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State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
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MAPK Signaling Cascades

Mitogen-activated protein kinase, or MAPK pathway, activates three sequential kinases to regulate cellular responses such as proliferation, differentiation, survival, and apoptosis. The canonical MAPK pathway starts with a mitogen or growth factor binding to an RTK. The activated RTKs stimulate Ras, which recruits Raf or MAP3 Kinase (MAPKKK), the first kinase of the MAPK signaling cascade. Raf further phosphorylates and activates MEK or MAP2 Kinases (MAPKK), which in turn phosphorylates MAP...
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Maxam-Gilbert Sequencing

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Masking and Demasking Agents01:19

Masking and Demasking Agents

EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
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Hückel's Rule Diagram of π MOs: Frost Circle01:08

Hückel's Rule Diagram of π MOs: Frost Circle

The Frost circle or the inscribed polygon method is a graphical method for determining the relative energies of π molecular orbitals (MOs) for planar, fully conjugated, and monocyclic compounds. This method was first described by A. A. Frost and Boris Musulin in 1953.
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Microtubule Associated Proteins (MAPs)

Microtubule function and architecture are regulated by an array of specialized proteins called microtubule-associated proteins or MAPs. These proteins are widespread across different organisms and have conserved protein motifs, like the multi-TOG domain for tubulin binding found in the CLASP family of MAPs. Some MAPs are lineage-specific based on their conserved domains. Their functions depend upon the cytoskeletal architecture and cell type they are located within. In-plant cells, a specific...

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Self-Organizing Hidden Markov Model Map (SOHMMM).

Christos Ferles1, Andreas Stafylopatis

  • 1Intelligent Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Zografou, Athens, Greece.

Neural Networks : the Official Journal of the International Neural Network Society
|September 5, 2013
PubMed
Summary
This summary is machine-generated.

A novel Self-Organizing Hidden Markov Model Map (SOHMMM) integrates unsupervised and dynamic programming methods for analyzing biological sequences like DNA and RNA. This approach enables effective clustering, search, and classification of large sequence datasets with minimal prior knowledge.

Keywords:
ClusteringDNA/RNA/protein sequencesHidden Markov Model (HMM)Self-Organizing Map (SOM)SpatiotemporalUnsupervised learning

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Biological sequence analysis (DNA, RNA, protein) presents challenges due to large data volumes and complexity.
  • Existing methods may require significant prior knowledge or lack adaptability for diverse sequence properties.

Purpose of the Study:

  • To introduce the Self-Organizing Hidden Markov Model Map (SOHMMM), a hybrid model combining Self-Organizing Maps (SOM) and Hidden Markov Models (HMM).
  • To develop an integrated, on-line gradient descent unsupervised learning algorithm for probabilistic sequence analysis.
  • To address the increasing demands of analyzing complex biological sequence data.

Main Methods:

  • Fusion of SOM's unsupervised learning with HMM's dynamic programming algorithms.
  • Development of an integrated on-line gradient descent unsupervised learning algorithm within the SOHMMM framework.
  • Probabilistic sequence analysis with minimal prior knowledge.

Main Results:

  • Demonstrated the SOHMMM's capability for probabilistic sequence analysis.
  • Showcased applications in clustering, dimensionality reduction, and visualization of large-scale sequence spaces.
  • Validated performance through experiments on artificial and splice junction gene sequences.

Conclusions:

  • The SOHMMM offers a powerful, integrated approach for analyzing biological sequences.
  • Its unsupervised learning and probabilistic nature make it suitable for diverse sequence-related tasks including discrimination, search, and classification.
  • The SOHMMM effectively handles large-scale sequence data, providing valuable insights with limited prior information.