Artificial intelligence (AI) techniques are widely applied in the life
sciences. However, applying innovative AI techniques to understand and
deconvolute biological complexity is hindered by the learning curve for life
science scientists to understand and use computing languages. An open-source,
user-friendly interface for AI models, that does not require programming skills
to analyze complex biological data will be extremely valuable to the
bioinformatics community. With easy access to different sequencing technologies
and increased interest in different 'omics' studies, the number of biological
datasets being generated has increased and analyzing these high-throughput
datasets is computationally demanding. The majority of AI libraries today
require advanced programming skills as well as machine learning, data
preprocessing, and visualization skills. In this research, we propose a
web-based end-to-end pipeline that is capable of preprocessing, training,
evaluating, and visualizing machine learning (ML) models without manual
intervention or coding expertise. By integrating traditional machine learning
and deep neural network models with visualizations, our library assists in
recognizing, classifying, clustering, and predicting a wide range of
multi-modal, multi-sensor datasets, including images, languages, and
one-dimensional numerical data, for drug discovery, pathogen classification,
and medical diagnostics.

我们提出了一个基于 Web 的一体化流程，能够进行数据预处理、训练、评估和可视化机器学习模型，无需手动干预或编程专业知识，通过集成传统机器学习和深度神经网络模型与可视化，帮助识别、分类、聚类和预测广泛的多模态、多传感器数据，包括图像、语言和一维数值数据，用于药物发现、病原体分类和医学诊断。

EndToEndML: 一个用于机器学习应用的开源端到端流水线

EndToEndML: An Open-Source End-to-End Pipeline for Machine Learning  Applications

CNNs and computational models of biological vision share some fundamental
principles, which opened new avenues of research. However, fruitful cross-field
research is hampered by conventional CNN architectures being based on spatially
and depthwise discrete representations, which cannot accommodate certain
aspects of biological complexity such as continuously varying receptive field
sizes and dynamics of neuronal responses. Here we propose deep continuous
networks (DCNs), which combine spatially continuous filters, with the
continuous depth framework of neural ODEs. This allows us to learn the spatial
support of the filters during training, as well as model the continuous
evolution of feature maps, linking DCNs closely to biological models. We show
that DCNs are versatile and highly applicable to standard image classification
and reconstruction problems, where they improve parameter and data efficiency,
and allow for meta-parametrization. We illustrate the biological plausibility
of the scale distributions learned by DCNs and explore their performance in a
neuroscientifically inspired pattern completion task. Finally, we investigate
an efficient implementation of DCNs by changing input contrast.

深度连续网络（DCNs）结合了空间连续滤波器和神经 ODE 的连续深度框架，能够学习滤波器的空间支持以及特征图的持续演化，与生物模型密切相关。DCNs 在标准图像分类和重构问题中具有多功能且广泛适用，可以提高参数和数据效率，并允许元参数化。我们展示了 DCNs 学习的尺度分布的生物合理性，并研究了它们在神经科学启发的模式补全任务中的性能。最后，我们通过改变输入对比度来实现 DCN 的高效实现。