Application of Kohonen maps for solving the classification puzzle in AGC kinase protein sequences

Murty, Upadhyayula Suryanarayana; Banerjee, Amit Kumar; Arora, Neelima

doi:10.1007/s12539-009-0032-1

Cited by 8 publications

(4 citation statements)

References 24 publications

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“…Medical informatics, the application of Information Technology techniques involves examination of patient records and reports and through analysing of huge amounts of such data; complex interactions and correlation in it is revealed [42][43][44][45][46]. Practical application is observed in areas such as oncology, liver pathology, thyroid disease diagnosis, rheumatology, dermatology, cardiology, neuropsychology, gynaecology and perinatology [47][48][49][50]. Medical data is now facing serious setbacks as we still have not been able to come up with statistical methods capable of dealing with noisy and missing data [51][52][53].…”

Section: Ai Applicationsmentioning

confidence: 99%

Artificial Intelligence in Biological Data

Chakraborty¹,

Choudhury²

2017

J Inform Tech Softw Eng

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Artificial Intelligence (AI) or Machine learning in present era, serves as the primary choice for data mining and big data analysis. With effective learning and adaptation model, it provides solutions to several engineering applications. These include techniques such as Artificial Neural Network modelling, Reasoning based decision algorithms, Simulation models, DNA computing and Quantum computing among several others. With the application of AI in Biomedical research, the fuzziness and randomness in handling such type of data has significantly reduced. Rapid technological advancements have helped AI techniques evolve in manner which promotes handling such fuzzy data effectively and much more conveniently. The review presents a comprehensive view of machine learning and AI computing models, advanced data analytics and optimisation approaches used in Bioengineering such as Drug Designing and Analysis, Medical imaging, biologically inspired learning and adaption for analytics, etc.

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Section: Ai Applicationsmentioning

confidence: 99%

Artificial Intelligence in Biological Data

Chakraborty¹,

Choudhury²

2017

J Inform Tech Softw Eng

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“…In order to cluster the resulting, spatially assigned sliding-window dataset, a SOFM neural network using Gaussian distance functions was used. SOFM networks have been successfully used to find patterns in high-dimensional and large datasets [33][34][35][36].…”

Section: Finding Regional Trends In Phase Shiftmentioning

confidence: 99%

Distinct Global Patterns of Strong Positive and Negative Shifts of Seasons over the Last 6 Decades

Schmidt¹,

Law²,

Hanson³

et al. 2012

ACS

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Alterations of annual temperature cycles have profound implications on how the planet responds to global climate change. In this study, a high resolution global analysis of temperature cycle shifts and their development over time is presented. We show that over the last 63 years, phase shifts in the annual near surface temperature cycle exhibit large spatiotemporal variability. The calculated phase shifts comprise earlier onsets of seasons as well as delays with similar frequencies, depending on location. From 1978 to 2010 Eastern Europe experienced an advanced annual cycle of near-surface temperature of 3.2 days while Eastern Australia shows an opposite shift towards later seasons of 3.5 days in comparison to the preceding 30-year period from 1948 to 1977. The largest phase shifts of -5.5 days toward earlier seasons over land were found in Belarus and Northwest Russia. For the first time the developments of seasonal temperature shifts were generalized for large areas by using self-organizing feature map neural networks resulting into 4 significant global trends. The temperature phase shifts are also shown to have strong correlations with the timing of shrub foliation observed at 57 phenological stations across the USA. The findings have far-reaching, yet regionally distinct consequences on agriculture, animal life cycles, plant phenology, and regional weather phenomena that change with annual temperature cycles.

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“…In the present study we have adopted an intelligent method dependent approach for classification and clustering the bacterial Pyruvate Dehydrogenase protein sequences according to their genera based on their physicochemical properties [14,15]. Similar kind of studies we have reported in the recent past [16,17,18]. …”

Section: Introductionmentioning

confidence: 97%

Classification and clustering analysis of pyruvate dehydrogenase enzyme based on their physicochemical properties

Us²

2010

Bioinformation

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Biological systems are highly organized and enormously coordinated maintaining greater complexity. The increment of secondary data generation and progress of modern mining techniques provided us an opportunity to discover hidden intra and inter relations among these non linear dataset. This will help in understanding the complex biological phenomenon with greater efficiency. In this paper we report comparative classification of Pyruvate Dehydrogenase protein sequences from bacterial sources based on 28 different physicochemical parameters (such as bulkiness, hydrophobicity, total positively and negatively charged residues, α helices, β strand etc.) and 20 type amino acid compositions. Logistic, MLP (Multi Layer Perceptron), SMO (Sequential Minimal Optimization), RBFN (Radial Basis Function Network) and SL (simple logistic) methods were compared in this study. MLP was found to be the best method with maximum average accuracy of 88.20%. Same dataset was subjected for clustering using 2*2 grid of a two dimensional SOM (Self Organizing Maps). Clustering analysis revealed the proximity of the unannotated sequences with the Mycobacterium and Synechococcus genus.

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Application of Kohonen maps for solving the classification puzzle in AGC kinase protein sequences

Cited by 8 publications

References 24 publications

Artificial Intelligence in Biological Data

Artificial Intelligence in Biological Data

Distinct Global Patterns of Strong Positive and Negative Shifts of Seasons over the Last 6 Decades

Classification and clustering analysis of pyruvate dehydrogenase enzyme based on their physicochemical properties

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