For each subsequence a score value (probability) was calculated by a machine learning classifier.
The work of [23] also deployed machine learning for the advertisement classification problem, by training a supervised learning classifier on labeled data (based on phone numbers of known traffickers) provided by a victim advocacy group.
Finally, by training a machine learning classifier to distinguish common, context-independent sites from cell-type specific sites, we show that site strength and clustering are the most important parameters for identifying context-independent TFBS.
In the glioblastoma and pancreatic cancer studies by the same research group, a machine learning classifier using a random forest algorithm, LSMUT, was developed to predict the functional impact of the non-synonymous mutations (Jones et al. 2008; Parsons et al. 2008).
We present a structure-based method for predicting class II epitopes that combines molecular mechanics docking of a fully flexible peptide into the MHC binding cleft followed by binding affinity prediction using a machine learning classifier trained on interaction energy components calculated from the docking solution.
Another notable TF identified by our machine learning classifier study of Drosophila heart enhancers is Su(H), an integral component of the Notch signaling pathway the presence of which we found unexpectedly discriminates between PC and CC regulatory elements.
An individual-based artificial ecosystem model is proposed, which is designed to explore the evolvability of adaptive behavioural strategies in artificial bacteria represented by rule-based learning classifier systems.
This will also have to address the interpretation of the decisive feature vector used by the machine learning classifier.
Regardless of the total size of the data which potentially amount to infinity, the rt-CDSS which is empowered by the incremental learning classifier will still work fine.
The networks used by the machine learning classifier for intrusive memory prediction are in line with neurocircuitry models of PTSD patients (Admon et al., 2013; Rauch et al., 2006): hyper-responsivity in the amygdala and associated limbic regions involved in emotional processing and the dorsal anterior cingulate cortex have been found in PTSD samples.
