Our mission statement is "You ask the questions,
we help you find the solutions !"
What we do ?
BioSieve is a bioinformatics software and bioinformatics service company,
founded to meet the challenges of biological data analysis, data mining
and data visualization/presentation. Our first product - ExpressionSieve
TM,
is for microarray data analysis. Combining our strength in statistical
analysis, software engineering, bioinformatics and complex data visualization,
we are able to provide excellent quality software products and user support
at an affordable cost.
Our strategies to tackle some of the challenges in microarray data
analysis?
Data visualization: We are not trying to present ready conclusions.
Instead, we are trying to help researchers in the field to quickly identify
interesting or important features inherent in the data and come to their
own conclusions, by providing "macroscope" and "microscope".
Data analysis process management: The main challenge in microarray
data analysis is to interpret the pattern recognition calculation results,
and extract biological meaningful information from them. So data analysis
software package should provide not only a computing environment, but
also a biological interpretation environment through integration with
biological knowlegebases, such as Genbank, SwissProt etc. The data analysis
process management system will help streamline the interpretation and
annotation process as well as facilitate analysis history tracking, analysis
results repository/retrieval and querying.
Annotation: Our innovative approach to pattern annotations through
gene ontology classification, interaction network/biological pathway analysis,
analysis of upstream regulatory elements and literature mining etc.,
include proprietary algorithms for data organization and visualization.
Combined with the process management system, it will give user a more integrated
platform for microarray data analysis.
Integrating data from other domains: Our product
tries to provide an integrated platform, where microarray data can be analyzed
along with data from other domains, such as gene function, biological pathway,
biochemical assay, and clinical data, thereby to arrive at more prudent conclusions
and more discoveries.
Pattern recognition algorithm: In addition to the public pattern
recognition algorithms is our proprietary graph theory based algorithm
that can solve the number-of-cluster problem in k-means analysis.
Speed and memory usage: For large data set such as all human genes
(~ 35,000 genes) X 200 experiments, analysis speed and memory usage will
become an issue. Most implementations require to store full similarity
matrix in memory, which means in this case, the similarity matrix alone
would consume more than 2 GB memory. In our implementation, there is no
need to store full matrix if memory is a problem, at the same time the running
time is made comparable to those implementations storing full similarity
matrix by using efficient algorithms.