AnVil Informatics – Application Systems – 6-14-01
This document briefly outlines some specific applications, which could be implemented
currently using techniques developed with AnVil technologies. These applications are to
be applied to specific problems in the bioinformatic or cheminformatic arenas.
For the most part the techniques have been tried or implemented using various tools
including AnVil’s Java platform, the new DS and other commercial or public domain
packages.
Outline of Specific Application Systems
1. Specific applied Classifiers - AnVil can evaluate and produce the best possible
classifier from 100 hundreds of possible models or classifiers. For example we
can generate most possible “state of the Art classifiers” such as “Support Vector
machines” , Random Forests or standard back-prop Neural Networks. The key
points are that to get the best possible classifier “data enhancement” is usually
necessary. In many cases a standard back-propogation NN may be the best or
unfortunately it may be impossible to get the accuracy desired.
2. A Radviz Gradual Visual Classifier – Training, Validation and testing any kind
of data can be made much more “user friendly” with interaction and the
“graduated classification” Radviz system.
3. Clustering and Specific Cluster Comparison – which clustering technique best
clusters your data for example to some known functional description. AnVil’s
proprietary cluster comparison methods will tell you which is best. A myriad of
clustering techniques can be applied such as Kohonen, K-means, Hiarchical, PCA
and variations.
4. Micro Array Gene Expression Disease Classifier– filtering, selection, MultiDisease Classifier. – This system can find the optimum gene set to diagnose two
or more disease states using various gene selection mechanisms such as AbsentPresent user selectiont (dendrogrammatic?) and RadViz differential selection,
PURS (Principle Uncorrelated Record Selection) and 3-way differential
clustering.
5. Quality Control MetaData Analysis System – Using a full statistical analysis,
multi-correlation and Multi-class analysis quality control data is analyzed and
visualized to get a high level overview of your data.
6. Data Cleaning, Impute Missings, Extraction and Organizing System - This
system is used to take a customers unformatted, dirty, and unorganized data and
produce a coherent database system which can be easily data mined.
7. Partial Subset Visual Classifier – On systems where classification accuracy is not
high enough, or a “black box” classifier is not satisfactory this system uses
“association rules” and RadViz to find “subset” areas in a dataset that can be
classified with a high accuracy.
8. Gene Correlation Searching/Analysis – based on our threee correlation methods
(Pearson , Jacknife and Cosine) we could build a database of gene correlation over
different experiments. For example in one experiment we have looked at the two genes
U11863(1713) and. U39400(2011) and found a very high negative correlation(-0.976).
A database query system could be built for customers with different correlation values for
different experiments.
Most of the above “systems” are “virtual” meaning that currently they can only be
implemented with a lot of “manual” work, but some or all of them could be implemented
(in varying degrees) into a “turnkey” system.
How much software development is necessary to implement some of these systems? Each
system would take at least a week to come up with a realistic estimate of the effort
involved.
Patrick Hoffman