Genetic breakthrough in multiple sclerosis -- biggest for decades

This is what data mining and predictive modeling is all about, a tool for subject matter experts to identify "new suspects". Once predictive modeling helps identify new suspects the subject matter experts apply their knowledge to determine whether this has value in their filed.

New processors present problems, payoff

The new challenge and opportunity in designing microprocessors is presented in this article. A new operating systems will be needed to optimized the utilization of these microprocessors. In my opinion the combination of data mining technologies that allow "automatic data mining (or predictive modeling) factories", intelligent agents, and parallel computing are going to be the fundamental blocks in addressing this challenge. Those technologies combined with gaming, simulation and other visualization technologies will be part of ingredients needed for this leap into the future.

Conceptually I think that it will be like this:

1. Data mining technologies will provide the fundamentals of pattern and error detection. Due to the complexity and diversity of the rich data environment that we currently face we will need the ability to have part of this technology embedded into any program, and we will probably need multiple and different data mining models analyzing data simultaneously so as to customize the needs of the end users;
2. Intelligent mobile agent technologies would be fundamental to access and process data from servers, mainframes, and handheld devices like cellphones;
3. Web based technologies will be fundamental in solving finding patterns and in improving remote communications;
4. Parallel computing technologies will be needed to optimize the processing of large quantities of data; and
5. Visualization technologies that make complex patterns easily understood, while simulateously adhering to establish laws of nature (i.e., medicine, or physics), or previous experience (business rules) would also be a keystone in this endevour.

Our biggest challenge is going to be to reach out acrross multiple disciplines and technologies to integrate all these technologies into a great schema. In this sense we are all pioneers. We bring different skills set that we combined will mark the path for others to follow. It will not be easy, but it will be worthwhile!

Understanding Molecular Imaging

GE Healthcare is correct in their assessment that if you can track molecular changes in cells and link them to disease progression an enterprise will be demostrating "the power of molecular imaging". I believe that web analytics algorithms and software is what is going to make the step possible. The reason is that web analytics algorithms allows to predict a variable (i.e., disease) given a series of inputs (medical procedures and other diagnoses) over a sequence.

Web Analytics and Healthcare: Disease Progression

We are starting to develop a heatlhcare model for disease progression prediction using Microsoft Sequence Clustering algorithm in SQL 2005 Server Analysis Services. It seems to work well, but I would like to make a comparison with other algorithms. I was wondering if anyone in the community knows how can we obtain Gooogle's permission to use (or adapt) their Web Analytics algorithm for disease progression prediction. Or if anyone has any other suggestion for Web Analytics software that we could try. We have the largest private payer healthcare database in the U.S. so we need robust algorithms.

Moving Closer To Solving Lou Gehrig's Disease Mystery

http://www.medicalnewstoday.com/medicalnews.php?newsid=75539

This is an area that I hope predictive modeling and data mining can make a difference. If we can do a linear disease progression modeling at the cellular level we might be able to diagnose and prevent ALS before its onset.

Web Analytics: Future Applications in Predicting Modeling

Web Analytics: Future Applications in Predicting Modeling

A lot of time and effort is being channeled in the area of web analytics. This terms refers to:

“[t]he measurement of data as it relates to an Internet site, including the behavior of visitors, the amount of traffic, the conversion rates, web server performance, user experience, and other information in order to understand and proof of results and continually improve the results of a site towards a set of objectives.”

Since web analytics is another area of predictive modeling, we must ask whether the methodologies, analytics software, and visualization tools develop in web analytics could have impact in other industries that use predictive modeling like healthcare, banking, insurance, retail, and manufacturing industries. I think that the processes and software developed for web analytics will ultimately be use in many other industries because the intersection of the Internet and other industries is already a reality.

Predictive modeling and web analytics have the same objective, to provide a measurement (or baseline) and to predict future behavior. One of the key contributions of web analytics has been software that can withstand the rigors of commercial use. The scalability components of web analytics are crucial for other industries in which large databases has become the norm.

Another significant issue that web analytics has contributed to the area of predictive modeling is the ability to come together and provide a series of metrics and benchmarks for the industry. Although some may disagree with this assessment, if we look at the history healthcare industry it apparent that the inability to agree upon benchmarks and metrics have negatively impacted the cost of healthcare in the United States. Moreover, those involved in web analytics could give industries like banking, insurance, and retail an innovative new look at what needs to be measured.

A third issue that web analytics have contributed to the issue of predictive analytics is the healthy, spirited, and robust exchange in the area of privacy. The Internet has created and raised serious, relevant, and pertinent questions regarding privacy that other industries could find beneficial.
A fourth area that web analytics has contributed to predictive modeling are the development of new return on investment (ROI) models in business. As companies adopt for these ROI models for their advertisement, new media, and marketing strategies they may find that these models are also applicable to other lines of businesses.

Last but not least, web analytics have contributed to a new set of visualization tools that summarize previously hidden nuggets of gold in a way that can be easily understood and act upon.

Geovisual Analytics and Crisis Management

good article about geovisual analytics. Good utilization of I2 and GIS technologies.

