Artificial Intelligence and Machine Learning in Bioinformatics

Lai, Kaitao; Twine, Natalie A.; O’Brien, Aidan; Guo, Yi; Bauer, Denis C.

doi:10.1016/b978-0-12-809633-8.20325-7

Cited by 31 publications

(28 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Though a wide number of image classification technique are available, its still unclear which are helpful for analyzing and perceptively categorizing suitable images. Some basic classification methods are k-nearest neighbors, bayesian classification, support vector machines and artificial neural networks (Wang 2019;Lai et al 2018;Vijayakumar and Arun 2017). Recently with the hasty progress in the technology advancement, application of artificial intelligence in several areas has improved vividly.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Computational intelligence on image classification methods for microscopic image data

Muthu

Kirubha

2020

J Ambient Intell Human Comput

View full text Add to dashboard Cite

Technology play a substantial function in exemplifying technical potentials that are unconsidered in the past to include intelligent behaviour. Computers in machine learning involve the modelling of intelligent behaviour with the least human mediation. Computational intelligence signifies a set of vital information processing methods for information managing and decision accomplishing. Major critical trials in technical exploration is the supervision of data obtained from various resources. Presently, Scanning Electron Microscopy (SEM) images are categorized as per user's measures, which greatly restrict misuse and reliability, owing for the need in definite classification algorithm. The foremost objective is to evaluate the exactness with each algorithm in terms of performance measures such as accuracy, precision, sensitivity, F-measure, kappa, computational time and confusion matrix. In this work, a recital assessment among three classification algorithms on SEM image dataset was conducted. Experimental results revealed that the Random forest algorithm gives the highest accuracy comparing to other two approach. Hence, the researchers would be able to perform automatic SEM image classification evading the necessity to categorize image and for providing a searchable database to identify a definite group of images.

show abstract

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Computational intelligence on image classification methods for microscopic image data

Muthu

Kirubha

2020

J Ambient Intell Human Comput

View full text Add to dashboard Cite

show abstract

“…SVM uses linear models to implement non-linear class boundaries and found great applications in a wide variety of fields [2]. A SVM modeled to tackle regression problem is known as Support Vector Regression (SVR), which has tremendous growth in control predictive and optimization systems face detection, bioinformatics and communication systems, Protein Fold and Remote Homology Detection, Handwriting Recognition, Geo and Environmental Sciences amongst others [6,13,19].…”

Section: Support Vector Machines (Svms)mentioning

confidence: 99%

Comparative Analysis and Investigations of Various SVM Kernels Using Cellular Network KPI Data

Philip¹,

Michael²,

Gabriel³

2019

WCMC

View full text Add to dashboard Cite

Classification plays a major role in every field in of human endeavors which Support Vector Machine (SVM) happened to be one of the popular algorithms for classification and prediction. However, the performance of SVM is greatly affected by the choice of a kernel function among other factors. In this research work, SVM is employed and evaluated with six different kernels by varying their parameters especially the training ratio to investigate their performance. The training ratio was varied in the proportion of 60-20-20, 40-30-30 and 20-40-40 to obtain higher classification accuracy. Based on the performance result, GRBK and ERBK kernels are capable of classifying datasets at hand accurately with the best specificity and sensitivity values. From the study, the SVM model with GRBF and ERBF kernels are the best suited for call algorithm data at hand in terms of best specificity and sensitivity values, followed by the RBF kernel. Also, the research further indicates that MLP, polynomial and linear kernels have worse performance. Therefore, despite SVMs being limited to making binary classifications, their superior properties of scalability and generalization capability give them an advantage in other domains.

show abstract

“…Hidden Markov Model (HMM) has been extended to deal with the sequential data (Lai et al, 2016). It is particularly an effective approach to predict the future risk of a disease in an individual using sequences of clinical measurements obtained from longitudinal samples of patients (El Nahas et al, 2012;Srikanth, 2015).…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Modeling Type 2 Diabetes Mellitus Progression

et al. 2020

View full text Add to dashboard Cite

Type 2 Diabetes Mellitus (T2DM) is a chronic, progressive metabolic disorder characterized by hyperglycemia resulting from abnormalities in insulin secretion, insulin action, or both. It is associated with an increased risk of developing vascular complication of micro as well as macro nature. Because of its inconspicuous and heterogeneous character, the management of T2DM is very complex. Modeling physiological processes over time demonstrating the patient's evolving health condition is imperative to comprehending the patient's current status of health, projecting its likely dynamics and assessing the requisite care and treatment measures in future. Hidden Markov Model (HMM) is an effective approach for such prognostic modeling. However, the nature of the clinical setting, together with the format of the Electronic Medical Records (EMRs) data, in particular the sparse and irregularly sampled clinical data which is well understood to present significant challenges, has confounded standard HMM. In the present study, we proposed an approximation technique based on Newton's Divided Difference Method (NDDM) as a component with HMM to determine the risk of developing diabetes in an individual over different time horizons using irregular and sparsely sampled EMRs data. The proposed method is capable of exploiting available sequences of clinical measurements obtained from a longitudinal sample of patients for effective imputation and improved prediction performance. Furthermore, results demonstrated that the discrimination capability of our proposed method, in prognosticating diabetes risk, is superior to the standard HMM.

show abstract

Artificial Intelligence and Machine Learning in Bioinformatics

Cited by 31 publications

References 84 publications

RETRACTED ARTICLE: Computational intelligence on image classification methods for microscopic image data

RETRACTED ARTICLE: Computational intelligence on image classification methods for microscopic image data

Comparative Analysis and Investigations of Various SVM Kernels Using Cellular Network KPI Data

A Hybrid Approach for Modeling Type 2 Diabetes Mellitus Progression

Contact Info

Product

Resources

About