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Biomedical subjects

Derong Liu

Publications and source records attributed to Derong Liu.

7 recordsLinked to original sources
PubMed2006

Motif discoveries in unaligned molecular sequences using self-organizing neural networks.

In this paper, we study the problem of motif discoveries in unaligned DNA and protein sequences. The problem of motif identification in DNA and protein sequences has been studied for many years in the literature. Major hurdles at this point include computational complexity and reliability of the search algorithms. We propose a self-organizing neural network structure for solving the problem of motif identification in DNA and protein sequences. Our network contains several layers, with each layer performing classifications at different levels. The top layer divides the input space into a small number of regions and the bottom layer classifies all input patterns into motifs and nonmotif patterns. Depending on the number of input patterns to be classified, several layers between the top layer and the bottom layer are needed to perform intermediate classifications. We maintain a low computational complexity through the use of the layered structure so that each pattern's classification is performed with respect to a small subspace of the whole input space. Our self-organizing neural network will grow as needed (e.g., when more motif patterns are classified). It will give the same amount of attention to each input pattern and will not omit any potential motif patterns. Finally, simulation results show that our algorithm outperforms existing algorithms in certain aspects. In particular, simulation results show that our algorithm can identify motifs with more mutations than existing algorithms. Our algorithm works well for long DNA sequences as well.

Amino Acid Sequence↗
PubMed2005

On the global output convergence of a class of recurrent neural networks with time-varying inputs.

This paper studies the global output convergence of a class of recurrent neural networks with globally Lipschitz continuous and monotone nondecreasing activation functions and locally Lipschitz continuous time-varying inputs. We establish two sufficient conditions for global output convergence of this class of neural networks. Symmetry in the connection weight matrix is not required in the present results which extend the existing ones.

Algorithms↗
PubMed2005

Restriction mapping of the IGS region in Alternaria spp. reveals variable and conserved domains.

Accurate identification of Alternaria spp. is dependent upon the production of diagnostic morphological characters under defined cultural conditions and the proper assessment of character variation. This process is often compromised by variation in laboratory facilities and technical expertise. To assist taxon identification and phylogenetic studies, restriction site information from the intergenic spacer (IGS) region of nuclear rDNA was evaluated. Restriction maps were constructed from 15 species of Alternaria and Stemphylium botryosum (telemorph Pleospora herbarum) for 11 restriction enzymes using a new method for restriction mapping based on differential priming of IGS amplicons. IGS fragment size varied among species from 2.2-3.9 kb. Based upon restriction site homology among closely-related and more distantly related species, the IGS region could be divided into conserved and variable domains. The conserved domain was approximately 0.75 kb in size and was located at the 3' end of the IGS region. Restriction site homology within this region was very high, especially among closely related taxa. The remainder of the region comprised the variable domain, which encompassed considerable differences in size and restriction sites among taxa. The presence or absence of restriction sites among taxa was analyzed using methods of neighbor-joining. Phylogenetic relationships based on this method were concordant with those previously resolved based upon other methods and other genomic regions.

Alternaria↗
PubMed2005

A self-learning call admission control scheme for CDMA cellular networks.

In the present paper, a call admission control scheme that can learn from the network environment and user behavior is developed for code division multiple access (CDMA) cellular networks that handle both voice and data services. The idea is built upon a novel learning control architecture with only a single module instead of two or three modules in adaptive critic designs (ACDs). The use of adaptive critic approach for call admission control in wireless cellular networks is new. The call admission controller can perform learning in real-time as well as in offline environments and the controller improves its performance as it gains more experience. Another important contribution in the present work is the choice of utility function for the present self-learning control approach which makes the present learning process much more efficient than existing learning control methods. The performance of our algorithm will be shown through computer simulation and compared with existing algorithms.

Algorithms↗
PubMed1999

Solving the N-bit parity problem using neural networks.

In this letter, a constructive solution to the N-bit parity problem is provided with a neural network that allows direct connections between the input layer and the output layer. The present approach requires no training and adaptation, and thus it warrants the use of the simple threshold activation function for the output and hidden layer neurons. It is previously shown that this choice of activation function and network structure leads to several solutions for the 3-bit parity problem obtained using linear programming. One of the solutions for the 3-bit parity problem is then generalized to obtain a solution for the N-bit parity problem using left floor N/2 right floor hidden layer neurons. It is shown that through the choice of a "staircase" type activation function, the left floor N/2 right floor hidden layer neurons can be further combined into a single hidden layer neuron.

Journal Article↗
PubMedDate not supplied

Identification of motifs with insertions and deletions in protein sequences using self-organizing neural networks.

The problem of motif identification in protein sequences has been studied for many years in the literature. Current popular algorithms of motif identification in protein sequences face two difficulties, high computational cost and the possibility of insertions and deletions. In this paper, we provide a new strategy that solve the problem more efficiently. We develop a self-organizing neural network structure with multiple levels of subnetworks to make an intelligent classification of the subsequences obtained from protein sequences. We maintain a low computational complexity through the use of this multi-level structure so that the classification of each subsequence is performed with respect to a small subspace of the whole input space. The new definition of pairwise distance between motif patterns provided in this paper can deal with up to two insertions/deletions allowed in a motif, while other existing algorithm can only deal with one insertion or deletion. We also maintain a high reliability using our self-organizing neural network since it will grow as needed to make sure all input patterns are considered and are given the same amount of attention. Simulation results show that our algorithm significantly outperforms existing algorithms in both accuracy and reliability aspects.

Algorithms↗