A primer on the use of machine learning to distil knowledge from data in biological psychiatry.

Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.

ABI1-based expression signature predicts breast cancer metastasis and survival.

Despite the current standard of care, breast cancer remains one of the leading causes of mortality in women worldwide, thus emphasizing the need for better predictive and therapeutic targets. ABI1 is associated with poor survival and an aggressive breast cancer phenotype, although its role in tumorigenesis, metastasis, and the disease outcome remains to be elucidated. Here, we define the ABI1-based seven-gene prognostic signature that predicts survival of metastatic breast cancer patients; ABI1 is an essential component of the signature. Genetic disruption of Abi1 in primary breast cancer tumors of PyMT mice led to significant reduction of the number and size of lung metastases in a gene dose-dependent manner. The disruption of Abi1 resulted in deregulation of the WAVE complex at the mRNA and protein levels in mouse tumors. In conclusion, ABI1 is a prognostic metastatic biomarker in breast cancer. We demonstrate, for the first time, that lung metastasis is associated with an Abi1 gene dose and specific gene expression aberrations in primary breast cancer tumors. These results indicate that targeting ABI1 may provide a therapeutic advantage in breast cancer patients.

Integrative survival analysis of breast cancer with gene expression and DNA methylation data.

MOTIVATION: Integrative multi-feature fusion analysis on biomedical data has gained much attention recently. In breast cancer, existing studies have demonstrated that combining genomic mRNA data and DNA methylation data can better stratify cancer patients with distinct prognosis than using single signature. However, those existing methods are simply combining these gene features in series and have ignored the correlations between separate omics dimensions over time. RESULTS: In the present study, we propose an adaptive multi-task learning method, which combines the Cox loss task with the ordinal loss task, for survival prediction of breast cancer patients using multi-modal learning instead of performing survival analysis on each feature dataset. First, we use local maximum quasi-clique merging (lmQCM) algorithm to reduce the mRNA and methylation feature dimensions and extract cluster eigengenes respectively. Then, we add an auxiliary ordinal loss to the original Cox model to improve the ability to optimize the learning process in training and regularization. The auxiliary loss helps to reduce the vanishing gradient problem for earlier layers and helps to decrease the loss of the primary task. Meanwhile, we use an adaptive weights approach to multi-task learning which weighs multiple loss functions by considering the homoscedastic uncertainty of each task. Finally, we build an ordinal cox hazards model for survival analysis and use long short-term memory (LSTM) method to predict patients' survival risk. We use the cross-validation method and the concordance index (C-index) for assessing the prediction effect. Stringent cross-verification testing processes for the benchmark dataset and two additional datasets demonstrate that the developed approach is effective, achieving very competitive performance with existing approaches. AVAILABILITY AND IMPLEMENTATION: https://github.com/bhioswego/ML_ordCOX.

Bioimage-Based Prediction of Protein Subcellular Location in Human Tissue with Ensemble Features and Deep Networks.

Prediction of protein subcellular location has currently become a hot topic because it has been proven to be useful for understanding both the disease mechanisms and novel drug design. With the rapid development of automated microscopic imaging technology in recent years, classification methods of bioimage-based protein subcellular location have attracted considerable attention for images can describe the protein distribution intuitively and in detail. In the current study, a prediction method of protein subcellular location was proposed based on multi-view image features that are extracted from three different views, including the four texture features of the original image, the global and local features of the protein extracted from the protein channel images after color segmentation, and the global features of DNA extracted from the DNA channel image. Finally, the extracted features were combined together to improve the performance of subcellular localization prediction. From the performance comparison of different combination features under the same classifier, the best ensemble features could be obtained. In this work, a classifier based on Stacked Auto-encoders and the random forest was also put forward. To improve the prediction results, the deep network was combined with the traditional statistical classification methods. Stringent cross-validation and independent validation tests on the benchmark dataset demonstrated the efficacy of the proposed method.

Case Mining for Research Findings in a Case-based Reasoning System in the Biology of Aging.

Scientific literature has been quickly expanding as the availability of articles in electronic form has increased rapidly. For the scientific researcher and the practitioner alike, keeping track with the advancement of the research is an ongoing challenge, and for the most part, the mass of experience recorded in the scientific literature is largely untapped. In particular, novice scientists, non researchers, and students would benefit from a system proposing recommendations for the problems they are interested in resolving. This article presents the first stages of the Digital Knowledge Finder design, a case-based reasoning system to manage experience from the scientific literature. One of the main functionality of the system is to enable both to represent the experience in a declarative and searchable form, and to reason from it through reuse - the latter being a consequence of the former. This article focuses on research findings mining and results from an aging literature dataset.

Mémoire: A framework for semantic interoperability of case-based reasoning systems in biology and medicine.

OBJECTIVE: Mémoire is a framework for sharing and distributing case bases and case-based reasoning (CBR) systems in biology and medicine. METHODS AND MATERIAL: This paper first introduces the semantic Web approach to build a better Web where search engines, knowledge sources and servers, applications and services can live, work, and learn in cooperation. This semantic approach is particularly well suited for biomedical domains because significant ontologies have been developed there and constitute a sound basis for the standardization effort required for the semantic Web. Case-based reasoning systems in biomedicine have also benefited from these biomedical ontologies and models. RESULTS: This article demonstrates for three such systems how a semantics infused approach in CBR gives better, more accurate results in CBR. From this previous work on a semantic approach in CBR in biomedicine, the Mémoire framework has evolved. CONCLUSION: Mémoire proposes a unified OWL-based representation language for cases and case-based ontologies in biomedicine, where a Web Ontology Language (OWL) is a language to represent ontologies on the Web. Mémoire provides a set of tools for building case-based reasoning systems compliant with its language. Mémoire is extensible and can be adapted to different types of biomedical application domains, tasks, and environments. Mémoire will permit bridging the gap within the multiple case-based reasoning systems dedicated to a single domain, and make available to agents and Web services the case-based competency of the CBR systems adopting its interchange language. The approach could be extended to other application domains of CBR.

Case-based reasoning in the health sciences: What's next?

OBJECTIVES: This paper presents current work in case-based reasoning (CBR) in the health sciences, describes current trends and issues, and projects future directions for work in this field. METHODS AND MATERIAL: It represents the contributions of researchers at two workshops on case-based reasoning in the health sciences. These workshops were held at the Fifth International Conference on Case-Based Reasoning (ICCBR-03) and the Seventh European Conference on Case-Based Reasoning (ECCBR-04). RESULTS: Current research in CBR in the health sciences is marked by its richness. Highlighted trends include work in bioinformatics, support to the elderly and people with disabilities, formalization of CBR in biomedicine, and feature and case mining. CONCLUSION: CBR systems are being better designed to account for the complexity of biomedicine, to integrate into clinical settings and to communicate and interact with diverse systems and methods.