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This module introduces a short course on the applications of statistical machine learning to three problems in computational bioogy: genefinding, molecular classification of cancers using microarray data, and inferring signaling and regulatory networks from microarray, and protein interaction data. The three statistical machine learning methods covered are: hidden Markov models, support vector machines and Bayesian network learning.

Inspiration for the module

The design of this course module is inspired by the following quote from Nobelist Stanley Fields in Science:February 16, 2001:1221-1224:

Deciphering how a mere 10 7 nucleotides result in a yeast cell, let alone how 3 10 9 nucleotides result in a human -- cannot begin until the genes have been annotated. This step includes figuring out the proteins these genes encode and what they do for a living. But understanding how all of these proteins collaborate to carry out cellular processes is the real enterprise at end.
The quest, indeed, is for the wiring diagrams of life; particularly how they are altered in diseased cells. A fine example is the well-known cancer subway map by William C. Hahn and Robert A. Weinberg published in Nature Reviews Cancer in 2002.

The cancer subway map

The Cancer Subway Map (from http://www.nature.com/nrc/poster/subpathways/).

Biology in the 21st century

Biology is awash in data today. We have ready access to the sequences of the human and other genomes, structures for several thousand proteins, sequences for over 1.5 million proteins, and information on thousands of protein-protein interactions (with over a billion interactions predicted). High-throughput assays such as microarrays, flow cytometry, SELDI-TOF spectra, cell-level imaging, and array CGH give us unprecedented access to the functioning of cells. All of this data needs to be interpreted to reveal models of cellular function so that we can understand the molecular basis of disease, and design appropriate therapeutic interventions. Data driven exploration of theories is the standard scientific paradigm in modern biology. However, these new technologies have accelerated the volume and pace at which data can be gathered, requiring significant use of computation. Further, it has allowed the focus of research to shift from individual components of a cell to system-level analysis. The situation is depicted nicely in the following cartoon taken from the Fields article in Science:February 16, 2001:1221-1224.

Fishing from the molecular biology pond.

The new world of computational biology (from http://www.sciencemag.org/cgi/content/full/291/5507/1221/F1)

Statistical machine learning

What is statistical machine learning? It is the science of understanding complex systems (such as cells) by actively gathering data from them, synthesizing the data using prior knowledge (if any) of the systems to form models, and using the models to predict responses of the system to interventions. The fundamental questions in machine learning are:

  • Feature Selection: What aspects of the system should be observed?
  • Model Selection: What class of models need to be built from observed data and prior knowledge?
  • Model validation: How do we evaluate the efficacy of the learned models?

Statistical machine learning

Observe complex systems and build models to predict their response to interventions.

Three problems from computational biology

The short course focuses on three central problems in computational biology that are at three different levels of abstraction.

  • Computational Genefinding: Given a DNA sequence, find and annotate genes in it.
  • Molecular fingerprinting of disease: Given micro-RNA expression levels in normal and diseased cells, find biologically significant genes that are differentially expressed.
  • Learning signaling and regulatory networks: Given flow cytometry data from normal and diseased cells, learn signaling networks from them.

Here is the computational genefinding problem cast in the framework of statistical machine learning. The observational data are annotated stretches of a genome, and the models learned are Hidden Markov models which are sequential models for labeling new DNA segments.

Computational genefinding

Computational genefinding as a statistical machine learning problem.

Here is the molecular fingerprinting or the biomarker discovery problem cast in the framework of statistical machine learning. The observational data are the mRNA or proteomic expression data from diseased and normal cells, and the model is a classifier that discriminates between them, and identifies the key genes or proteins that are key to classification.

Molecular fingerprinting of disease

Molecular fingerprinting of disease as a statistical machine learning problem.

