Machine Learning Based Computer Aided Diagnosis of Breast Cancer Utilizing Anthropometric and Clinical Features

Breast cancer is one of the most prevalent types of cancers in females, which has become rampant all over the world in recent years. The survival rate of breast cancer patients degrades considerably for patients diagnosed at an advanced stage compared to those diagnosed at an early stage. The objective of this study is two folds. The first one is to find the most relevant biomarkers of breast cancer, which can be attained from regular blood analysis and anthropometric measurements. The other one is to improve the performance of current computer-aided diagnosis (CAD) system of early breast cancer detection. This study utilized a recent data set containing nine anthropometric and clinical attributes. In our methodology, first, we performed multicollinearity analysis and ranked the features based on the weighted average score obtained from four filter-based feature evaluation methods such as F-score, information gain, chi-square statistic, and Minimum Redundancy Maximum Relevance. Next, to improve the separability of the target classes, we scaled and weighted the dataset using min-max normalization and similarity-based attribute weighting by the k-means clustering algorithm, respectively. Finally, we trained standard machine learning (ML) models and evaluated the performance metrics by 10-fold cross-validation method. Our support vector machine (SVM) model with radial basis function (RBF) kernel appeared to be the most successful classifier by utilizing six features, namely, Body Mass Index (BMI), Age, Glucose, MCP-1, Resistin, and Insulin. The obtained classification accuracy, sensitivity, and specificity are 93.9% (95% CI: 93.2–94.6%), 95.1% (95% CI: 94.4–95.8%), and 94.0% (95% CI: 93.3–94.7%), respectively; these performance metrics outperformed state-of-the-art methods reported in the literature. The developed model could potentially assist the medical experts for the early diagnosis of breast cancer by employing a set of attributes that can be easily obtained from regular blood analysis and anthropometric measurements.     

基于蛋白质序列的泛素化位点预测研究进展

泛素化是目前广受关注的一种翻译后修饰过程,对蛋白质降解、DNA修复等多种细胞过程都具有重要的调控作用。本文根据国内外蛋白质泛素化位点预测的研究,分析了预测泛素化位点的特征属性,总结了对这些特征进行优化的特征选择方法,并对预测过程中所使用的各种机器学习分类器进行了概述。     

基于机器学习的蛋白质编码区识别

针对DNA序列编码区的识别问题,本研究提出一个特征向量和逻辑回归的组合模型。首先对DNA序列进行数值处理转化为特征向量,并结合k字符相对频率技术提取特征向量的元素特征,之后利用二分类逻辑回归算法,对编码区和非编码区进行准确区分。选取了HMR195和BG570两个基准数据集进行五折交叉验证,结果表明,平均AUC(Area Under Curve)值分别为0.981 3和0.987 4,明显优于传统的贝叶斯判别法和VOSSDFT等方法。此外,本文提出的特征向量的维度很低,提高了运算效率。因此,本文组合模型能够较为高效准确地识别蛋白质编码区。     

多特征融合的植物长链非编码RNA的预测

长链非编码RNA(Long non-coding RNA, lncRNA)是一类被定义为转录本的长度大于200 nt、没有蛋白编码能力的RNA转录本。研究表明,lncRNA在调节植物生长发育、表观遗传反应以及各种胁迫反应中起重要作用。但是与人类和动物相比,植物lncRNA的研究仍然处于起步阶段。目前,如何从大量的转录本中准确地挑选出lncRNA仍然是植物lncRNA研究领域的重要问题之一。本文构建了新的植物lncRNA和mRNA数据集,分析了数据集中植物lncRNA的序列及结构特征,提取了序列的k-mer频数信息、二级结构信息、开放阅读框信息以及序列的几何柔性等特征,基于SVM(Support Vector Machine, SVM)算法,用Jackknife检验对植物lncRNA进行了预测,并且计算了各种特征融合后对植物lncRNA预测结果的影响,准确率达到了96.14%。     

基于迁移学习的近红外光谱非侵入性血糖检测研究

目的：近红外光谱技术在无创血糖检测中面临因个体差异导致的模型泛化性不足问题,为了解决这一问题,提高数据利用率,并建立泛化能力更强的预测模型,本研究引入了迁移学习方法来研究近红外光谱非侵入性血糖检测。方法：迁移学习是一种旨在将源域的知识迁移到目标域,从而提高目标域任务性能的机器学习技术。在本研究中,我们将社区人群数据作为源域,将学生群体数据作为目标域,以改善无创血糖检测模型在目标域上的表现。为了验证迁移学习的有效性,本研究对比了迁移学习前后模型的性能。结果：通过迁移学习策略,模型在无创血糖检测任务中的表现得到了显著提升,迁移后的模型MAPE与MAE分别下降了52.5460%与6.0805%,RMSE与MSE分别下降了10.7215%与12.1135%。结论：本研究展示了迁移学习在非侵入性血糖检测领域的应用前景,通过将源域中难以获取但与血糖值相关的特征迁移到目标域,有望在未来实现便携式、连续性的血糖监测,这将极大地提高糖尿病患者的生活质量。无创血糖检测技术的进步,不仅能够减少患者的痛苦,还能提供更为便捷的血糖监测手段,为糖尿病管理提供有力支持。     

Deciphering the effects of gene deletion on yeast longevity using network and machine learning approaches

Longevity is one of the most basic and one of the most essential properties of all living organisms. Identification of genes that regulate longevity would increase understanding of the mechanisms of aging, so as to help facilitate anti-aging intervention and extend the life span. In this study, based on the network features and the biochemical/physicochemical features of the deletion network and deletion genes, as well as their functional features, a two-layer model was developed for predicting the deletion effects on yeast longevity. The first stage of our prediction approach was to identify whether the deletion of one gene would change the life span of yeast; if it did, the second stage of our procedure would automatically proceed to predict whether the deletion of one gene would increase or decrease the life span. It was observed by analyzing the predicted results that the functional features (such as mitochondrial function and chromatin silencing), the network features (such as the edge density and edge weight density of the deletion network), and the local centrality of deletion gene, would have important impact for predicting the deletion effects on longevity. It is anticipated that our model may become a useful tool for studying longevity from the angle of genes and networks. Moreover, it has not escaped our notice that, after some modification, the current model can also be used to study many other phenotype prediction problems from the angle of systems biology.     

基于单细胞转录组数据的疾病表型预测研究进展

单细胞转录组测序(scRNA-seq)已被广泛应用于基础医学研究中,分析和挖掘scRNA-seq数据有助于深入理解病变组织的细胞组成结构和功能,揭示复杂疾病过程和阐明药物作用机制,进而推动精准医学的发展。然而,如何基于海量的scRNA-seq数据对患者疾病表型进行预测,并筛选关键特征是单细胞技术临床转化的关键问题。本文综述了基于单细胞转录组数据进行患者疾病表型预测的相关方法,并对原理、算法、优缺点进行归纳和讨论,最后对相关研究的发展和应用进行了展望。