AIMC Topic: Sequence Analysis, DNA

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Optimizing ChIP-seq peak detectors using visual labels and supervised machine learning.

Bioinformatics (Oxford, England) Feb 15, 2017
MOTIVATION: Many peak detection algorithms have been proposed for ChIP-seq data analysis, but it is not obvious which algorithm and what parameters are optimal for any given dataset. In contrast, regions with and without obvious peaks can be easily l...
Humans Software Animals Genomics Sequence Analysis, DNA Supervised Machine Learning Chromatin Immunoprecipitation
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AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields.

Bioinformatics (Oxford, England) Sep 1, 2016
MOTIVATION: Protein intrinsically disordered regions (IDRs) play an important role in many biological processes. Two key properties of IDRs are (i) the occurrence is proteome-wide and (ii) the ratio of disordered residues is about 6%, which makes it ...
Neural Networks, Computer Machine Learning Forecasting Proteome Sequence Analysis, DNA Area Under Curve
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Genetics. Predicting the impact of genomic variation.

Nature methods Mar 1, 2016
Humans Computer Simulation Models, Genetic Sequence Analysis, DNA Models, Statistical Unsupervised Machine Learning Genetic Variation Base Sequence Genome, Human Molecular Sequence Data Chromosome Mapping
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GI-SVM: A sensitive method for predicting genomic islands based on unannotated sequence of a single genome.

Journal of bioinformatics and computational biology Feb 1, 2016
Genomic islands (GIs) are clusters of functionally related genes acquired by lateral genetic transfer (LGT), and they are present in many bacterial genomes. GIs are extremely important for bacterial research, because they not only promote genome evol...
Genomics Support Vector Machine Sequence Analysis, DNA Pseudomonas aeruginosa Salmonella typhi Genomic Islands Genome, Bacterial Gene Transfer, Horizontal Corynebacterium diphtheriae
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