We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

Motivation The computational prediction of gene function is a key step in making full use of newly sequenced genomes. Function is generally predicted by transferring annotations from homologous genes or proteins for which experimental evidence exists. The 'ortholog conjecture' proposes that orthologous genes should be preferred when making such predictions, as they evolve functions more slowly than paralogous genes. Previous research has provided little support for the ortholog conjecture, though the incomplete nature of the data cast doubt on the conclusions. Results We use experimental annotations from over 40 000 proteins, drawn from over 80 000 publications, to revisit the ortholog conjecture in two pairs of species: (i) Homo sapiens and Mus musculus and (ii) Saccharomyces cerevisiae and Schizosaccharomyces pombe. By making a distinction between questions about the evolution of function versus questions about the prediction of function, we find strong evidence against the ortholog conjecture in the context of function prediction, though questions about the evolution of function remain difficult to address. In both pairs of species, we quantify the amount of information that would be ignored if paralogs are discarded, as well as the resulting loss in prediction accuracy. Taken as a whole, our results support the view that the types of homologs used for function transfer are largely irrelevant to the task of function prediction. Maximizing the amount of data used for this task, regardless of whether it comes from orthologs or paralogs, is most likely to lead to higher prediction accuracy. Availability and implementation https://github.com/predragradivojac/oc. Supplementary information Supplementary data are available at Bioinformatics online.

The ortholog conjecture revisited: the value of orthologs and paralogs in function prediction.

The fast and seemingly uncontrollable spread of the novel coronavirus disease (COVID-19) poses great challenges to an already overloaded health system worldwide. It thus exemplifies an urgent need for fast and effective triage. Such triage can help in the implementation of the necessary measures to prevent patient deterioration and conserve strained hospital resources. We examine two types of machine learning models, a multilayer perceptron artificial neural networks and decision trees, to predict the severity level of illness for patients diagnosed with COVID-19, based on their medical history and laboratory test results. In addition, we combine the machine learning models with a LIME-based explainable model to provide explainability of the model prediction. Our experimental results indicate that the model can achieve up to 80% prediction accuracy for the dataset we used. Finally, we integrate the explainable machine learning models into a mobile application to enable the usage of the proposed models by medical staff worldwide.

A LIME-Based Explainable Machine Learning Model for Predicting the Severity Level of COVID-19 Diagnosed Patients

Estimating class proportions has emerged as an important direction in positive-unlabeled learning. Well-estimated class priors are key to accurate approximation of posterior distributions and are necessary for the recovery of true classification performance. While significant progress has been made in the past decade, there remains a need for accurate strategies that scale to big data. Motivated by this need, we propose an intuitive and fast nonparametric algorithm to estimate class proportions. Unlike any of the previous methods, our algorithm uses a sampling strategy to repeatedly (1) draw an example from the set of positives, (2) record the minimum distance to any of the unlabeled examples, and (3) remove the nearest unlabeled example. We show that the point of sharp increase in the recorded distances corresponds to the desired proportion of positives in the unlabeled set and train a deep neural network to identify that point. Our distance-based algorithm is evaluated on forty datasets and compared to all currently available methods. We provide evidence that this new approach results in the most accurate performance and can be readily used on large datasets.

/pdf/fast-nonparametric-estimation-of-class-proportions-in-the-50hr11xkcb.pdf

Fast Nonparametric Estimation of Class Proportions in the Positive-Unlabeled Classification Setting

Although distance measures are used in many machine learning algorithms, the literature on the context-independent selection and evaluation of distance measures is limited in the sense that prior k...

The Exploitation of Distance Distributions for Clustering

We propose a new class of metrics on sets, vectors, and functions that can be used in various stages of data mining, including exploratory data analysis, learning, and result interpretation. These new distance functions unify and generalize some of the popular metrics, such as the Jaccard and bag distances on sets, Manhattan distance on vector spaces, and Marczewski-Steinhaus distance on integrable functions. We prove that the new metrics are complete and show useful relationships with f-divergences for probability distributions. To further extend our approach to structured objects such as ontologies, we introduce information-theoretic metrics on directed acyclic graphs drawn according to a fixed probability distribution. We conduct empirical investigation to demonstrate the effectiveness on real-valued, high-dimensional, and structured data. Overall, the new metrics compare favorably to multiple similarity and dissimilarity functions traditionally used in data mining, including the Minkowski (
 $$L^p$$
 ) family, the fractional $$L^p$$
 family, two f-divergences, cosine distance, and two correlation coefficients. We provide evidence that they are particularly appropriate for rapid processing of high-dimensional and structured data in distance-based learning.

A new class of metrics for learning on real-valued and structured data.

The importance of scientific conferences, symposia, workshops and satellite meetings is often discussed in scientific circles. When deciding where to submit a manuscript and present new findings, the quality of science and the opportunity to network with top researchers are of significant concern. To explore the question of the quality of science, we are interested in assessing the impact of primary research presented in the meetings in the field of bioinformatics and computational biology. We looked at five conferences with good reputation: Intelligent Systems for Molecular Biology (ISMB), Pacific Symposium on Biocomputing (PSB), Research in Computational Molecular Biology (RECOMB), European Conference on Computational Biology (ECCB) and the ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (BCB). We collected all research papers published in these venues and extracted their citations. We then summarized these citations to quantify the long-term performance of an average paper, median paper, and median of the top 10 papers for each meeting in each year. Our results suggest that the original research presented in all conferences is influential, with ISMB being more impactful than ECCB and RECOMB, that themselves have comparable statistics. Despite some outstanding years and strong top papers, PSB ranked next, and ahead of BCB. Although citation-based measures of impact are imperfect, we submit that the results of our analyses provide a useful characterization of these venues.


AVAILABILITY
https://github.com/predragradivojac/conferences.


SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.

An examination of citation-based impact of the computational biology conferences.

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A new class of metrics for learning on real-valued and structured data.

An examination of citation-based impact of the computational biology conferences.