Classification-Based Self-Learning for Weakly Supervised Bilingual Lexicon Induction [PDF]
, 2020 Mladen Karan +3 more
openalex +1 more source
An Integrated NLP‐ML Framework for Property Prediction and Design of Steels
Advanced Science, EarlyView.This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju +5 more
wiley +1 more source
scEpiLock: A Weakly Supervised Learning Framework for cis-Regulatory Element Localization and Variant Impact Quantification for Single-Cell Epigenetic Data. [PDF]
Biomolecules, 2022 Gong Y +4 more
europepmc +1 more source
Self-Guided Multiple Instance Learning for Weakly Supervised Disease Classification and Localization in Chest Radiographs [PDF]
, 2020 Constantin Seibold +3 more
openalex +1 more source
Advanced Science, EarlyView.This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo +6 more
wiley +1 more source
Weakly Supervised Learning for Predictive Maintenance
, 2021 With the advent of Industry 4.0, Predictive Maintenance (PdM) has garnered a lot of interest, both academically and in the industry. This thesis will be developing and using machine learning methods for PdM, using real world event-log data gathered from hybrid marine vessels, equipped with electric propulsion systems. The methods that will be used were
openaire +1 more source
Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data. [PDF]
Bioinformatics, 2021 Huang D +5 more
europepmc +1 more source
Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy
Advanced Science, EarlyView.Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu +4 more
wiley +1 more source
AAWS-Net: Anatomy-aware weakly-supervised learning network for breast mass segmentation. [PDF]
PLoS One, 2021 Sun Y, Ji Y.
europepmc +1 more source
Advanced Science, EarlyView.This cross‐species study reveals that pathological hyperactivity of BNST neurons in depressive states disrupts inhibitory period and isolated spikes in the BNST‐NAc circuit. DBS achieves its antidepressant effects by precisely restoring network inhibitory periods and high‐fidelity signal transmission.
Xin Lv +12 more
wiley +1 more source