Biological Machine Learning Research Articles

Codon language embeddings provide strong signals for use in protein engineering

Protein representations from deep language models have yielded state-of-the-art performance across many tasks in computational protein engineering. In recent years, progress has primarily focused on parameter count, with recent models’ capacities surpassing the size of the very datasets they were trained on. Here we propose an alternative direction. We show that large language models trained on codons, instead of amino acid sequences, provide high-quality representations that outperform comparable state-of-the-art models across a variety of tasks. In some tasks, such as species recognition, prediction of protein and transcript abundance or melting point estimation, we show that a language model trained on codons outperforms every other published protein language model, including some that contain over 50 times more parameters. These results indicate that, in addition to commonly studied scale and model complexity, the information content of biological data provides an orthogonal direction to improve the power of machine learning in biology.

Current Status of Machine Learning Applications in Molecular Biology and Biological Signal Processing

Data science is the study of extracting knowledge and making inferences from data. Various data science techniques have been developed to manage, process, and use big/heterogeneous data. The most successful technique is deep learning using neural networks. This paper first describes the current state of machine learning. It then presents two applications of machine learning are presented. The first is an application of machine learning in molecular biology, and the second is an application of machine learning in biological signal processing. The molecular biology application will mainly explain DNA methylation analysis will be explained. The biological signal processing application will mainly explain the classification and quantification of tremors.

Artificial intelligence in interdisciplinary life science and drug discovery research.

Artificial intelligence in interdisciplinary life science and drug discovery research.

A guide to machine learning for biologists.

The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models to data; however, the specific methods are quite varied and can at first glance seem bewildering. In this Review, we aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are alsodiscussed.

Systematic auditing is essential to debiasing machine learning in biology

Biases in data used to train machine learning (ML) models can inflate their prediction performance and confound our understanding of how and what they learn. Although biases are common in biological data, systematic auditing of ML models to identify and eliminate these biases is not a common practice when applying ML in the life sciences. Here we devise a systematic, principled, and general approach to audit ML models in the life sciences. We use this auditing framework to examine biases in three ML applications of therapeutic interest and identify unrecognized biases that hinder the ML process and result in substantially reduced model performance on new datasets. Ultimately, we show that ML models tend to learn primarily from data biases when there is insufficient signal in the data to learn from. We provide detailed protocols, guidelines, and examples of code to enable tailoring of the auditing framework to other biomedical applications.

BactClass: Simplifying the Use of Machine Learning in Biology and Medicines

BactClass: Simplifying the Use of Machine Learning in Biology and Medicines

Validity of machine learning in biology and medicine increased through collaborations across fields of expertise

Machine learning (ML) has become an essential asset for the life sciences and medicine. We selected 250 articles describing ML applications from 17 journals sampling 26 different fields between 2011 and 2016. Independent evaluation by two readers highlighted three results. First, only half of the articles shared software, 64% shared data and 81% applied any kind of evaluation. Although crucial for ensuring the validity of ML applications, these aspects were met more by publications in lower-ranked journals. Second, the authors’ scientific backgrounds highly influenced how technical aspects were addressed: reproducibility and computational evaluation methods were more prominent with computational co-authors; experimental proofs more with experimentalists. Third, 73% of the ML applications resulted from interdisciplinary collaborations comprising authors from at least two of the three disciplines: computational sciences, biology, and medicine. The results suggested collaborations between computational and experimental scientists to generate more scientifically sound and impactful work integrating knowledge from both domains. Although scientifically more valid solutions and collaborations involving diverse expertise did not correlate with impact factors, such collaborations provide opportunities to both sides: computational scientists are given access to novel and challenging real-world biological data, increasing the scientific impact of their research, and experimentalists benefit from more in-depth computational analyses improving the technical correctness of work. Applications of machine learning in the life sciences and medicine require expertise in computational methods and in scientific subject matter. The authors surveyed articles in the life sciences that included machine learning applications, and found that interdisciplinary collaborations increased the scientific validity of published research.

Setting the standards for machine learning in biology.

Machine learning is a branch of artificial intelligence (AI) involving computer programs that are able to improve their own performance through experience (training). The diverse applications of new ‘deep learning’ approaches with neural networks are now expanding into the field of biology. But these applications to biological data require more scrutiny and caution to increase the standards of publishing and allow the AI revolution in biology to take off. David Jones discusses problems associated with the application of machine learning to biology and advocates for improving publishing standards in this area through a more thorough reporting on the design of the computational experiments.