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Results for machine learning interpretability

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14 papers · ranked by Valyu relevance

01
BbioRxiv21 Feb 2024
Interpretable and explainable predictive machine learning models for data-driven protein engineering
David Medina-Ortiz, Ashkan Khalifeh, Hoda Anvari-Kazemabad, Mehdi D. Davari
Protein engineering using directed evolution and (semi)rational design has emerged as a powerful strategy for optimizing and enhancing enzymes or proteins with desired properties. Integrating artificial intelligence methods has further enhanced and accelerated protein engineering through predictive models developed in…
89relevance
02
BbioRxiv19 Sep 2022
Interpretable machine learning with tree-based shapley additive explanations: application to metabolomics datasets for binary classification
Olatomiwa O. Bifarin
Machine learning (ML) models are used in clinical metabolomics studies most notably for biomarker discoveries, to identify metabolites that discriminate between a case and control group. To improve understanding of the underlying biomedical problem and to bolster confidence in these discoveries, model interpretability…
89relevance
03
BbioRxiv22 Feb 2022
Assessment and Optimization of the Interpretability of Machine Learning Models Applied to Transcriptomic Data
Yongbing Zhao, Jinfeng Shao, Yan W Asmann
While explainable artificial intelligence has emerged with aim at interpreting how the machine learning models make decisions, many model explainers have been developed in computer vision field. By far, there still lacks an understanding of the applicability of these model explainers in biological study. To address…
88relevance
04
BbioRxiv23 Dec 2024
BenchXAI: Comprehensive Benchmarking of Post-hoc Explainable AI Methods on Multi-Modal Biomedical Data
Jacqueline Michelle Metsch, Anne-Christin Hauschild
The increasing digitalisation of multi-modal data in medicine and novel artificial intelligence (AI) algorithms opens up a large number of opportunities for predictive models. In particular, deep learning models show great performance in the medical field. A major limitation of such powerful but complex models…
88relevance
05
BbioRxiv21 Feb 2022
Enabling interpretable machine learning for biological data with reliability scores
K. D. Ahlquist, Lauren Sugden, Sohini Ramachandran
Machine learning has become an important tool across biological disciplines, allowing researchers to draw conclusions from large datasets, and opening up new opportunities for interpreting complex and heterogeneous biological data. Alongside the rapid growth of machine learning, there have also been growing pains: some…
87relevance
06
BbioRxiv24 Jan 2021
REM: An Integrative Rule Extraction Methodology for Explainable Data Analysis in Healthcare
Zohreh Shams, Botty Dimanov, Sumaiyah Kola, Nikola Simidjievski + 6 more
Deep learning models are receiving increasing attention in clinical decision-making, however the lack of interpretability and explainability impedes their deployment in day-to-day clinical practice. We propose REM, an interpretable and explainable methodology for extracting rules from deep neural networks and combining…
87relevance
07
BbioRxiv23 Jun 2020
Probing machine-learning classifiers using noise, bubbles, and reverse correlation
Etienne Thoret, Thomas Andrillon, Damien Léger, Daniel Pressnitzer
Many scientific fields now use machine-learning tools to assist with complex classification tasks. In neuroscience, automatic classifiers may be useful to diagnose medical images, monitor electrophysiological signals, or decode perceptual and cognitive states from neural signals. However, such tools often remain…
87relevance
08
BbioRxiv8 May 2019
R.ROSETTA: an interpretable machine learning framework
Mateusz Garbulowski, Klev Diamanti, Karolina Smolińska, Nicholas Baltzer + 5 more
For machine learning to matter beyond intellectual curiosity, the models developed therefrom must be adopted within the greater scientific community. In this study, we developed an interpretable machine learning framework that allows identification of semantics from various datatypes. Our package can analyze and…
86relevance
09
BbioRxiv17 Oct 2022
Explainable Machine Learning to Identify Patient-specific Biomarkers for Lung Cancer
Masrur Sobhan, Ananda Mohan Mondal
Lung cancer is the leading cause of cancer compared to other cancers in the USA despite being the most commonly diagnosed. The overall survival rate of lung cancer is not satisfactory even though having cutting edge treatment methods for cancers. Genomic profiling and biomarker gene identification of lung cancer…
85relevance
10
BbioRxiv17 Apr 2025
Can interpretability and accuracy coexist in cancer survival analysis?
Piyush Borole, Tongjie Wang, Antonio Vergari, Ajitha Rajan
Survival analysis refers to statistical procedures used to analyze data that focuses on the time until an event occurs, such as death in cancer patients. Traditionally, the linear Cox Proportional Hazards (CPH) model is widely used due to its inherent interpretability. CPH model help identify key disease-associated…
84relevance
11
BbioRxiv2 Feb 2026
BIG-TB: A benchmark for prediction and interpretability of sequence-based machine learning using *Mycobacterium tuberculosis* genomes
Mahbuba Tasmin, Saishradha Mohanty, Sanjana Kulkarni, Maha R. Farhat + 1 more
Foundation models aim to learn useful representations of biological sequences. However, the applicability of these representations for a wide range of tasks, including phenotype prediction and variant discovery, is still in question, in large part due to the relatively small set of benchmark tasks. To this end, we…
82relevance
12
BbioRxiv18 Oct 2018
On Parameter Interpretability of Phenomenological-Based Semiphysical Models
Laura Lema-Perez, Rafael Muñoz-Tamayo, Jose Garcia-Tirado, Hernan Alvarez
Empirical and phenomenological based models are used to represent biological and physiological processes. Phenomenological models are derived from the knowledge of the mechanisms that underlie the behaviour of the system under study, while empirical models are derived from analysis of data to quantify relationships…
81relevance
13
BbioRxiv22 May 2023
Discovering Biomarker Proteins and Peptides for Parkinson’s Disease Prognosis Prediction with Machine Learning and Interpretability Methods
Ho-min Park, Espoir Kabanga, Dongin Moon, Minjae Chung + 4 more
Parkinson’s disease is a neurodegenerative disorder that affects millions of people worldwide, posing significant challenges for diagnosis and treatment. This study presents a machine learning pipeline for identifying candidate biomarker proteins and peptides from cerebrospinal fluid mass spectrometry (CSF-MS) tests in…
80relevance
14
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…
73relevance

