To better combat the expansion of antibiotic resistance in pathogens, new compounds, particularly those with novel mechanisms-of-action [MOA], represent a major research priority in biomedical science. However, rediscovery of known antibiotics demonstrates a need for approaches that accurately identify potential novelty with higher throughput and reduced labor. Here we describe an explainable artificial intelligence classification methodology that emphasizes prediction performance and human interpretability by using a Hierarchical Ensemble of Classifiers model optimized with a novel feature selection algorithm called Clairvoyance; collectively referred to as a CoHEC model. We evaluated our methods using whole transcriptome responses from Escherichia coli challenged with 41 known antibiotics and 9 crude extracts while depositing 122 transcriptomes unique to this study. Our CoHEC model can properly predict the primary MOA of previously unobserved compounds in both purified forms and crude extracts at an accuracy above 99%, while also correctly identifying darobactin, a newly discovered antibiotic, as having a novel MOA. In addition, we deploy our methods on a recent E. coli transcriptomics dataset from a different strain and a Mycobacterium smegmatis metabolomics timeseries dataset showcasing exceptionally high performance; improving upon the performance metrics of the original publications. We not only provide insight into the biological interpretation of our model but also that the concept of MOA is a non-discrete heuristic with diverse effects for different compounds within the same MOA, suggesting substantial antibiotic diversity awaiting discovery within existing MOA.

UNLABELLED: We present a web server to predict the functional effect of single or multiple amino acid substitutions, insertions and deletions using the prediction tool PROVEAN. The server provides rapid analysis of protein variants from any organisms, and also supports high-throughput analysis for human and mouse variants at both the genomic and protein levels. AVAILABILITY AND IMPLEMENTATION: The web server is freely available and open to all users with no login requirements at http://provean.jcvi.org. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Information theory traditionally deals with “conventional data,” be it textual data, image, or video data. However, databases of various sorts have come into existence in recent years for storing “unconventional data” including biological data, social data, web data, topographical maps, and medical data. In compressing such data, one must consider two types of information: the information conveyed by the structure itself, and the information conveyed by the data labels implanted in the structure. In this paper, we attempt to address the former problem by studying information of graphical structures (i.e., unlabeled graphs). As the first step, we consider the Erdös-Rényi graphs G(n,p) over n vertices in which edges are added independently and randomly with probability p. We prove that the structural entropy of G(n,p) is (n;2)h(p)-logn!+o(1)=(n;2)h(p)-nlog+O(n), where h(p)=-plogp-(1-p)log(1-p) is the entropy rate of a conventional memoryless binary source. Then, we propose a two-stage compression algorithm that asymptotically achieves the structural entropy up to the nlog term (i.e., the first two leading terms) of the structural entropy. Our algorithm runs either in time O(n2) in the worst case for any graph or in time O(n+e) on average for graphs generated by G(n,p), where e is the average number of edges. To the best of our knowledge, this is the first provable (asymptotically) optimal graph compressor for Erdös-Rényi graph models. We use combinatorial and analytic techniques such as generating functions, Mellin transform, and poissonization to establish these findings. Our experiments confirm the theoretical results and also show the usefulness of our algorithm for some real-world graphs such as the Internet, biological networks, and social networks.

Constrained sequences are strings satisfying certain additional structural restrictions (e.g., some patterns are forbidden). They find applications in communication, digital recording, and biology. In this article, we restrict our attention to the so-called ( d , k ) constrained binary sequences in which any run of zeros must be of length at least d and at most k , where 0≤ d < k . In many applications, one needs to know the number of occurrences of a given pattern w in such sequences, for which we coin the term constrained pattern matching . For a given word w , we first estimate the mean and the variance of the number of occurrences of w in a ( d , k ) sequence generated by a memoryless source. Then we present the central limit theorem and large deviations results. As a by-product, we enumerate asymptotically the number of ( d , k ) sequences with exactly r occurrences of w , and compute Shannon entropy of ( d , k ) sequences with a given number of occurrences of w . We also apply our results to detect under- and overrepresented patterns in neuronal data (spike trains), which satisfy structural constraints that match the framework of ( d , k ) binary sequences. Throughout this article we use techniques of analytic combinatorics such as combinatorial calculus, generating functions, and complex asymptotics.

F. Brooks argues in [3] there is “no theory that gives us a metric for information embodied in structure” Shannon himself alluded to it fifty years earlier in his little known 1953 paper [14]. Indeed, in the past information theory dealt mostly with “conventional data,” be it textual data, image, or video data. However, databases of various sorts have come into existence in recent years for storing “unconventional data” including biological data, web data, topographical maps, and medical data. In compressing such data structures, one must consider two types of information: the information conveyed by the structure itself, and the information conveyed by the data labels implanted in the structure. In this paper, we attempt to address the former problem by studying information of graphical structures (i.e., unlabeled graphs). In particular, we consider the Erdös-Rényi graphs G(n; p) over n vertices in which edges are added randomly with probability p. We prove that the structural entropy of G(n; p) is (2nh(p) − log n! + o(1) = (2nh(p) − n log n + O(n); where h(p) = −p log p − (1 − p) log(1 − p) is the entropy rate of a conventional memoryless binary source. Then, we design a two-stage encoding that optimally compresses unlabeled graphs up to the first two leading terms of the structural entropy.

