scholarly journals Classification of HIV-1 Protease Inhibitors by Machine Learning Methods

ACS Omega ◽  
2018 ◽  
Vol 3 (11) ◽  
pp. 15837-15849 ◽  
Author(s):  
Yang Li ◽  
Yujia Tian ◽  
Zijian Qin ◽  
Aixia Yan
Keyword(s):  
Machine Learning ◽  
Protease Inhibitors ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Hiv 1

Machine Learning methods for micro-FTIR imaging classification of human skin tumors

2021 ◽  
Author(s):  
Matheus del Valle ◽  
Kleber Stancari ◽  
Pedro Arthur Augusto de Castro ◽  
Moises Oliveira dos Santos ◽  
Denise Maria Zezell
Keyword(s):  
Machine Learning ◽  
Human Skin ◽  
Skin Tumors ◽  
Learning Methods ◽  
Ftir Imaging ◽  
Machine Learning Methods ◽  
Imaging Classification

scholarly journals Seeing It All: Evaluating Supervised Machine Learning Methods for the Classification of Diverse Otariid Behaviours

PLoS ONE ◽  
2016 ◽  
Vol 11 (12) ◽  
pp. e0166898 ◽  
Author(s):  
Monique A. Ladds ◽  
Adam P. Thompson ◽  
David J. Slip ◽  
David P. Hocking ◽  
Robert G. Harcourt
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods

Classification of beta‐site amyloid precursor protein cleaving enzyme 1 inhibitors by using machine learning methods

2021 ◽  
Author(s):  
Ravi Singh ◽  
Ankit Ganeshpurkar ◽  
Powsali Ghosh ◽  
Ankit Vyankatrao Pokle ◽  
Devendra Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Amyloid Precursor Protein ◽  
Precursor Protein ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging

2020 ◽  
Vol 493 (3) ◽  
pp. 4209-4228 ◽  
Author(s):  
Ting-Yun Cheng ◽  
Christopher J Conselice ◽  
Alfonso Aragón-Salamanca ◽  
Nan Li ◽  
Asa F L Bluck ◽  
...  
Keyword(s):  
Machine Learning ◽  
Imaging Data ◽  
Morphological Classification ◽  
Learning Methods ◽  
Visual Classification ◽  
Machine Learning Methods ◽  
Dark Energy Survey ◽  
Galaxy Classification ◽  
Energy Survey

ABSTRACT There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or an investigation for maximizing their effectiveness. We carry out a comparison between several common machine learning methods for galaxy classification [Convolutional Neural Network (CNN), K-nearest neighbour, logistic regression, Support Vector Machine, Random Forest, and Neural Networks] by using Dark Energy Survey (DES) data combined with visual classifications from the Galaxy Zoo 1 project (GZ1). Our goal is to determine the optimal machine learning methods when using imaging data for galaxy classification. We show that CNN is the most successful method of these ten methods in our study. Using a sample of ∼2800 galaxies with visual classification from GZ1, we reach an accuracy of ∼0.99 for the morphological classification of ellipticals and spirals. The further investigation of the galaxies that have a different ML and visual classification but with high predicted probabilities in our CNN usually reveals the incorrect classification provided by GZ1. We further find the galaxies having a low probability of being either spirals or ellipticals are visually lenticulars (S0), demonstrating that supervised learning is able to rediscover that this class of galaxy is distinct from both ellipticals and spirals. We confirm that ∼2.5 per cent galaxies are misclassified by GZ1 in our study. After correcting these galaxies’ labels, we improve our CNN performance to an average accuracy of over 0.99 (accuracy of 0.994 is our best result).

scholarly journals MLW-gcForest: A Multi-Weighted gcForest Model for Cancer Subtype Classification by Methylation Data

2019 ◽  
Vol 9 (17) ◽  
pp. 3589 ◽  
Author(s):  
Yunyun Dong ◽  
Wenkai Yang ◽  
Jiawen Wang ◽  
Juanjuan Zhao ◽  
Yan Qiang
Keyword(s):  
Machine Learning ◽  
Small Sample Size ◽  
Small Sample ◽  
The Cancer Genome Atlas ◽  
High Dimensionality ◽  
Methylation Data ◽  
Learning Methods ◽  
Cancer Subtypes ◽  
Machine Learning Methods

Effective cancer treatment requires a clear subtype. Due to the small sample size, high dimensionality, and class imbalances of cancer gene data, classifying cancer subtypes by traditional machine learning methods remains challenging. The gcForest algorithm is a combination of machine learning methods and a deep neural network and has been indicated to achieve better classification of small samples of data. However, the gcForest algorithm still faces many challenges when this method is applied to the classification of cancer subtypes. In this paper, we propose an improved gcForest algorithm (MLW-gcForest) to study the applicability of this method to the small sample sizes, high dimensionality, and class imbalances of genetic data. The main contributions of this algorithm are as follows: (1) Different weights are assigned to different random forests according to the classification ability of the forests. (2) We propose a sorting optimization algorithm that assigns different weights to the feature vectors generated under different sliding windows. The MLW-gcForest model is trained on the methylation data of five data sets from the cancer genome atlas (TCGA). The experimental results show that the MLW-gcForest algorithm achieves high accuracy and area under curve (AUC) values for the classification of cancer subtypes compared with those of traditional machine learning methods and state of the art methods. The results also show that methylation data can be effectively used to diagnose cancer.

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