Results for machine learning interpretability

Interpretable and explainable predictive machine learning models for data-driven protein engineering

Interpretable machine learning with tree-based shapley additive explanations: application to metabolomics datasets for binary classification

Assessment and Optimization of the Interpretability of Machine Learning Models Applied to Transcriptomic Data

BenchXAI: Comprehensive Benchmarking of Post-hoc Explainable AI Methods on Multi-Modal Biomedical Data

Enabling interpretable machine learning for biological data with reliability scores

REM: An Integrative Rule Extraction Methodology for Explainable Data Analysis in Healthcare

Probing machine-learning classifiers using noise, bubbles, and reverse correlation

R.ROSETTA: an interpretable machine learning framework

Explainable Machine Learning to Identify Patient-specific Biomarkers for Lung Cancer

Can interpretability and accuracy coexist in cancer survival analysis?

BIG-TB: A benchmark for prediction and interpretability of sequence-based machine learning using Mycobacterium tuberculosis genomes

On Parameter Interpretability of Phenomenological-Based Semiphysical Models

Discovering Biomarker Proteins and Peptides for Parkinson’s Disease Prognosis Prediction with Machine Learning and Interpretability Methods

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

Interpretable and explainable predictive machine learning models for data-driven protein engineering

Interpretable machine learning with tree-based shapley additive explanations: application to metabolomics datasets for binary classification

Assessment and Optimization of the Interpretability of Machine Learning Models Applied to Transcriptomic Data

BenchXAI: Comprehensive Benchmarking of Post-hoc Explainable AI Methods on Multi-Modal Biomedical Data

Enabling interpretable machine learning for biological data with reliability scores

REM: An Integrative Rule Extraction Methodology for Explainable Data Analysis in Healthcare

Probing machine-learning classifiers using noise, bubbles, and reverse correlation

R.ROSETTA: an interpretable machine learning framework

Explainable Machine Learning to Identify Patient-specific Biomarkers for Lung Cancer

Can interpretability and accuracy coexist in cancer survival analysis?

BIG-TB: A benchmark for prediction and interpretability of sequence-based machine learning using Mycobacterium tuberculosis genomes

On Parameter Interpretability of Phenomenological-Based Semiphysical Models

Discovering Biomarker Proteins and Peptides for Parkinson’s Disease Prognosis Prediction with Machine Learning and Interpretability Methods

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

Results for machine learning interpretability

Interpretable and explainable predictive machine learning models for data-driven protein engineering

Interpretable machine learning with tree-based shapley additive explanations: application to metabolomics datasets for binary classification

Assessment and Optimization of the Interpretability of Machine Learning Models Applied to Transcriptomic Data

BenchXAI: Comprehensive Benchmarking of Post-hoc Explainable AI Methods on Multi-Modal Biomedical Data

Enabling interpretable machine learning for biological data with reliability scores

REM: An Integrative Rule Extraction Methodology for Explainable Data Analysis in Healthcare

Probing machine-learning classifiers using noise, bubbles, and reverse correlation

R.ROSETTA: an interpretable machine learning framework

Explainable Machine Learning to Identify Patient-specific Biomarkers for Lung Cancer

Can interpretability and accuracy coexist in cancer survival analysis?

BIG-TB: A benchmark for prediction and interpretability of sequence-based machine learning using *Mycobacterium tuberculosis* genomes

On Parameter Interpretability of Phenomenological-Based Semiphysical Models

Discovering Biomarker Proteins and Peptides for Parkinson’s Disease Prognosis Prediction with Machine Learning and Interpretability Methods

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

Interpretable and explainable predictive machine learning models for data-driven protein engineering

Interpretable machine learning with tree-based shapley additive explanations: application to metabolomics datasets for binary classification

Assessment and Optimization of the Interpretability of Machine Learning Models Applied to Transcriptomic Data

BenchXAI: Comprehensive Benchmarking of Post-hoc Explainable AI Methods on Multi-Modal Biomedical Data

Enabling interpretable machine learning for biological data with reliability scores

REM: An Integrative Rule Extraction Methodology for Explainable Data Analysis in Healthcare

Probing machine-learning classifiers using noise, bubbles, and reverse correlation

R.ROSETTA: an interpretable machine learning framework

Explainable Machine Learning to Identify Patient-specific Biomarkers for Lung Cancer

Can interpretability and accuracy coexist in cancer survival analysis?

BIG-TB: A benchmark for prediction and interpretability of sequence-based machine learning using *Mycobacterium tuberculosis* genomes

On Parameter Interpretability of Phenomenological-Based Semiphysical Models

Discovering Biomarker Proteins and Peptides for Parkinson’s Disease Prognosis Prediction with Machine Learning and Interpretability Methods

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

BIG-TB: A benchmark for prediction and interpretability of sequence-based machine learning using Mycobacterium tuberculosis genomes

BIG-TB: A benchmark for prediction and interpretability of sequence-based machine learning using Mycobacterium tuberculosis genomes