Traditionally, the performance of distributed algorithms has been measured in terms of time and message complexity.Message complexity concerns the number of messages transmitted over all the edges during the course of the algorithm. However, in energy-constrained ad hoc wireless networks (e.g., sensor networks), energy is a critical factor in measuring the efficiency of a distributed algorithm. Transmitting a message between two nodes has an associated cost (energy) and moreover this cost can depend on the two nodes (e.g., the distance between them among other things). Thus in addition to the time and message complexity, it is important to consider energy complexity that accounts for the total energy associated with the messages exchanged among the nodes in a distributed algorithm. This paper addresses the minimum spanning tree (MST) problem, a fundamental problem in distributed computing and communication networks. We study energy-efficient distributed algorithms for the Euclidean MST problem assuming random distribution of nodes. We show a non-trivial lower bound ofΩ(log n)on the energy complexity of any distributed MST algorithm. We then give an energy-optimal distributed algorithm that constructs an optimal MST with energy complexityO(log n)on average andO(log n log log n)with high probability. This is an improvement over the previous best known bound on the average energy complexity ofΩ(log2). Our energy-optimal algorithm exploits a novel property of the giant component of sparse random geometric graphs. All of the above results assume that nodes do not know their geometric coordinates. If the nodes know their own coordinates, then we give an algorithm withO(1)energy complexity (which is the best possible) that gives anO(1)approximation to the MST.

In the constrained pattern matching one searches for a given pattern in a constrained sequence, which finds applications in communication, magnetic recording, and biology. We concentrate on the so-called (d, k) constrained binary sequences in which any run of zeros must be of length at least d and at most k, where 0 ≤ d ≪ k. In our previous paper [2] we established the central limit theorem (CLT) for the number of occurrences of a given pattern in such sequences. Here, we present precise large deviations results, often used in diverse applications. In particular, we apply our results to detect under- and over-represented patterns in neuronal data (spike trains), which satisfy structural constraints that match the framework of (d, k) binary sequences. Among others, we obtain justifiably accurate statistical inferences about their biological properties and functions. Throughout, we use techniques of analytic information theory such as combinatorial calculus, generating functions, and complex asymptotics.

Traditionally, the performance of distributed algorithms has been measured in terms of time and message complexity. Message complexity concerns the number of messages transmitted over all the edges during the course of the algorithm. However, in energy-constraint radio or wireless networks (e.g., sensor networks), energy is a critical factor in measuring the efficiency of a distributed algorithm. Transmitting a message between two nodes has an associated cost (energy) and moreover this cost can depend on the two nodes (e.g., the distance between them among other things). Thus in addition to the time and message complexity, it is important to consider energy complexity that accounts for the total energy associated with the messages exchanged among the nodes in a distributed algorithm, and design energy-efficient distributed algorithms for energy-constraint networks.

Constrained sequences find applications in communication, magnetic recording, and biology. In this paper, we restrict our attention to the so-called (d, k) constrained binary sequences in which any run of zeros must be of length at least d and at most k, where 0lesd

Summary form only given. In this paper, various lossless and lossy adaptations and extensions of that paradigm are developed and tested, for the most part susceptible to simple linear time implementation. This is in contrast to the existing lossy variants of the Ziv-Lempel family of encoders, which have been traditionally built around the iterated quest for the best match within an assigned fidelity, thereby resulting in algorithms that are inherently superlinear and not easy to implement and analyze. Whereas a thorough analytical treatment of the proposed method seems hard, the basic algorithm and its main variants show good performance and latitude of practical applicability.

Lossy variants of the Ziv-Lempel family of encoders are built traditionally around the iterated quest for best matches within an assigned fidelity. Most of the resulting algorithms are inherently superlinear and not easy to implement and analyze. On the other hand, it is well known that any lossy scheme of low computational complexity must have the drawback that it cannot yield the minimal distortion which can be achieved by the optimal data compression algorithm specifically tailored for that case. This paper concentrates on parses of guaranteed low time performance, in which phrases are all distinct and generated mechanically by self-correlations of the source set forth by the parsing process. The goal of the paper is to describe the basic algorithm and some of its variants, show their good performance and latitude of practical applicability, and possibly stimulate in-depth analytical treatment, which is made particularly hard by the fact that the underlying processes are not stationary.