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Results for statistical learning methods

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13 papers · ranked by Valyu relevance

01
BbioRxiv15 Nov 2014
Circumstantial Evidence? Comparison of Statistical Learning Methods using Functional Annotations for Prioritizing Risk Variants
Sarah A. Gagliano, Reena Ravji, Michael R. Barnes, Michael E. Weale + 1 more
Although technology has triumphed in facilitating routine genome re-sequencing, new challenges have been created for the data analyst. Genome scale surveys of human disease variation generate volumes of data that far exceed capabilities for laboratory characterization, and importantly also create a substantial burden…
85relevance
02
BbioRxiv3 Feb 2017
An Evaluation of Machine-learning for Predicting Phenotype: studies in yeast and wheat
Nastasiya F. Grinberg, Ross D. King
In phenotype prediction the physical character of an organism is predicted from knowledge of its genotype and environment. Such studies are of the highest societal importance as they are now of central importance to medicine, crop-breeding, etc. We investigated two phenotype prediction problems: one simple and clean…
81relevance
03
BbioRxiv26 Mar 2017
Machine Learning-based state-of-the-art methods for the classification of RNA-Seq data
Almas Jabeen, Nadeem Ahmad, Khalid Raza
RNA-Seq measures expression levels of several transcripts simultaneously. The identified reads can be gene, exon, or other region of interest. Various computational tools have been developed for studying pathogen or virus from RNA-Seq data by classifying them according to the attributes in several predefined classes…
79relevance
04
BbioRxiv1 Jan 2025
Multivariate Bayesian Inversion for Classification and Regression
Joram Soch, Carsten Allefeld
We propose the statistical modelling approach to supervised learning (i.e. predicting labels from features) as an alternative to algorithmic machine learning (ML). The approach is demonstrated by employing a multivariate general linear model (MGLM) describing the effects of labels on features, possibly accounting for…
78relevance
05
BbioRxiv17 Mar 2024
Interpretable and predictive models to harness the life science data revolution
Joshua P. Jahner, C. Alex Buerkle, Dustin G. Gannon, Eliza M. Grames + 11 more
The proliferation of biological data with large numbers of samples and many dimensions is kindling hope that life scientists will be able to fit statistical and machine learning models that are highly predictive and interpretable. However, large biological data sets are commonly burdened with an inherent trade-off…
77relevance
06
BbioRxiv20 May 2019
A short review of machine learning methods for classifying the outcome of Gestational Diabetes
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…
77relevance
07
BbioRxiv29 Apr 2024
Contributions of statistical learning to learning from reward feedback
Aryan Yazdanpanah, Michael Chong Wang, Ethan Trepka, Marissa Benz + 1 more
Natural environments are abundant with patterns and regularities. These regularities can be captured through statistical learning, which strongly influences perception, memory, and other cognitive functions. By combining a sequence-prediction task with an orthogonal multidimensional reward learning task, we tested…
77relevance
08
BbioRxiv21 May 2016
Combining complex networks and data mining: why and how
M. Zanin, D. Papo, P. A. Sousa, E. Menasalvas + 3 more
The increasing power of computer technology does not dispense with the need to extract meaningful in-formation out of data sets of ever growing size, and indeed typically exacerbates the complexity of this task. To tackle this general problem, two methods have emerged, at chronologically different times, that are now…
77relevance
09
BbioRxiv16 Dec 2019
Don’t Dismiss Logistic Regression: The Case for Sensible Extraction of Interactions in the Era of Machine Learning
Joshua J. Levy, A. James O’Malley
Machine learning approaches have become increasingly popular modeling techniques, relying on data-driven heuristics to arrive at its solutions. Recent comparisons between these algorithms and traditional statistical modeling techniques have largely ignored the superiority gained by the former approaches due to…
71relevance
10
BbioRxiv5 Jul 2020
Different Perspective of Machine Learning Technique to Better Predict Breast Cancer Survival
Kaushal Kumar, Vijay Vikram Singh, Ramakrishna Ramaswamy
Machine learning (ML) plays a key job in the guide of cancer diagnosis and identification. The researcher has implemented different algorithms of ML for the prediction of breast cancer. Some researchers recommend their algorithms are more accurate, faster, and easier than others. My study relies on recently developed…
71relevance
11
BbioRxiv25 Oct 2024
Getting Started with Machine Learning for Experimental Biochemists and Other Molecular Scientists
Matthew J. K. Vince, Kristin A. Hughes, Anastasiya Buzuk, Deborah L. Perlstein + 2 more
Machine learning (ML) is rapidly gaining traction in many areas of experimental molecular science for elucidating relationships and patterns in large or complex data sets. Historically, ML was largely the preserve of those with specialized training in fields such as statistics or cheminformatics. Increasingly, however…
70relevance
12
BbioRxiv2 Jun 2025
Deviation Error: assessing predictions for replicate measurements in genomics and beyond
Husam Abdulnabi, J. Timothy Westwood
A quantitative measurement can have variation, referred to here as measurement variation, which is a probability distribution. Machine Learning models typically produce a prediction corresponding to the mode of the measurement variation. The Deviation Error is a novel metric, described here, to assess predictions that…
65relevance
13
BbioRxiv21 May 2018
Prediction and inference diverge in biomedicine: Simulations and real-world data
Danilo Bzdok, Denis Engemann, Olivier Grisel, Gaël Varoquaux + 1 more
In the 20^th^ century many advances in biological knowledge and evidence-based medicine were supported by p-values and accompanying methods. In the beginning 21^st^ century, ambitions towards precision medicine put a premium on detailed predictions for single individuals. The shift causes tension between traditional…
63relevance

Results for statistical learning methods

Circumstantial Evidence? Comparison of Statistical Learning Methods using Functional Annotations for Prioritizing Risk Variants

An Evaluation of Machine-learning for Predicting Phenotype: studies in yeast and wheat

Machine Learning-based state-of-the-art methods for the classification of RNA-Seq data

Multivariate Bayesian Inversion for Classification and Regression

Interpretable and predictive models to harness the life science data revolution

A short review of machine learning methods for classifying the outcome of Gestational Diabetes

Contributions of statistical learning to learning from reward feedback

Combining complex networks and data mining: why and how

Don’t Dismiss Logistic Regression: The Case for Sensible Extraction of Interactions in the Era of Machine Learning

Different Perspective of Machine Learning Technique to Better Predict Breast Cancer Survival

Getting Started with Machine Learning for Experimental Biochemists and Other Molecular Scientists

Deviation Error: assessing predictions for replicate measurements in genomics and beyond

Prediction and inference diverge in biomedicine: Simulations and real-world data

Circumstantial Evidence? Comparison of Statistical Learning Methods using Functional Annotations for Prioritizing Risk Variants

An Evaluation of Machine-learning for Predicting Phenotype: studies in yeast and wheat

Machine Learning-based state-of-the-art methods for the classification of RNA-Seq data

Multivariate Bayesian Inversion for Classification and Regression

Interpretable and predictive models to harness the life science data revolution

A short review of machine learning methods for classifying the outcome of Gestational Diabetes

Contributions of statistical learning to learning from reward feedback

Combining complex networks and data mining: why and how

Don’t Dismiss Logistic Regression: The Case for Sensible Extraction of Interactions in the Era of Machine Learning

Different Perspective of Machine Learning Technique to Better Predict Breast Cancer Survival

Getting Started with Machine Learning for Experimental Biochemists and Other Molecular Scientists

Deviation Error: assessing predictions for replicate measurements in genomics and beyond

Prediction and inference diverge in biomedicine: Simulations and real-world data