• Suche: Interpretable Machine Learning with Python

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182

Adversarial training improves model interpretability in single-cell RNA-seq analysis.
Sadria, Mehrshad ; Layton, Anita ; Bader, Gary D
Bioinformatics Advances; 2023, Vol. 3 Issue 1, p1-10, 10p

RNA sequencing COMPUTATIONAL biology DEEP learning PYTHON programming langu... BIOINFORMATICS
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183

ParaDeep: sequence-based deep learning for residue-level paratope prediction using chain-aware BiLSTM-CNN models
Piyachat Udomwong ; Thanathat Pamonsupornwichit ; Kanchanok Kodchakorn ; et al.
Frontiers in Bioinformatics, Vol 5 (2025)

antibody binding site pr... deep learning BiLSTM-CNN heavy and light chains paratope identification sequence modeling …
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184

Efficient and visualizable convolutional neural networks for COVID-19 classification using Chest CT
Garg, Aksh ; Salehi, Sana ; Rocca, Marianna La ; et al.
In Expert Systems With Applications 1 June 2022 195

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185

M.E.T.A.-AI: A Modular, Emergent, Topological Architecture for Trustworthy AGI
Blankert, Jean Philippe

Artificial General Intel... Modular AI Architecture Topological AI Emergent Computation Symbolic Reasoning Ethical AI …
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186

Python code for Sturm and Wexler (2021): Conservation laws in a neural network architecture
Patrick Obin Sturm ; orcid:0000-0001-7361-

machine learning physically interpretable... photochemical modeling surrogate model conservation laws
E-Ressource
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187

Interpretable machine learning models for single-cell ChIP-seq imputation
Albrecht, Steffen ; Andreani, Tommaso ; Andrade-Navarro, Miguel A. ; et al.

0301 basic medicine 03 medical and health sc...
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188

A multi-modality framework for precise brain tumor detection and multi-class classification using hybrid GAN approach
Karpakam, S. ; Kumareshan, N.
In Biomedical Signal Processing and Control June 2025 104

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189

Deep-Learning Application to in silico Drug Design
Gomes, António José Preto Martins

Aprendizagem automática Aprendizagem profunda Desenho de fármacos Inteligência Artificial Proteínas Artificial Intelligence …
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190

A Performance Study of Deep Neural Network Representations of Interpretable ML on Edge Devices with AI Accelerators
Julian Schauer ; Payman Goodarzi ; Jannis Morsch ; et al.
Sensors, Vol 25, Iss 18, p 5681 (2025)

edge computing smart sensors interpretable ML AI accelerator latency energy efficiency …
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191

Genome-Factory: An Integrated Library for Tuning, Deploying, and Interpreting Genomic Models
Wu, Weimin ; Song, Xuefeng ; Wen, Yibo ; et al.

Genomics Machine Learning
Report
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192

DeepKoopFormer: A Koopman Enhanced Transformer Based Architecture for Time Series Forecasting
Forootani, Ali ; Khosravi, Mohammad ; Barati, Masoud

Machine Learning
Report
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193

Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance.
Wild, Romina ; Wodaczek, Felix ; Del Tatto, Vittorio ; et al.
Nature Communications; 1/2/2025, Vol. 16, p1-12, 12p

FEATURE selection ARTIFICIAL intelligence MACHINE learning BENCHMARK problems (Comp... MOLECULAR weights
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194

Protocol for deep-learning-driven cell type label transfer in single-cell RNA sequencing data
Zabetian, Zoe ; Gonzalez-Ferrer, Jesus ; Lehrer, Julian ; et al.
In STAR Protocols 20 June 2025 6(2)

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195

BackboneLearn: A Library for Scaling Mixed-Integer Optimization-Based Machine Learning
Digalakis Jr, Vassilis ; Ziakas, Christos

Computer Science - Machi... Mathematics - Optimizati... Statistics - Machine Lea...
Report
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196

Predicting the shear strength of rectangular RC beams strengthened with externally-bonded FRP composites using constrained monotonic neural networks
Benzaamia, Ali ; Ghrici, Mohamed ; Rebouh, Redouane ; et al.
In Engineering Structures 15 August 2024 313

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197

Gradient free stochastic training of ANNs, with local approximation in partitions.
Bakas, N. P. ; Langousis, A. ; Nicolaou, M. A. ; et al.
Stochastic Environmental Research & Risk Assessment; Jul2023, Vol. 37 Issue 7, p2603-2617, 15p

PARTIAL differential equ... PYTHON programming langu... ARTIFICIAL neural networ... COMPUTER programming DATABASES MACHINE learning …
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198

HCGA: Highly comparative graph analysis for network phenotyping
Peach, Robert L. ; Arnaudon, Alexis ; Schmidt, Julia A. ; et al.
In Patterns 9 April 2021 2(4)

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200

CVD22: Explainable artificial intelligence determination of the relationship of troponin to D-Dimer, mortality, and CK-MB in COVID-19 patients
Kırboğa, Kevser Kübra ; Küçüksille, Ecir Uğur ; Naldan, Muhammet Emin ; et al.
In Computer Methods and Programs in Biomedicine May 2023 233

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