Pinned Repositories
ATC
Anatomical Therapeutic Codes (ATC) are a drug classification system which is extensively used in the field of drug development research.
BayesSurprise
Bayesian surprise is the result of mismatches between our expectations and actual results, hence the degree of surprise or anomalousness attached to a pattern will vary with respect to these differences. The implication of obtaining large surprise values identifies those patterns likely to be useful and interesting to the user.
CommunityDetection
Community network discovery by weighting a random walk algorithm with ontological information
ComplexNetworks
complex network theory for identifying drug-target proteins. Undergoing some revisions and bug fixes.
DrugSideEffects
Drugs with similar side-effects are potential candidates for use elsewhere, the supposition is that similar side-effects may be caused by drugs targeting similar proteins.
motif
Using REGEX to seek patterns (motifs)
Paradox
Paradox detection in data for knowledge discovery
SBM
In this work we integrate complex networks and stochastic block models with heuristic reasoning for the purposes of data mining interesting patterns.
TextMiner
Turning messy data into tidy data. The majority of human knowledge and experience is in the form of the written word (messy data) and not structured databases (tidy data) which is required for machine learning algorithms.
UKCI2015-side-effects
The R work described in our conference paper presented at UKCI-2015 in Exeter, 7th-9th Sept. Drug development is a lengthy and highly costly endeavor, often with limited success and high risk. The objective of drug repositioning is to apply existing drugs to different diseases or medical conditions than the original target, and thus alleviate to a certain extent the time and cost expended.
kenmcgarry's Repositories
kenmcgarry/ATC
Anatomical Therapeutic Codes (ATC) are a drug classification system which is extensively used in the field of drug development research.
kenmcgarry/DrugSideEffects
Drugs with similar side-effects are potential candidates for use elsewhere, the supposition is that similar side-effects may be caused by drugs targeting similar proteins.
kenmcgarry/UKCI2015-side-effects
The R work described in our conference paper presented at UKCI-2015 in Exeter, 7th-9th Sept. Drug development is a lengthy and highly costly endeavor, often with limited success and high risk. The objective of drug repositioning is to apply existing drugs to different diseases or medical conditions than the original target, and thus alleviate to a certain extent the time and cost expended.
kenmcgarry/CommunityDetection
Community network discovery by weighting a random walk algorithm with ontological information
kenmcgarry/ComplexNetworks
complex network theory for identifying drug-target proteins. Undergoing some revisions and bug fixes.
kenmcgarry/TextMiner
Turning messy data into tidy data. The majority of human knowledge and experience is in the form of the written word (messy data) and not structured databases (tidy data) which is required for machine learning algorithms.
kenmcgarry/UKCI-2014-DiseaseNetworks
The R work described in our conference paper presented at UKCI-2014, in Bradford, 8th-10th Sept
kenmcgarry/UKCI2017-AR
Our knowledge of drug-to-drug interactions, side-effects and disease comorbidity is derived from healthcare record systems and these are now starting to receive increased attention as a way of improving public health and drug safety.
kenmcgarry/BayesSurprise
Bayesian surprise is the result of mismatches between our expectations and actual results, hence the degree of surprise or anomalousness attached to a pattern will vary with respect to these differences. The implication of obtaining large surprise values identifies those patterns likely to be useful and interesting to the user.
kenmcgarry/motif
Using REGEX to seek patterns (motifs)
kenmcgarry/Paradox
Paradox detection in data for knowledge discovery
kenmcgarry/SBM
In this work we integrate complex networks and stochastic block models with heuristic reasoning for the purposes of data mining interesting patterns.
kenmcgarry/BiologicalPlausibility
Bioinformatics algorithms need the ability to assess the relevance and biological plausibility of their discoveries.
kenmcgarry/Disease-Modules
The new science of complex networks has revealed the dynamic nature of diseases through shared genes and mechanisms.
kenmcgarry/Edgetics
Complex networks of SNP's - work in progress
kenmcgarry/GO-slim
comparision of GO-slim ontologies for building classifiers
kenmcgarry/Heuristics
Heuristics are often described as rules of thumb or short cuts to useful solutions that avoid lengthy or complex calculations.
kenmcgarry/Hypothesis
Hypothesis creation and testing in a data mining domain. We develop a reasoning system that is seeded with a base level of data mining knowledge and is capable of expanding, modifying and updating this knowledge with new experiences.
kenmcgarry/InterestingPatterns
One of the most insightful definitions of data mining states that to be truly successful data mining should be “the nontrivial process of identifying valid, novel, potentially useful, and ultimately comprehensible knowledge from databases”
kenmcgarry/NeuralNetworks
Using SVM, MLP and RBF for predicting protein targets
kenmcgarry/QSAR-hiv
Modelling QSAR compound data for affinity to binding with GP120/CD4 proteins.
kenmcgarry/Reinforcement
Integrating reinforcement learning within a cognitive framework for pattern detection
kenmcgarry/UKCI2016-Edgetics
The R work described in conference paper #1 presented at UKCI-2016 in Lancaster 7th-9th Sept. Complex networks are a graph theoretic method that can model genetic mutations, in particular single nucleotide polymorphisms (snp’s) which are genetic variations that only occur at single position in a DNA sequence.
kenmcgarry/UKCI2016-MCL
The R work described in conference paper #2 presented at UKCI-2016 in Lancaster 7th-9th Sept. The detection of protein complexes is an important research problem in bioinformatics, which may help increase our understanding of the biological functions of proteins inside our body.