Anticancer peptides (ACPs) are a class of molecules that have gained
significant attention in the field of cancer research and therapy. ACPs are
short chains of amino acids, the building blocks of proteins, and they possess
the ability to selectively target and kill cancer cells. One of the key
advantages of ACPs is their ability to selectively target cancer cells while
sparing healthy cells to a greater extent. This selectivity is often attributed
to differences in the surface properties of cancer cells compared to normal
cells. That is why ACPs are being investigated as potential candidates for
cancer therapy. ACPs may be used alone or in combination with other treatment
modalities like chemotherapy and radiation therapy. While ACPs hold promise as
a novel approach to cancer treatment, there are challenges to overcome,
including optimizing their stability, improving selectivity, and enhancing
their delivery to cancer cells, continuous increasing in number of peptide
sequences, developing a reliable and precise prediction model. In this work, we
propose an efficient transformer-based framework to identify anticancer
peptides for by performing accurate a reliable and precise prediction model.
For this purpose, four different transformer models, namely ESM, ProtBert,
BioBERT, and SciBERT are employed to detect anticancer peptides from amino acid
sequences. To demonstrate the contribution of the proposed framework, extensive
experiments are carried on widely-used datasets in the literature, two versions
of AntiCp2, cACP-DeepGram, ACP-740. Experiment results show the usage of
proposed model enhances classification accuracy when compared to the
state-of-the-art studies. The proposed framework, ESM, exhibits 96.45 of
accuracy for AntiCp2 dataset, 97.66 of accuracy for cACP-DeepGram dataset, and
88.51 of accuracy for ACP-740 dataset, thence determining new state-of-the-art.

提出了一种高效的基于 Transformer 的框架，利用 ESM、ProtBert、BioBERT 和 SciBERT 四种不同的 Transformer 模型从氨基酸序列中检测抗癌肽，实验证明该模型在准确率上优于现有研究，为抗癌肽研究确定了新的最先进水平。

ACP-ESM：一种基于蛋白定向变压器方法的抗癌肽分类的新型框架

ACP-ESM: A novel framework for classification of anticancer peptides  using protein-oriented transformer approach

Early diagnosis of the cancer cells is necessary for making an effective
treatment plan and for the health and safety of a patient. Nowadays, doctors
usually use a histological grade that pathologists determine by performing a
semi-quantitative analysis of the histopathological and cytological features of
hematoxylin-eosin (HE) stained histopathological images. This research
contributes a potential classification model for cancer prognosis to
efficiently utilize the valuable information underlying the HE-stained
histopathological images. This work uses the PatchCamelyon benchmark datasets
and trains them in a multi-layer perceptron and convolution model to observe
the model's performance in terms of precision, Recall, F1 Score, Accuracy, and
AUC Score. The evaluation result shows that the baseline convolution model
outperforms the baseline MLP model. Also, this paper introduced ResNet50 and
InceptionNet models with data augmentation, where ResNet50 is able to beat the
state-of-the-art model. Furthermore, the majority vote and concatenation
ensemble were evaluated and provided the future direction of using transfer
learning and segmentation to understand the specific features.

癌症早期诊断是制定有效治疗计划并确保病人健康安全的必要步骤，本研究通过深度学习构建了一个潜在的肿瘤预后分类模型，利用组织病理学图像中有价值的信息，选择了 PatchCamelyon 基准数据集，训练了多层感知器和卷积模型，并评估了其精确度、召回率、F1 得分、准确度和 AUC 得分，结果显示基准卷积模型表现优于基准多层感知器模型。此外，本文还引入了 ResNet50 和 InceptionNet 模型，并使用数据增强，其中 ResNet50 能够超越最先进的模型，最后还通过多数投票和连接集成的方法探索了将迁移学习和分割应用于理解特定特征的未来方向。

组织病理学癌症检测

Histopathologic Cancer Detection

Image classification with deep neural networks has reached state-of-art with
high accuracy. This success is attributed to good internal representation
features that bypasses the difficulties of the non-convex optimization
problems. We have little understanding of these internal representations, let
alone quantifying them. Recent research efforts have focused on alternative
theories and explanations of the generalizability of these deep networks. We
propose the alternative perturbation of deep models during their training
induces changes that lead to transitions to different families. The result is
an Anna Karenina Principle AKP for deep learning, in which less generalizable
models unhappy families vary more in their representation than more
generalizable models happy families paralleling Leo Tolstoy dictum that all
happy families look alike, each unhappy family is unhappy in its own way. Anna
Karenina principle has been found in systems in a wide range: from the surface
of endangered corals exposed to harsh weather to the lungs of patients
suffering from fatal diseases of AIDs. In our paper, we have generated
artificial perturbations to our model by hot-swapping the activation and loss
functions during the training. In this paper, we build a model to classify
cancer cells from non-cancer ones. We give theoretical proof that the internal
representations of generalizable happy models are similar in the asymptotic
limit. Our experiments verify similar representations of generalizable models.

本文提出了一个基于深度学习的模型，以分类癌细胞和非癌细胞，并证明了泛化性较好的模型具有相似的内部表示特征，同时提出了 Anna Karenina 原则（即快乐的家庭都是一样的，不快乐的家庭各有各的不快乐），以解释解释深度学习框架中的问题。