In this paper a multi-domain multi-task algorithm for feature selection in
bulk RNAseq data is proposed. Two datasets are investigated arising from mouse
host immune response to Salmonella infection. Data is collected from several
strains of collaborative cross mice. Samples from the spleen and liver serve as
the two domains. Several machine learning experiments are conducted and the
small subset of discriminative across domains features have been extracted in
each case. The algorithm proves viable and underlines the benefits of across
domain feature selection by extracting new subset of discriminative features
which couldn't be extracted only by one-domain approach.

提出了一种用于特征选择的多领域多任务算法，通过研究小鼠对沙门氏菌感染的宿主免疫反应产生的两个数据集，收集了来自多种协作交叉小鼠品系的数据，样本来自脾脏和肝脏，进行了多个机器学习实验并分别提取了在不同案例中具有区分度的少量跨领域特征子集，证明了这种算法的可行性并突出了跨领域特征选择相比于单领域方法所提取的新的具有区分度的特征子集的好处。

大规模 RNA 数据的特征选择多域多任务方法

A Multi-Domain Multi-Task Approach for Feature Selection from Bulk RNA  Datasets

Transferring knowledge across a sequence of reinforcement-learning tasks is
challenging, and has a number of important applications. Though there is
encouraging empirical evidence that transfer can improve performance in
subsequent reinforcement-learning tasks, there has been very little theoretical
analysis. In this paper, we introduce a new multi-task algorithm for a sequence
of reinforcement-learning tasks when each task is sampled independently from
(an unknown) distribution over a finite set of Markov decision processes whose
parameters are initially unknown. For this setting, we prove under certain
assumptions that the per-task sample complexity of exploration is reduced
significantly due to transfer compared to standard single-task algorithms. Our
multi-task algorithm also has the desired characteristic that it is guaranteed
not to exhibit negative transfer: in the worst case its per-task sample
complexity is comparable to the corresponding single-task algorithm.

介绍了一种新的多任务算法用于强化学习任务，该算法显着减少了探索的每个任务的样本复杂性，并保证不会出现负转移，与对应的单任务算法相比具有可比性。