Results 71 to 80 of about 1,489,144 (280)

Digital twins to accelerate target identification and drug development for immune‐mediated disorders

FEBS Open Bio, EarlyView.
Digital twins integrate patient‐derived molecular and clinical data into personalised computational models that simulate disease mechanisms. They enable rapid identification and validation of therapeutic targets, prediction of drug responses, and prioritisation of candidate interventions.
Anna Niarakis, Philippe Moingeon
wiley   +1 more source

Online Multi-task Learning with Hard Constraints [PDF]

, 2009
We discuss multi-task online learning when a decision maker has to deal simultaneously with M tasks. The tasks are related, which is modeled by imposing that the M-tuple of actions taken by the decision maker needs to satisfy certain constraints. We give
Lugosi, Gabor, Papaspiliopoulos, Omiros, Stoltz, Gilles   +2 more
core   +6 more sources

Self-Paced Multi-Task Learning

, 2017
In this paper, we propose a novel multi-task learning (MTL) framework, called Self-Paced Multi-Task Learning (SPMTL). Different from previous works treating all tasks and instances equally when training, SPMTL attempts to jointly learn the tasks by ...
Dong, Weishan, Li, Changsheng, Liu, Qingshan, Wei, Fan, Yan, Junchi, Zha, Hongyuan   +5 more
core   +1 more source

Multi-Task Learning with Multi-Task Optimization

Multi-task learning solves multiple correlated tasks. However, conflicts may exist between them. In such circumstances, a single solution can rarely optimize all the tasks, leading to performance trade-offs. To arrive at a set of optimized yet well-distributed models that collectively embody different trade-offs in one algorithmic pass, this paper ...
Bai, Lu, Gupta, Abhishek, Ong, Yew-Soon
openaire   +2 more sources

Multi-Task Learning for Blind Source Separation [PDF]

IEEE Transactions on Image Processing, 2018
Blind source separation (BSS) aims to discover the underlying source signals from a set of linear mixture signals without any prior information of the mixing system, which is a fundamental problem in signal and image processing field. Most of the state-of-the-art algorithms have independently handled the decompositions of mixture signals. In this paper,
Bo Du, Shaodong Wang, Chang Xu, Nan Wang, Liangpei Zhang, Dacheng Tao   +5 more
openaire   +4 more sources

Rapid screening of staphylokinase protein variants using an unpurified cell‐free expression system

FEBS Open Bio, EarlyView.
An unpurified cell‐free protein synthesis (CFPS) platform enables rapid functional screening of staphylokinase variants. Direct plasminogen‐activation assays performed in microplate format provide real‐time activity readouts, allowing rapid identification and ranking of variants with improved or reduced fibrinolytic activity without protein ...
Maria Tomková, Veronika Hovanová, Jiri Damborsky, Erik Sedlák   +3 more
wiley   +1 more source

Incremental Data Stream Classification with Adaptive Multi-Task Multi-View Learning

Big Data Mining and Analytics
With the enhancement of data collection capabilities, massive streaming data have been accumulated in numerous application scenarios. Specifically, the issue of classifying data streams based on mobile sensors can be formalized as a multi-task multi-view
Jun Wang, Maiwang Shi, Xiao Zhang, Yan Li, Yunsheng Yuan, Chenglei Yang, Dongxiao Yu   +6 more
doaj   +1 more source

Multi-Task Learning Using Task Dependencies for Face Attributes Prediction

Applied Sciences, 2019
Face attributes prediction has an increasing amount of applications in human–computer interaction, face verification and video surveillance. Various studies show that dependencies exist in face attributes.
Di Fan, Hyunwoo Kim, Junmo Kim, Yunhui Liu, Qiang Huang   +4 more
doaj   +1 more source

Curriculum Learning for Multi-Task Classification of Visual Attributes

, 2017
Visual attributes, from simple objects (e.g., backpacks, hats) to soft-biometrics (e.g., gender, height, clothing) have proven to be a powerful representational approach for many applications such as image description and human identification.
Giannakopoulos, Theodore, Kakadiaris, Ioannis A., Nikou, Christophoros, Sarafianos, Nikolaos   +3 more
core   +1 more source

Deep Learning–Assisted Differentiation of Four Peripheral Neuropathies Using Corneal Confocal Microscopy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Peripheral neuropathies contribute to patient disability but may be diagnosed late or missed altogether due to late referral, limitation of current diagnostic methods and lack of specialized testing facilities. To address this clinical gap, we developed NeuropathAI, an interpretable deep learning–based multiclass classification ...
Chaima Ben Rabah, Ioannis N. Petropoulos, Mark Stettner, Maryam Ferdousi, Uazman Alam, Nathan Efron, Ahmed Serag, Rayaz A. Malik   +7 more
wiley   +1 more source