The diagnosis of cancer primarily relies on pathological slides stained with hematoxylin and eosin (HE). These slides are typically prepared from tissue samples that have been fixed in formalin and embedded in paraffin (FFPE). However, the traditional process of staining FFPE samples with HE is time-consuming and resource-intensive. Recent advances in virtual staining technologies, driven by digital pathology and generative models, offer a promising alternative. However, the blurred structures in FFPE images pose unique challenges to achieving high-quality FFPE-to-HE virtual staining. In this context, we developed a novel Multiple Cell Semantics-guided supervised generative adversarial model, MCS-Stain. Specifically, the guidance consists of three components: 1) pretrained cell semantic guidance, aligning the powerful intermediate features of real and virtual images, embedded in the pretrained cell segmentation model (PCSM); 2) cell mask guidance, introducing comprehensible cell information which serves as part of the input to the discriminator through channel concatenation; 3) dynamic cell semantic guidance, aligning the dynamic intermediate features embedded in the generator during training. The comparative results on FFPE-to-HE datasets demonstrated that MCS-Stain outperforms existing state-of-the-art (SOTA) methods with substantial qualitative and quantitative improvements. Results across various PCSMs and data sources further confirmed its effectiveness and robustness. Notably, the dynamic cell semantic exhibits strong potential beyond FFPE-to-HE virtual staining, further demonstrated by virtual staining from HE images to immunohistochemical (IHC) images. In general, MCS-Stain presents a promising avenue to advance virtual staining techniques. Code is available at https://github.com/huyihuang/MCS-Stain.

Multiple instance learning is an effective paradigm for whole slide image (WSI) classification, where labels are only provided at the bag level. However, instance-level prediction is also crucial as it offers insights into fine-grained regions of interest. Existing multiple instance learning methods either solely focus on training a bag classifier or have the insufficient capability of exploring instance prediction. In this work, we propose a novel model-agnostic framework to boost existing multiple instance learning models, to improve the WSI classification performance in both bag and instance levels. Specifically, we propose a counterfactual inference-based sub-bag assessment method and a hierarchical instance searching strategy to help to search reliable instances and obtain their accurate pseudo labels. Furthermore, an instance classifier is well-trained to produce accurate predictions. The instance embedding it generates is treated as a prompt to refine the instance feature for bag prediction. This framework is model-agnostic, capable of adapting to existing multiple instance learning models, including those without specific mechanisms like attention. Extensive experiments on three datasets demonstrate the competitive performance of our method. Code will be available at https://github.com/centurion-crawler/CIMIL.

For the interaction prediction task in the biomedical field, most machine learning algorithms overlook the relationships between entities within a pair by treating their features independently. To address this issue, this paper proposes a novel Cluster-based Paired Decision Tree model (CPDT), which pairs synonymous features of entity pairs to form paired feature spaces for simultaneous processing. It employs an adaptive grid-based clustering algorithm to partition these spaces in an axis-parallel manner, constructing interpretable decision boundaries. Moreover, the clustering algorithm leverages the probability density function to accommodate various data distributions in paired feature spaces, enhancing the effectiveness of sample partitioning. Experimental results demonstrate that CPDT performs well in two interaction prediction tasks: Drug Combination and Synthetic Lethality predictions. Furthermore, CPDT yields simple and interpretable decision rules that uncover potential patterns in biomedical interaction prediction. It also identifies molecules with medical significance, suggesting promising applications in the biomedical domain.

Combination therapy has shown an obvious curative effect on complex diseases, whereas the search space of drug combinations is too large to be validated experimentally even with high-throughput screens. With the increase of the number of drugs, artificial intelligence techniques, especially machine learning methods, have become applicable for the discovery of synergistic drug combinations to significantly reduce the experimental workload. In this study, in order to predict novel synergistic drug combinations in various cancer cell lines, the cell line-specific drug-induced gene expression profile (GP) is added as a new feature type to capture the cellular response of drugs and reveal the biological mechanism of synergistic effect. Then, an enhanced cascade-based deep forest regressor (EC-DFR) is innovatively presented to apply the new small-scale drug combination dataset involving chemical, physical and biological (GP) properties of drugs and cells. Verified by the dataset, EC-DFR outperforms two state-of-the-art deep neural network-based methods and several advanced classical machine learning algorithms. Biological experimental validation performed subsequently on a set of previously untested drug combinations further confirms the performance of EC-DFR. What is more prominent is that EC-DFR can distinguish the most important features, making it more interpretable. By evaluating the contribution of each feature type, GP feature contributes 82.40%, showing the cellular responses of drugs may play crucial roles in synergism prediction. The analysis based on the top contributing genes in GP further demonstrates some potential relationships between the transcriptomic levels of key genes under drug regulation and the synergism of drug combinations.

The diagnosis and analysis of cancer are usually roughly judged through the accumulation of professional knowledge, which is difficult to deal with a large number of patient samples and a variety of causes and symptoms. Moreover, most of the existing machine learning methods are black-box, and can not give reasonable diagnosis basis. Therefore, an accurate and interpretable method is urgently required for cancer diagnosis. In this paper, a total of 700 serum samples consisting of three groups of patients and one group of healthy individuals were collected and subjected to SERS measurements. We rank the Raman spectra of 700 human SERA according to the feature importance, and construct the feature importance vector reflecting the spectral feature importance. We further construct candidate feature sets based on importance selection, so as to construct a random forest model based on feature ensemble selection. On the one hand, we compare the proposed method with the popular machine learning methods to verify the effectiveness in the task of cancer screening. On the other hand, we conduct qualitative and quantitative analysis of cancer characteristics, and give model basis and biomedical explanation for the impact of different important cancer characteristics on the final classification and diagnosis. Some more experimental results and discussions are included in the appendix. Our source code and appendix are available under https://github.com/liujiatong429/BIBM2021