Top Tweets for #CLASSIFIER
Anthropic introduces auto mode for Claude Code. A classifier determines which actions are safe. Now available as a preview for Team subscribers. https://t.co/8KSmgngZuI #Devops #Anthropic #automode #classifier - Follow for more
'Learning Bayesian Network Classifiers to Minimize Class Variable Parameters', by Shouta Sugahara, Koya Kato, James Cussens, Maomi Ueno.
https://t.co/Pr4dGhsGEF
#classifier #bayesian #class
Predictomes, a classifier-curated database of AlphaFold-modeled protein-protein interactions
https://t.co/TdlcdlthXq
#Predictomes #BioAI #classifier #AlphaFold
Summary: Protein–protein interactions (PPIs) are fundamental to nearly all cellular processes, yet we still lack a comprehensive structural map of how proteins physically interact across the proteome. While AlphaFold-Multimer (AF-M) offers a powerful tool for predicting the structures of protein complexes, its built-in confidence metrics are not sufficient to reliably distinguish true biological interactions from false positive predictions—especially in large-scale, proteome-wide screens.
To overcome this limitation, the authors developed SPOC (Structure Prediction and Omics-informed Classifier), a machine learning–based approach trained on curated datasets. SPOC integrates structural prediction features with omics data to effectively classify AF-M predicted PPIs as likely true or false. Importantly, it performs well even in large-scale, high-throughput prediction settings.
The researchers applied SPOC to an all-by-all interaction screen of nearly 300 human genome maintenance proteins, generating approximately 40,000 predicted protein complex structures. These predictions are publicly accessible at https://t.co/w6KFgEMd8n, where users can also evaluate their own AF-M predictions using the SPOC scoring framework.
High-confidence PPIs identified through this approach provide a valuable resource for hypothesis generation in genome maintenance research. More broadly, this work establishes a scalable framework for interpreting large-scale AlphaFold-Multimer screens and advances the goal of building a proteome-wide structural interactome.
Classification of high-dimensional data with spiked covariance matrix structure
Yin-Jen Chen, Minh Tang.
Action editor: Trevor Campbell.
https://t.co/UD1nE1dLqQ
#classification #classifier #discriminant
'A Hybrid Weighted Nearest Neighbour Classifier for Semi-Supervised Learning', by Stephen M. S. Lee, Mehdi Soleymani.
https://t.co/VLJIlyK4eu
#classifier #classifiers #supervised
A great pleasure working with @MadinaSukhanova @santanasantosl @DrewDuckett6 @MccordMatt on a clinically validated #DNA #methylation #classifier for risk stratification in #meningioma!
New publication from our department on a DNA methylation classifier to better predict meningioma recurrence risk.
Led by Drs. Sukhanova and Jamshidi with contributions from Dr. Santana dos Santos and others. #PurplePath
https://t.co/503DkkBdmE
🌍🛰️ Use of #Landsat Imagery Time-Series and #RandomForests #Classifier to Reconstruct Eelgrass Bed Distribution #Maps in Eeyou Istchee
✍️ Kevin Clyne et al.
🔗 https://t.co/rFTX6wDqdJ
'Classification in the high dimensional Anisotropic mixture framework: A new take on Robust Interpolation', by Stanislav Minsker, Mohamed Ndaoud, Yiqiu Shen.
https://t.co/TszOCVN48g
#classifiers #classifier #classification
🔬 Excited to share the publication "Automatic Detection and Classification of Hypertensive Retinopathy with Improved Convolution Neural Network and Improved SVM" 👉 https://t.co/qZh7ChSqWk
#channel_attention #spatial_attention #spatial_pooling #pooling #SVM #aKNN #classifier
Out-of-Distribution Learning with Human Feedback
Haoyue Bai, Xuefeng Du, Katie Rainey, Shibin Parameswaran, Yixuan Li.
Action editor: Ying Wei.
https://t.co/9yQ1o8YcFV
#classifier #detection #labeled
New cross-validated molecular #machine #learning 🤖derived #classifier predicts mucosal #inflammation in #ulcerative #colitis based on Mayo endoscopic subscore (AUC 0.85) new research👩🔬🧬🔬from @UAlberta 🇨🇦🌲@NaturePortfolio shows: https://t.co/GPmzOQhab8 #IBD #AI #GITwitter #gut
Active Learning via Classifier Impact and Greedy Selection for Interactive Image Retrieval
Leah Bar, Boaz Lerner, Nir Darshan, Rami Ben-Ari.
Action editor: ERIC EATON.
https://t.co/Me73tcFgtY
#classification #classifiers #classifier
🛰️🛰️ A One-Class #Classifier for the #Detection of GAN Manipulated #MultiSpectral Satellite #Images
✍️ Lydia Abady et al.
🔗 https://t.co/lWlMnl5nbo
Your Classifier Can Be Secretly a Likelihood-Based OOD Detector
Jirayu Burapacheep, Yixuan Li.
Action editor: Changjian Shui.
https://t.co/lhWFuDUfKH
#classifiers #classifier #classification
👋👋 A Space #Infrared Dim #Target Recognition #Algorithm Based on Improved DS Theory and Multi-Dimensional #Feature Decision Level Fusion Ensemble #Classifier
✍️ Xin Chen et al.
🔗 https://t.co/hXSQTQD1zm
'Consistent Multiclass Algorithms for Complex Metrics and Constraints', by Harikrishna Narasimhan et al.
https://t.co/84zIFkSsJ8
#classifier #multiclass #classification
Mislabeled examples detection viewed as probing machine learning models: concepts, survey and ext...
Thomas George, Pierre Nodet, Alexis Bondu, Vincent Lemaire
https://t.co/W8oZ7tH1fi
#classifiers #classifier #labeling
Class-Discriminative Attention Maps for Vision Transformers
Lennart Brocki, Jakub Binda, Neo Christopher Chung.
Action editor: Jianbo Jiao.
https://t.co/6BMy2La2or
#attention #importance #classifier
Mislabeled examples detection viewed as probing machine learning models: concepts, survey and ext...
Thomas George, Pierre Nodet, Alexis Bondu, Vincent Lemaire.
Action editor: Aditya Menon.
https://t.co/lfvByvEcxC
#classifiers #classifier #labeling
Selective Classification Under Distribution Shifts
Hengyue Liang, Le Peng, Ju Sun.
Action editor: Yonatan Bisk.
https://t.co/nb9ZdEyZ6Q
#classifiers #classification #classifier
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