Preprints (not yet peer-reviewed)

3 downloads
43 views

We present in this article a lightweight ontology named PGxO and a set of rules for its instantiation, which we developed as a frame for reconciling and tracing pharmacogenomics (PGx) knowledge. PGx studies how genomic variations impact variations in drug response...

["Bioinformatics","Artificial Intelligence","Data Mining and Machine Learning","Databases","World Wide Web and Web Science"]
doi:10.7287/peerj.preprints.3140v1
64 downloads
195 views

Metabarcoding and metagenomic approaches are becoming routine techniques in biodiversity assessment and ecological studies. The assignment of taxonomic information to sequences is challenging, as many reference libraries are lacking information on certain taxonomic...

["Biodiversity","Bioinformatics","Ecology","Molecular Biology","Data Mining and Machine Learning"]
doi:10.7287/peerj.preprints.3133v1
54 downloads
64 views

Minor syntax errors are made by novice and experienced programmers alike; however, novice programmers lack the years of intuition that help them resolve these tiny errors. Standard LR parsers typically resolve syntax errors and their precise location poorly. We...

["Data Mining and Machine Learning","Software Engineering"]
doi:10.7287/peerj.preprints.3123v1
62 downloads
661 views

The nonparametric minimum hypergeometric (mHG) test is a popular alternative to Kolmogorov-Smirnov (KS)-type tests for determining gene set enrichment. However, these approaches have not been compared to each other in a quantitative manner. Here, I first perform...

["Computational Biology","Algorithms and Analysis of Algorithms","Data Mining and Machine Learning"]
doi:10.7287/peerj.preprints.1962v3
177 downloads
386 views

Background. The availability of large databases containing high resolution three-dimensional (3D) models of proteins in conjunction with functional annotation allows the exploitation of advanced supervised machine learning techniques for automatic protein function...

["Bioinformatics","Computational Biology","Data Mining and Machine Learning"]
doi:10.7287/peerj.preprints.2778v1
14 downloads
111 views

Data mining is one of the main activities in bioinformatics, specifically to extract knowledge from massive data sets related with gene expression measurement, CNV, DNA strings, and others. A long array of methods are used to perform such task, ranging from the...

["Bioinformatics","Computational Biology","Algorithms and Analysis of Algorithms","Data Mining and Machine Learning","Optimization Theory and Computation"]
doi:10.7287/peerj.preprints.2635v1
42 downloads
256 views

Sparse coding is an effective operating principle for the brain, one that can guide the discovery of features and support the learning of assocations. Here we show how spiking neurons with discrete dendrites can learn sparse codes via an online, nonlinear Hebbian...

["Computational Biology","Adaptive and Self-Organizing Systems","Artificial Intelligence","Data Mining and Machine Learning"]
doi:10.7287/peerj.preprints.2595v1
115 downloads
394 views

Forest trees cover just over 30% of the earth's surface and are studied by researchers around the world for both their conservation and economic value. With the onset of high throughput technologies, tremendous phenotypic and genomic data sets have been generated...

["Bioinformatics","Data Mining and Machine Learning","Databases"]
doi:10.7287/peerj.preprints.2345v4
70 downloads
482 views

Deregulation of miRNAs is implicated in many diseases in particular cancer, where miRNAs can act as tumour suppressors or oncogenes. As sequence-based miRNA target predictions do not provide condition-specific information, many algorithms combine expression data...

["Bioinformatics","Computational Biology","Data Mining and Machine Learning"]
doi:10.7287/peerj.preprints.2377v2
45 downloads
381 views

Cardiotocography is currently the standard surveillance tool during labour. For this reason, there are large cardiotography datasets available. Several studies gathering additional information of computerized CTG data aiming to improve surveillance during delivery...

["Bioinformatics","Computational Biology","Data Mining and Machine Learning","Data Science","Software Engineering"]
doi:10.7287/peerj.preprints.2098v2
25 downloads
112 views

A traditional random variable X is a function that maps from a stochastic process to the real line (X,<=,d,+,.), where R is the set of real numbers, <= is the standard linear order relation on R, d(x,y)=|x-y| is the usual metric on R, and (R, +, .) is the standard...

["Bioinformatics","Computational Biology","Data Mining and Machine Learning","Data Science"]
doi:10.7287/peerj.preprints.2052v1
631 downloads
824 views

Efficient methods of biodiversity assessment and monitoring are central to ecological research and crucial in conservation management. Technological advances in remote acoustic sensing inspire new perspectives in ecology: environmental sound monitoring is emerging...

["Bioinformatics","Adaptive and Self-Organizing Systems","Data Mining and Machine Learning","Data Science"]
doi:10.7287/peerj.preprints.1407v3
158 downloads
377 views

Computational models in biology encode molecular and cell biological processes. Many of them can be represented as biochemical reaction networks. Studying such networks, one is often interested in systems that share similar reactions and mechanisms. Typical goals...

["Bioinformatics","Computational Biology","Data Mining and Machine Learning","Data Science"]
doi:10.7287/peerj.preprints.1479v2
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