Scarcity of labeled data is a common problem in supervised classification,
since hand-labeling can be time consuming, expensive or hard to label; on the
other hand, large amounts of unlabeled information can be found. The problem of
scarcity of labeled data is even more notorious in hierarchical classification,
because the data of a node is split among its children, which results in few
instances associated to the deepest nodes of the hierarchy. In this work it is
proposed the semi-supervised hierarchical multi-label classifier based on local
information (SSHMC-BLI) which can be trained with labeled and unlabeled data to
perform hierarchical classification tasks. The method can be applied to any
type of hierarchical problem, here we focus on the most difficult case:
hierarchies of DAG type, where the instances can be associated to multiple
paths of labels which can finish in an internal node. SSHMC-BLI builds
pseudo-labels for each unlabeled instance from the paths of labels of its
labeled neighbors, while it considers whether the unlabeled instance is similar
to its neighbors. Experiments on 12 challenging datasets from functional
genomics show that making use of unlabeled along with labeled data can help to
improve the performance of a supervised hierarchical classifier trained only on
labeled data, even with statistical significance.

本研究提出一种基于局部信息的半监督分层多标签分类器（SSHMC-BLI），可利用标记和未标记的数据进行训练，用于执行分层分类任务。实验结果表明，利用未标记数据可以显著提高仅基于标记数据训练的监督式分层分类器的性能。

基于局部信息的半监督层次多标签分类器

Semi-Supervised Hierarchical Multi-Label Classifier Based on Local  Information

Gene set analysis is a mainstay of functional genomics, but it relies on
manually curated databases of gene functions that are incomplete and unaware of
biological context. Here we evaluate the ability of OpenAI's GPT-4, a Large
Language Model (LLM), to develop hypotheses about common gene functions from
its embedded biomedical knowledge. We created a GPT-4 pipeline to label gene
sets with names that summarize their consensus functions, substantiated by
analysis text and citations. Benchmarking against named gene sets in the Gene
Ontology, GPT-4 generated very similar names in 50% of cases, while in most
remaining cases it recovered the name of a more general concept. In gene sets
discovered in 'omics data, GPT-4 names were more informative than gene set
enrichment, with supporting statements and citations that largely verified in
human review. The ability to rapidly synthesize common gene functions positions
LLMs as valuable functional genomics assistants.

OpenAI 的 GPT-4 是一个基于生物医学知识的大型语言模型，能够从嵌入式知识中提出关于普遍基因功能的假设，为基因集分析提供可靠的功能概述和支持。

评估大型语言模型在基因集功能发现中的应用

Evaluation of large language models for discovery of gene set function

In the field of functional genomics, the analysis of gene expression profiles
through Machine and Deep Learning is increasingly providing meaningful insight
into a number of diseases. The paper proposes a novel algorithm to perform
Feature Selection on genomic-scale data, which exploits the reconstruction
capabilities of autoencoders and an ad-hoc defined Explainable Artificial
Intelligence-based score in order to select the most informative genes for
diagnosis, prognosis, and precision medicine. Results of the application on a
Chronic Lymphocytic Leukemia dataset evidence the effectiveness of the
algorithm, by identifying and suggesting a set of meaningful genes for further
medical investigation.

本文提出了一种利用自编码器和可解释的基于人工智能的评分来进行特征选择的算法，可用于诊断、预后和精准医疗。该算法在慢性淋巴细胞白血病数据集上应用，能够确定一组有意义的基因，供进一步医学研究参考。