Property-Guided Generative Modelling for Robust Model-Based Design with Imbalanced Data

The problem of designing protein sequences with desired properties is challenging, as it requires to explore a high-dimensional protein sequence space with extremely sparse meaningful regions. This has led to the development of model-based optimization (MBO) techniques that aid in the design, by using effective search models guided by the properties over the sequence space. However, the intrinsic imbalanced nature of experimentally derived datasets causes existing MBO approaches to struggle or outright fail. We propose a property-guided variational auto-encoder (PGVAE) whose latent space is explicitly structured by the property values such that samples are prioritized according to these properties. Through extensive benchmarking on real and semi-synthetic protein datasets, we demonstrate that MBO with PGVAE robustly finds sequences with improved properties despite significant dataset imbalances. We further showcase the generality of our approach to continuous design spaces, and its robustness to dataset imbalance in an application to physics-informed neural networks.

CIMLA: Interpretable AI for inference of differential causal networks

The discovery of causal relationships from high-dimensional data is a major open problem in bioinformatics. Machine learning and feature attribution models have shown great promise in this context but lack causal interpretation. Here, we show that a popular feature attribution model estimates a causal quantity reflecting the influence of one variable on another, under certain assumptions. We leverage this insight to implement a new tool, CIMLA, for discovering condition-dependent changes in causal relationships. We then use CIMLA to identify differences in gene regulatory networks between biological conditions, a problem that has received great attention in recent years. Using extensive benchmarking on simulated data sets, we show that CIMLA is more robust to confounding variables and is more accurate than leading methods. Finally, we employ CIMLA to analyze a previously published single-cell RNA-seq data set collected from subjects with and without Alzheimer's disease (AD), discovering several potential regulators of AD.

Toward Scalable Machine Learning and Data Mining: the Bioinformatics Case

In an effort to overcome the data deluge in computational biology and bioinformatics and to facilitate bioinformatics research in the era of big data, we identify some of the most influential algorithms that have been widely used in the bioinformatics community. These top data mining and machine learning algorithms cover classification, clustering, regression, graphical model-based learning, and dimensionality reduction. The goal of this study is to guide the focus of scalable computing experts in the endeavor of applying new storage and scalable computation designs to bioinformatics algorithms that merit their attention most, following the engineering maxim of "optimize the common case".

Relational Learning and Feature Extraction by Querying over Heterogeneous Information Networks

Many real world systems need to operate on heterogeneous information networks that consist of numerous interacting components of different types. Examples include systems that perform data analysis on biological information networks; social networks; and information extraction systems processing unstructured data to convert raw text to knowledge graphs. Many previous works describe specialized approaches to perform specific types of analysis, mining and learning on such networks. In this work, we propose a unified framework consisting of a data model -a graph with a first order schema along with a declarative language for constructing, querying and manipulating such networks in ways that facilitate relational and structured machine learning. In particular, we provide an initial prototype for a relational and graph traversal query language where queries are directly used as relational features for structured machine learning models. Feature extraction is performed by making declarative graph traversal queries. Learning and inference models can directly operate on this relational representation and augment it with new data and knowledge that, in turn, is integrated seamlessly into the relational structure to support new predictions. We demonstrate this system's capabilities by showcasing tasks in natural language processing and computational biology domains.