NIH-NSF Visualization Research Challenges Report

This article is for all the developers and scientists working on visualization tools in analytics and data mining. Enjoy!

BioGRID version 2.0.29 release ( maintenance update )

the latest release of BioGrid

What Data Mining Can and Can't Do

I include this article because Peter Fader is an expert in behavior predictive modeling. The caveat is that there is a difference between predicting behavior and predicting patterns that are not necessary related to behavior. For example, in molecular genetics we try to predict how a gene or a chemical substance have an impact on the physiology of a person. These reactions are mostly physical instead of behavior oriented. Nevertheless, I agree with Peter that executives expectations of data mining are out of proportion to the investment in predictive modeling. Predicting modeling using Excel spreadsheets might work on behavioral analysis, but I do not think it will work in the health care and biotechnology industries. When I was practicing law I used to refer to this as the "what is at stake syndrome". In a civil case is money, but in a criminal case is a person's freedom. Experience have taught me that generalizations make a great sound bite, but could be dangerous in the real world.

Evaluation of noise reduction techniques in the splice junction recognition problem

The authors have done a good job in evaluating noise reduction techniques using pre-processing algorithms in large genetic databases which are characterized by the presence of noisy data which can affect the performance of data mining processes.

A review of symbolic analysis of experimental data

This article suggest a time-series analysis as a way to reduce noise in large databases when doing analysis. My first impression was, "you must be kidding time series have nothing to do with noise reduction". Then I did an experiment using my 4.7 terabytes of data and I found that a time series analysis could detect the cause of noise in my sample data (or training set). When I re-read the article after the experiment I found that this methodology is for processes that are non-linear and possible chaotic. I am using healthcare data that is non-linear and chaotic. I found that the time-series analysis was a good methodology to identify noise in the training set for the data tags=1. I still need to do a lot of reverse engineering to understand the why, but in the meantime I thought this was worthwhile passing on.

Enhancing Data Analysis with Noise Removal

I am working on doing some noise reduction to an enterprise data mining model I thought that this was a good overall article on the different techniques applicable.

Incremental Mining of Sequential Patterns in Large Databases

The fundamentals of this algorithm could be use in large databases.

The problem: "As databases evolve the problem of maintaining sequential patterns over a significantly long period of time becomes essential, since a large number of new records may be added to a database. To reflect the current state of the database where previous sequential patterns would become irrelevant and new sequential patterns might appear, there is a need for efficient algorithms to update, maintain and manage the information discovered [12]. Several efficient algorithms for maintaining association rules have been developed [12–15]. Nevertheless, the problem of maintaining sequential patterns is much more complicated than maintaining association rules, since transaction cutting and sequence permutation have to be taken into account [16]."

The proposed solution: "This method is based on the discovery of frequent
sequences by only considering frequent sequences obtained by an earlier mining
step. By proposing an iterative approach based only on such frequent sequences
we are able to handle large databases without having to maintain negative border
information, which was proved to be very memory consuming [16]. Maintaining
such a border is well adapted to incremental association mining [26,19], where association rules are only intended to discover intra-transaction patterns (itemsets). Nevertheless, in sequence mining, we also have to discover inter-transaction patterns (sequences) and the set of all frequent sequences is an unbounded superset of the set of frequent itemsets (bounded) [16]. The main consequence is that such approaches are very limited by the negative border size."

Molecular Staging for Survival Prediction of Colorectal Cancer Patients

This article shows the potential of data mining for prognostic diseases using microarray (SAM) data.

Go Stanford! That's were one of my daughters graduated from and SAM is a product of Stanford University.

The treatment of missing values and its effect in the classifier accuracy

Good paper on the effects on missing values in the accuracy of your model. The organization of this paper could improve if the authors would have included their recommendation as part of the Summary.

Nevertheless, this is the crucial recommnedation (p.8): "We recommend that we can deal with datasets having up to 20 % of missing values. For the CD (Complete Deletion) method we have up to 60 % of instances containing missing
values and still have a reasonable performance."

For healthcare, pharma, and biotech data this paper is important because of the complexity and diversity of this data.

An Assessment of Accuracy, Error, and Conflict with Support Values from

This article is for experienced biostatisticians. Nevertheless, this is the interpretation for the layman:
When molecular biology theories are tested with real data we need to be cautious in reading bootstrap values if we are assuming an underestimation of the actual support. For example (my example is not in this article), if using a decision tree vs. logistic regression bayesian model, be cautious in how you assess the accuracy of your model since the decision-tree tends to understimate and bayesian models tend to overestimate.

I have found that to increase a classifier accuracy for a model, this type of distinction (non-parametric bootstrap values vs. Bayesian probabilities) is fundamental.

Phase II Studies: Which is Worse, False Positive or False Negative

A short but powerful article that helps understands the effects of Type I and Type II errors in clinical trials.

SPSS Launches Enhanced Predictive Analytics Platform

I have not tried this product yet, but SPSS tend to have good products in predictive modeling.

The Advantages of Smart Data Mining

Good general article about data mining in the retail and POS indsutry. Free registration required.