1,720,982 research outputs found
Bioinformatics: an overview for cancer research
No full textBioinformatics is a new science that is glowing out in the recent years. It is a multidisciplinary science that is made out of different kinds of other scientific fields like biology, computer science, chemistry, statistics, mathematics and others. It was a big challenge for researchers to describe this new field in a systematic scientific way and bring out the attention of its applications and services; one of these important services that Bioinformatics can be applied in, is the cancer studies, research and therapies for many beneficial reasons. This paper will give a clear glance overview of bioinformatics, its definition, aims, applications, technologies, the large amount of data produced in the biological field and how bioinformatics can organize, analyze and store them, discuss some algorithms that can be implemented over bioinformatics data, and how to apply bioinformatics to discover and diagnose diseases like cancer
A Study on the Most Common Algorithms Implemented for Cancer Gene Search and Classifications
No full textUnderstanding the gene expression is an important factor to cancer diagnosis. One target of this understanding is implementing cancer gene search and classification methods. However, cancer gene search and classification is a challenge in that there is no obvious exact algorithm that can be implemented individually for various cancer cells. In this paper, a research is conducted through the most common top ranked algorithms implemented for cancer gene search and classification, and how they are implemented to reach a better performance. The paper will distinguish algorithms implemented for Bio image analysis for cancer cells and algorithms implemented based on DNA microarray data. The paper will also explore the road map towards presenting the most current algorithms implemented for cancer gene search and classification, as well as focusing on the importance of the searching algorithms and how they are implemented to enhance searching and what factors affects the performance
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A model to enhance the atrial fibrillations’ risk detection using deep learning
Full text linkAtrial fibrillation (AF) is a complex arrhythmia linked to a variety of common cardiovascular illnesses and conventional cardiovascular risk factors. Although awareness and improved detection of AF have improved over the last decade as the incidence and prevalence of AF has increased, current trends in using machine learning approaches to diagnose AF are still lacking in precision. To determine the true nature of the Electrocardiography (ECG) signal segments, a Convolutional Neural Network (CNN) model was employed to discover hidden information. Fully Connected (FC) layers were then utilized to categorize the ECG data segments as normal or abnormal. The suggested algorithm\u27s findings were compared to state-of-the-art arrhythmia identification algorithms in the literature for the MIT-BIH ECG database. The methodology proved not only to yield high classification performance (98.5%) but also low processing computational advantage where the CNN was the most accurate algorithm used for atrial fibrillation detection hence. To conclude the findings of the research, a model was prepared to test the accuracy of the most common ML algorithms used for AF detection. After comparing the results of the experiment, it was clear that CNN algorithm is the best approach compared to Support Vector Machine (SVM) and K-Nearest Neighbor (KNN)
Sustainable EnergySense: a predictive machine learning framework for optimizing residential electricity consumption
Full text linkIn a world where electricity is often taken for granted, the surge in consumption poses significant challenges, including elevated CO2 emissions and rising prices. These issues not only impact consumers but also have broader implications for the global environment. This paper endeavors to propose a smart application dedicated to optimizing the electricity consumption of household appliances. It employs Augmented Reality (AR) technology along with YOLO to detect electrical appliances and provide detailed electricity consumption insights, such as displaying the appliance consumption rate and computing the total electricity consumption based on the number of hours the appliance was used. The application utilizes Linear Regression as a machine learning (ML) algorithm to develop the electricity consumption forecasting model for the next months, based on past utility bills. Linear regression is often considered one of the most computationally lightweight ML algorithms, making it suitable for smartphones. The application also offers users practical tips for optimizing their electricity consumption habits
EFFICIENT ALGORITHMS FOR CANCER GENE SEARCHING AND CLASSIFICATION:COLON CANCER
No full textCancer kills millions of people worldwide each year. It is a growing problem and is the foremost cause of death worldwide. The numbers of people battling cancer is growing rapidly, owing to different reasons, such as lifestyle. Clinically, determining the cause of cancer is very challenging and often inaccurate.
The goal of this research springs from the increasing necessity to incorporate efficient and accurate algorithms to detect colon cancer. In this research, two main models within case studies are proposed. The first case study (model) suggests a 3-phased method of examining the accuracy and time efficiency of high-performance gene selection and cancer classification algorithms applied to detecting colon cancer cells. The first and second phases examine gene/feature selection and cancer classification algorithms applied independently across the entire colon dataset. Phase three examines the performance of the first two phases incorporated together. The performance accuracies and time analyses are then compared across algorithms. The second case study proposes a model that reports accuracy improvements using a two-stage hybrid multifilter feature selection method for colon-cancer classification. This model is a benefit of applying gene selection prior to classification methods, and it enhances the accuracy of cancer-cell detection performance results. The proposed model first applies a hybrid genetic algorithm (GA) and information gain incorporated as the first stage of selection, followed by a filter-ranking algorithm of minimum redundancy maximum relevance (mRMR) to refine the subset of selected genes for the second stage of selection. Thereafter, the selected genes are evaluated by a variety of machine-learning algorithms.
It is found from the first case study that GA performs better for gene selection on the colon dataset during phase 1. Whereas, during phase 2, decision tree (DT) and support vector machine (SVM) classifiers reflect very good accuracy results(86%–87%). During phase 3, the incorporation of GA as a selector and DT as a classifier outperforms other algorithms with respect to accuracy (92%). The incorporation also analyses better with a time efficiency. However, the second case study finds that SVM classifiers reflected high accuracy following the proposed 2-stage multifilter selection approach (94%). When compared to methods in the literature, the proposed models yield better results
Algorithms Implemented for Cancer Gene Searching and Classifications
No full textUnderstanding the gene expression is an important factor to cancer diagnosis. One target of this understanding is implementing cancer gene search and classification methods. However, cancer gene search and classification is a challenge in that there is no an obvious exact algorithm that can be implemented individually for various cancer cells. In this paper a research is con-ducted through the most common top ranked algorithms implemented for cancer gene search and classification, and how they are implemented to reach a better performance. The paper will distinguish algorithms implemented for Bio image analysis for cancer cells and algorithms implemented based on DNA array data. The main purpose of this paper is to explore a road map towards presenting the most current algorithms implemented for cancer gene search and classification
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
- …