Here is the problem of learning cell signaling networks from flow cytometry data cast as a statistical machine learning problem. The observational data are the measurements at a given time of the levels of signaling molecules in a cell, prior knowledge could be known interactions between the molecules, and the predictive model learned in a signaling network that is the best fit to the data. Therapeutic interventions can then be planned on the learned model. Since there are tremendous individual variations in cells with cancer, an ab-initio technique for inferring signaling pathways directly from observational data can pave the way for the era of personalized cancer therapy.

Learning signaling networks from data.

Learning cell signaling networks from data.

Three statistical machine learning algorithms will be introduced in the context of these three problems.

  • Hidden Markov models and variants.
  • k-NN classifiers and support vector machines.
  • Bayesian networks: learning parameters and structure.

Module objectives

The module objectives are:

  • To show how to handle heterogeneous biological data, how to formulate biological problems in the statistical machine learning framework, and how to choose appropriate algorithms for these problems.
  • To cover the basics of supervised and sequential machine learning algorithms with particular focus on Hidden Markov Models, k-NN and SVM classifiers, and Bayesian networks.
  • To provide opportunities to apply these methods in the context of real data (human chromosome 22, prostate cancer gene expression data, and flow cytometry data from T-cell signaling).

Questions & Answers

Is there any normative that regulates the use of silver nanoparticles?
Damian Reply
what king of growth are you checking .?
What fields keep nano created devices from performing or assimulating ? Magnetic fields ? Are do they assimilate ?
Stoney Reply
why we need to study biomolecules, molecular biology in nanotechnology?
Adin Reply
yes I'm doing my masters in nanotechnology, we are being studying all these domains as well..
what school?
biomolecules are e building blocks of every organics and inorganic materials.
anyone know any internet site where one can find nanotechnology papers?
Damian Reply
sciencedirect big data base
Introduction about quantum dots in nanotechnology
Praveena Reply
what does nano mean?
Anassong Reply
nano basically means 10^(-9). nanometer is a unit to measure length.
do you think it's worthwhile in the long term to study the effects and possibilities of nanotechnology on viral treatment?
Damian Reply
absolutely yes
how to know photocatalytic properties of tio2 nanoparticles...what to do now
Akash Reply
it is a goid question and i want to know the answer as well
characteristics of micro business
for teaching engĺish at school how nano technology help us
Do somebody tell me a best nano engineering book for beginners?
s. Reply
there is no specific books for beginners but there is book called principle of nanotechnology
what is fullerene does it is used to make bukky balls
Devang Reply
are you nano engineer ?
fullerene is a bucky ball aka Carbon 60 molecule. It was name by the architect Fuller. He design the geodesic dome. it resembles a soccer ball.
what is the actual application of fullerenes nowadays?
That is a great question Damian. best way to answer that question is to Google it. there are hundreds of applications for buck minister fullerenes, from medical to aerospace. you can also find plenty of research papers that will give you great detail on the potential applications of fullerenes.
what is the Synthesis, properties,and applications of carbon nano chemistry
Abhijith Reply
Mostly, they use nano carbon for electronics and for materials to be strengthened.
is Bucky paper clear?
carbon nanotubes has various application in fuel cells membrane, current research on cancer drug,and in electronics MEMS and NEMS etc
so some one know about replacing silicon atom with phosphorous in semiconductors device?
s. Reply
Yeah, it is a pain to say the least. You basically have to heat the substarte up to around 1000 degrees celcius then pass phosphene gas over top of it, which is explosive and toxic by the way, under very low pressure.
Do you know which machine is used to that process?
how to fabricate graphene ink ?
for screen printed electrodes ?
What is lattice structure?
s. Reply
of graphene you mean?
or in general
in general
Graphene has a hexagonal structure
On having this app for quite a bit time, Haven't realised there's a chat room in it.
what is biological synthesis of nanoparticles
Sanket Reply
how did you get the value of 2000N.What calculations are needed to arrive at it
Smarajit Reply
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Source:  OpenStax, Statistical machine learning for computational biology. OpenStax CNX. Oct 14, 2007 Download for free at http://cnx.org/content/col10455/1.2
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