Preprints (not yet peer-reviewed)

13 downloads
26 views

Machine learning is a field of study that uses computational and statistical techniques to enable computers to learn. When machine learning is applied, it functions as an instrument that can solve problems or expand knowledge about the surrounding world. Increasingly,...

["Artificial Intelligence","Computer Vision","Data Mining and Machine Learning","Data Science","Multimedia"]
doi:10.7287/peerj.preprints.27280v1
62 downloads
243 views

Translational models that utilize omics data generated in in vitro studies to predict the drug efficacy of anti-cancer compounds in patients are highly distinct, which complicates the benchmarking process for new computational approaches. In reaction to this, we...

["Computational Biology","Genomics","Statistics","Data Mining and Machine Learning"]
doi:10.7287/peerj.preprints.27256v1
336 downloads
795 views

Pathway and cell-type signatures are patterns present in transcriptome data that are associated with biological processes or phenotypic consequences. These signatures result from specific cell-type and pathway expression, but can require large transcriptomic compendia...

["Bioinformatics","Computational Biology","Genomics","Data Mining and Machine Learning"]
doi:10.7287/peerj.preprints.27229v1
26 downloads
66 views

This paper focuses on the recognition of Activities of Daily Living (ADL) applying pattern recognition techniques to the data acquired by the accelerometer available in the mobile devices. The recognition of ADL is composed by several stages, including data acquisition,...

["Algorithms and Analysis of Algorithms","Artificial Intelligence","Data Mining and Machine Learning","Data Science","Mobile and Ubiquitous Computing"]
doi:10.7287/peerj.preprints.27225v1
294 downloads
283 views

In the fifty years since Evarts first recorded single neurons in motor cortex of behaving monkeys, great effort has been devoted to understanding their relation to movement. Yet these single neurons exist within a vast network, the nature of which has been largely...

["Bioengineering","Neuroscience","Computational Science","Data Mining and Machine Learning"]
doi:10.7287/peerj.preprints.27217v1
53 downloads
126 views

Motivation: The identification of functional sequence variations in regulatory DNA regions is one of the major challenges of modern genetics. Here, we report results of a combined multifactor analysis of properties characterizing functional sequence variants located...

["Bioinformatics","Genomics","Data Mining and Machine Learning"]
doi:10.7287/peerj.preprints.27199v1
43 downloads
77 views

MAP-Elites is an evolutionary computation technique that has proven valuable for exploring and illuminating the genotype-phenotype space of a computational problem. In MAP-Elites, a population is structured based on phenotypic traits of prospective solutions; each...

["Adaptive and Self-Organizing Systems","Data Mining and Machine Learning"]
doi:10.7287/peerj.preprints.27154v1
85 downloads
140 views

Background. Functional groups serve two important functions in ecology, they allow for simplification of ecosystem models and can aid in understanding diversity. Despite their important applications, there has not been a universally accepted method of how to define...

["Ecology","Marine Biology","Statistics","Data Mining and Machine Learning","Data Science"]
doi:10.7287/peerj.preprints.27148v1
157 downloads
248 views

Predicting disease status for a complex human disease using genomic data is an important, yet challenging, step in personalized medicine. Among many challenges, the so-called curse of dimensionality problem results in unsatisfied performances of many state-of-art...

["Bioinformatics","Computational Biology","Genomics","Data Mining and Machine Learning","Data Science"]
doi:10.7287/peerj.preprints.27123v1
46 downloads
97 views

The increasing availability of open data and the demand to understand better the nature of anomalies and the causes underlying them in modern systems is encouraging researchers to analyse open datasets in various ways. These include both quantitative and qualitative...

["Data Mining and Machine Learning","Data Science","Security and Privacy","World Wide Web and Web Science"]
doi:10.7287/peerj.preprints.27116v1
16 downloads
59 views

Remote homology detection is the problem of detecting homology in cases of low sequence similarity. It is a hard computational problem with no approach that works well in all cases. Methods based on profile hidden Markov models (HMM) often exhibit relatively higher...

["Bioinformatics","Artificial Intelligence","Data Mining and Machine Learning","Data Science"]
doi:10.7287/peerj.preprints.27111v1
1,626 downloads
2,237 views

Random forest and similar Machine Learning techniques are already used to generate spatial predictions, but spatial location of points (geography) is often ignored in the modeling process. Spatial auto-correlation, especially if still existent in the cross-validation...

["Biogeography","Soil Science","Computational Science","Data Mining and Machine Learning","Spatial and Geographic Information Science"]
doi:10.7287/peerj.preprints.26693v3
104 downloads
142 views

With the popularization of the CRISPR-Cas gene editing system there has been an explosion of new techniques made possible by this versatile technology. However, the computational field has lagged behind with a current lack of computational tools for developing...

["Bioinformatics","Genomics","Synthetic Biology","Data Mining and Machine Learning"]
doi:10.7287/peerj.preprints.27094v1
128 downloads
438 views

The success of personalized medicine does not only rely on methodological advances but also on the availability of data to learn from. While the generation and sharing of large data sets is becoming increasingly easier, there is a remarkable lack of diversity within...

["Genomics","Neuroscience","Ethical Issues","Data Mining and Machine Learning","Data Science"]
doi:10.7287/peerj.preprints.27079v1
352 downloads
828 views

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

["Bioinformatics","Computational Biology","Data Mining and Machine Learning","Data Science"]
doi:10.7287/peerj.preprints.1479v4
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