13 papers · ranked by Valyu relevance
Seyyed Mahmood Ghasem, Johannes F. Fahrmann, Samir Hanash, Kim-Anh Do + 2 more
Logistic regression has demonstrated its utility in classifying binary labeled datasets through the maximum likelihood approach. However, in numerous biological and clinical contexts, the aim is often to determine coefficients that yield the highest sensitivity at the pre-specified specificity or vice versa. Therefore…
Océane Fourquet, Martin S. Krejca, Carola Doerr, Benno Schwikowski
Monotonic bivariate classifiers can describe simple patterns in high-dimensional data that may not be discernible using only elementary linear decision boundaries. Such classifiers are relatively simple, easy to interpret, and do not require large amounts of data to be effective. A challenge is that finding optimal…
Shreeya Banerji
Diabetes mellitus is a growing problem, especially in developing countries. People suffering from diabetes have an increased risk of developing a number of serious health problems. Consistently high blood glucose levels can lead to serious diseases affecting the heart and blood vessels, eyes, kidney, etc. In addition…
Melania Nowicka, Heike Siebert
Cell classifiers are decision-making synthetic circuits that allow in vivo cell-type classification. Their design is based on finding a relationship between differential expression of miRNAs and the cell condition. Such biological devices have shown potential to become a valuable tool in cancer treatment as a new…
Sarita Limbu, Sivanesan Dakshanamurthy
Determining environmental chemical carcinogenicity is an urgent need as humans are increasingly exposed to these chemicals. In this study, we determined the carcinogenicity of wide variety real-life exposure chemicals in large scale. To determine chemical carcinogenicity, we have developed carcinogenicity prediction…
Theo Knijnenburg, Gunnar Klau, Francesco Iorio, Mathew Garnett + 3 more
Mining large datasets using machine learning approaches often leads to models that are hard to interpret and not amenable to the generation of hypotheses that can be experimentally tested. Finding ‘actionable knowledge’ is becoming more important, but also more challenging as datasets grow in size and complexity. We…
Diego Jarquin, Arkaprava Roy, Bertrand Clarke, Subhashis Ghosal
Plant breeders want to develop cultivars that outperform existing genotypes. Some characteristics (here ‘main traits’) of these cultivars are categorical and difficult to measure directly. It is important to predict the main trait of newly developed genotypes accurately. In addition to marker data, breeding programs…
Raeuf Roushangar, George I. Mias
ClassificaIO is an open-source Python graphical user interface (GUI) for machine learning classification for the scikit-learn module. ClassificaIO aims to provide an easy-to-use interactive way to train, validate, and test data on a range of classification algorithms. The GUI enables fast comparisons within and across…
Richard Dinga, Brenda W.J.H. Penninx, Dick J. Veltman, Lianne Schmaal + 1 more
Pattern recognition predictive models have become an important tool for analysis of neuroimaging data and answering important questions from clinical and cognitive neuroscience. Regardless of the application, the most commonly used method to quantify model performance is to calculate prediction accuracy, i.e. the…
Stephen R. Piccolo, Terry J. Lee, Erica Suh, Kimball Hill
Classification algorithms assign observations to groups based on patterns in data. The machine-learning community have developed myriad classification algorithms, which are employed in diverse life-science research domains. When applying such algorithms, researchers face the challenge of deciding which algorithm(s) to…
Fei Deng, Jibing Huang, Xiaoling Yuan, Chao Cheng + 1 more
Most of the biomedical datasets, including those of ‘omics, population studies and surveys, are rectangular in shape and have few missing data. Recently, their sample sizes have grown significantly. Rigorous analyses on these large datasets demand considerably more efficient and more accurate algorithms. Machine…
Jesse Sherwood, Jesse Lowe, Reza Derakhshani
[Finding suitable common feature sets for use in multiclass subject independent brain-computer interface (BCI) classifiers is problematic due to characteristically large inter-subject variation of electroencephalographic signatures. We propose a wrapper search method using a one versus the rest discrete output…
Attila Lengyel, Zoltán Botta-Dukát
Numerical classification methods provide essential tools for data analysis in various fields of science. Reallocation algorithms are used for improving an a priori clustering (either a random clustering, or one obtained by another method) by changing iteratively the assignment of objects with the purpose of optimizing…