Peptides are formed by the dehydration condensation of multiple amino acids.
The primary structure of a peptide can be represented either as an amino acid
sequence or as a molecular graph consisting of atoms and chemical bonds.
Previous studies have indicated that deep learning routes specific to
sequential and graphical peptide forms exhibit comparable performance on
downstream tasks. Despite the fact that these models learn representations of
the same modality of peptides, we find that they explain their predictions
differently. Considering sequential and graphical models as two experts making
inferences from different perspectives, we work on fusing expert knowledge to
enrich the learned representations for improving the discriminative
performance. To achieve this, we propose a peptide co-modeling method, RepCon,
which employs a contrastive learning-based framework to enhance the mutual
information of representations from decoupled sequential and graphical
end-to-end models. It considers representations from the sequential encoder and
the graphical encoder for the same peptide sample as a positive pair and learns
to enhance the consistency of representations between positive sample pairs and
to repel representations between negative pairs. Empirical studies of RepCon
and other co-modeling methods are conducted on open-source discriminative
datasets, including aggregation propensity, retention time, antimicrobial
peptide prediction, and family classification from Peptide Database. Our
results demonstrate the superiority of the co-modeling approach over
independent modeling, as well as the superiority of RepCon over other methods
under the co-modeling framework. In addition, the attribution on RepCon further
corroborates the validity of the approach at the level of model explanation.

通过融合基于序列和图形编码的深度学习方法，本研究提出了一种名为 RepCon 的肽共建模方法，用于提高鉴别性能。实验证明，RepCon 方法在共建模框架下的优越性以及在模型解释方面的有效性。

肽序列和图形路径的联合建模

Co-modeling the Sequential and Graphical Route for Peptide

Noisy training labels can hurt model performance. Most approaches that aim to
address label noise assume label noise is independent from the input features.
In practice, however, label noise is often feature or
\textit{instance-dependent}, and therefore biased (i.e., some instances are
more likely to be mislabeled than others). E.g., in clinical care, female
patients are more likely to be under-diagnosed for cardiovascular disease
compared to male patients. Approaches that ignore this dependence can produce
models with poor discriminative performance, and in many healthcare settings,
can exacerbate issues around health disparities. In light of these limitations,
we propose a two-stage approach to learn in the presence instance-dependent
label noise. Our approach utilizes \textit{\anchor points}, a small subset of
data for which we know the observed and ground truth labels. On several tasks,
our approach leads to consistent improvements over the state-of-the-art in
discriminative performance (AUROC) while mitigating bias (area under the
equalized odds curve, AUEOC). For example, when predicting acute respiratory
failure onset on the MIMIC-III dataset, our approach achieves a harmonic mean
(AUROC and AUEOC) of 0.84 (SD [standard deviation] 0.01) while that of the next
best baseline is 0.81 (SD 0.01). Overall, our approach improves accuracy while
mitigating potential bias compared to existing approaches in the presence of
instance-dependent label noise.

我们提出了一种针对存在实例相关标签噪声的两阶段方法，利用锚定点取得了判别性能的显著改进，同时减轻了偏差。