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Fluorescence spectroscopy is a powerful technique usually used to evaluate phytoplankton marine environments. In this study, a kernel method (Potential Support Vector Machine, P-SVM) is presented, evaluating its capability to achieve phytoplankton classification from its fluorescence spectra. Different phytoplankton species were studied, and their fluorescence spectra were acquired in laboratory....
Exploring micro RNA (miRNA) and mRNA regulatory interactions may give new insights into diverse biological phenomena. While elucidating complex miRNA-mRNA interactions has been studied with experimental and computational approaches, it is still difficult to infer miRNA-mRNA regulatory modules. Here we present a novel method for identifying functional miRNA-mRNA modules from heterogeneous expression...
High-throughput automated analysis of cell population behaviors in vitro is of great importance to biological research. In particular, automated quantification of cellular mitosis in time-lapse microscopy video is useful for multiple applications such as tissue engineering, cancer research, and developmental biology. Accurate localization and counting of mitosis are challenging since cells undergo...
The high dimensionality of microarray data, the expressions of thousands of genes in a much smaller number of samples, presents challenges that affect the applicability of the analytical results. In principle, it would be better to describe the data in terms of a small number of metagenes, derived as a result of matrix factorization, which could reduce noise while still capturing the essential features...
Visible and near infrared (NIR) spectroscopy was utilized to determine the growing areas of Tremella fuciformis. Principal component analysis (PCA) obtained the cluster plot which shows the difficulty to determine the growing area by the first three principal components. Least-square support vector machine (LS-SVM) was used to establish the calibration model. Successive projections algorithm (SPA)...
The problem of recognizing multiple object classes in natural images has proven to be a difficult challenge for compute vision. It is reasonable to look to biology for inspiration, a novel multiclass object recognition algorithm based on a biologically inspired model named ST model is proposed. ST model is based on the theory of biological neurology, which calculates object features that exhibit position...
We identified 90 germline single nucleotide polymorphisms (SNPs) that were informative for discriminative analysis of 9 major cancers among genotyped Framingham Heart Study participants. Support vector machines resulted in the greatest classification performance, which was in the range of 70-100%. The germline SNPs identified are based on DNA from peripheral blood lymphocytes obtained during non-invasive...
Affymetrix High Oligonucleotide expression arrays are widely used for the high-throughput assessment of gene expression of thousands of genes simultaneously. Although disputed by several authors, there are non-biological variations and systematic biases that must be removed as much as possible through the pre-processing step before an absolute expression level for every gene is assessed. It is important...
The Primary Open Angle Glaucoma(POAG) discriminated model using support vector machine(SVC) method is presented to distinguish the primary open-angle glaucoma disease, which is not clear in early symptoms and involves in various risk factors, moreover easy to blind with prolonged intraocular hypertension. Through case study of clinical diagnosis, SVM classifier with a radial basis inner function was...
Understanding protein structures is vital to determining the function of a protein and its interaction with DNA, RNA and enzyme. The information about its conformation can provide essential information for drug design and protein engineering. While there are over a million known protein sequences, only a limited number of protein structures are experimentally determined. Hence, prediction of protein...
Emotion perception similar to thinking, learning and remembering is consequent of complicated brain processes which are related to specific biological metabolism. Different human's emotional states are recognizable by measuring and interpreting of human physiological signals. Bio-sensors possess a number of advantages against other emotion recognition methods as they are relatively more consistent...
With the rapid development of high-throughput genotyping technologies, more and more attentions are paid to the disease association study identifying DNA variations that are highly associated with a specific disease. One main challenge for this study is to find the optimal subsets of Single Nucleotide Polymorphisms (SNPs) which are most tightly associated with diseases. Feature selection has become...
Coscinodiscus Ehrenberg is a large and ecologically important diatom genus with plentiful species in marine phytoplankton and with a variety of round shapes and ornamentation. These properties can be measured by computer image pre-segmentation and feature extraction with threshold methods. However, it proves to be complicated task because of the high spatial variability of ornamentation properties...
Marine phytoplanktons are unicellular algae with a variety of shapes and ornamentation, and they are widely used as indicators of marine ecosystem changes. A dual layer and hybrid classifier is presented in this study for phytoplankton recognition. The method is based on k-NN, SVM mechanisms and uses shape and texture information such as moments, geometric features and gray level co-occurrence matrix...
Remote homology detection between protein sequences is a central problem in computational biology. The discriminative method incorporating Support Vector Machine (SVM) is one of the most effective methods. Many of SVM-based methods focus on finding useful representations of protein sequences, using either explicit feature vector representations or kernel functions. In this paper, we focuses on feature...
The support vector machine (SVM) was employed to construct dipeptides Quantitative Structure-Activity Relationship (QSAR) model. Amino acid descriptors PRIN parameter has been introduced in bioactive peptides QSAR Study in the article at firstly. Outliers of 168 Angiotensin I-Converting Enzyme (ACE) Inhibitory Dipeptides are deleted and QSAR model is constructed by support vector regression (SVR)...
We have developed a method for remote homology detection using profile-based fragment matching. Our method compares a sliding nine-residue long fragment of the target sequence to all such fragments in the database and keeps up to 150 candidate fragments with the highest profile-profile score. For each candidate sequence, a dot plot of the positions of the fragments in the target sequence against the...
This paper addresses the problem of distinguishing retroviruses from non-coding DNA sequences. Retroviruses have a distinctive reading frame structure that includes multiple reading frames that often overlap. This paper uses reading frame information generated from Fourier spectral analysis as input for Side Effect Machines (SEMs) that are evolved to create clusterings which separate the two types...
Influenza viruses continue to evolve rapidly and are responsible for seasonal epidemics and occasional, but catastrophic, pandemics. We recently demonstrated the use of decision tree and support vector machine methods in classifying pandemic swine flu viral strains with high accuracy. Here, we applied the technique of artificial neural networks for the prediction of important influenza virus antigenic...
Visible and near infrared (NIR) spectroscopy was utilized to classify the verities of laver. As there are almost six hundreds of NMR variables which would cause poor classification and long calculation time, uninformative variables should be eliminated. Successive projections algorithm (SPA) was applied to select the effective variables from the full-spectrum (FS). Finally 13 variables were selected,...
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