarray Data Using Analysis of Variance and A Bayesian Statistical Framework (2001, background): “statistical approach (19) that incorporates prior information of”
Machine learning in critical care: state-of-the-art and a sepsis case study (2018, background): “These fields include bioinformatics [9, 10], genetics and genomics [11, 12], clinical applications [13], medical decision support and clinical diagnosis [14, 15], oncology [16, 17], psychiatry and neurological disorders [18, 19], or cytopathology [20].”
Machine Learning Methods for Protein Structure Prediction (2008, background): “D reconstruction algorithms that can tolerate the noise contained in predicted contact maps.”
Direct importance estimation for covariate shift adaptation (2008, background): “2008), bioinformatics (Baldi and Brunak 1998; Borgwardt et al. 2006), spam filtering (Bickel and Scheffer 2007), brain-computer interfacing (Wolpaw et al.”
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, methodology): “In the last thirty years, ML has been successfully applied to the field of computer vision [13], automatic control [14], bioinformatics [15], etc.”
Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation (2009, background): “bioinformatics [1, 5], spam filtering [4], natural language processing [14], brain-computer interfacing [28, 22], or econometrics [12], covariate shift is conceivable.”
Graph kernels for chemical informatics (2005, methodology): “Graphical models, such as Hidden Markov Models, have been very successful in bioinformatics, for instance, in order to model biological sequences [Baldi and Brunak, 2001].”
Current methods for the prediction of T‐cell epitopes (2018, background): “ML is best suited to situations in computational biology, where a large amount of peptide data is available with many missing or wrong facts also with a risk of having a high degree of uncertainty in selecting the appropriate models with numerous parameters.([144]) This technique is further classifi”