GNE: A Deep Learning Framework for Gene Network Inference by Aggregating Biological Information

BACKGROUND: The topological landscape of gene interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, it is still a challenging task to aggregate heterogeneous biological information such as gene expression and gene interactions to achieve more accurate inference for prediction and discovery of new gene interactions. In particular, how to generate a unified vector representation to integrate diverse input data is a key challenge addressed here. RESULTS: We propose a scalable and robust deep learning framework to learn embedded representations to unify known gene interactions and gene expression for gene interaction predictions. These low- dimensional embeddings derive deeper insights into the structure of rapidly accumulating and diverse gene interaction networks and greatly simplify downstream modeling. We compare the predictive power of our deep embeddings to the strong baselines. The results suggest that our deep embeddings achieve significantly more accurate predictions. Moreover, a set of novel gene interaction predictions are validated by up-to-date literature-based database entries. CONCLUSION: The proposed model demonstrates the importance of integrating heterogeneous information about genes for gene network inference. GNE is freely available under the GNU General Public License and can be downloaded from GitHub ( https://github.com/kckishan/GNE )

Question-Context Alignment and Answer-Context Dependencies for Effective Answer Sentence Selection

Answer sentence selection (AS2) in open-domain question answering finds answer for a question by ranking candidate sentences extracted from web documents. Recent work exploits answer context, i.e., sentences around a candidate, by incorporating them as additional input string to the Transformer models to improve the correctness scoring. In this paper, we propose to improve the candidate scoring by explicitly incorporating the dependencies between question-context and answer-context into the final representation of a candidate. Specifically, we use Optimal Transport to compute the question-based dependencies among sentences in the passage where the answer is extracted from. We then represent these dependencies as edges in a graph and use Graph Convolutional Network to derive the representation of a candidate, a node in the graph. Our proposed model achieves significant improvements on popular AS2 benchmarks, i.e., WikiQA and WDRASS, obtaining new state-of-the-art on all benchmarks.Comment: final copy for INTERSPEECH 202

Controlling a robot using Android interface and voice

The objective of this thesis is to develop a program or an Android app to control a robot powered by Arduino using a motor driver shield and a Bluetooth modem.The process involved in building the robot includes the assembling of a chassis used for the robot and programing the Arduino as well as the interface for the android device. This thesis documents the design process for the robot and programming for the android interface. The details in the thesis give the information about the different aspects of computing involved in whole project. The outcome of the project is a combination of embedded computing and programming

Construction and calibration of an optical trap on a fluorescence optical microscope

The application of optical traps has come to the fore in the last three decades. They provide a powerful, sterile and noninvasive tool for the manipulation of cells, single biological macromolecules, colloidal microparticles and nanoparticles. An optically trapped microsphere may act as a force transducer that is used to measure forces in the piconewton regime. By setting up a well-calibrated single-beam optical trap within a fluorescence microscope system, one can measure forces and collect fluorescence signals upon biological systems simultaneously. In this protocol, we aim to provide a clear exposition of the methodology of assembling and operating a single-beam gradient force trap (optical tweezers) on an inverted fluorescence microscope. A step-by-step guide is given for alignment and operation, with discussion of common pitfalls.</p

<em>In situ</em> wavefront correction and its application to micromanipulation

In any optical system, distortions to a propagating wavefront reduce the spatial coherence of a light field, making it increasingly difficult to obtain the theoretical diffraction-limited spot size. Such aberrations are severely detrimental to optimal performance in imaging, nanosurgery, nanofabrication and micromanipulation, as well as other techniques within modern microscopy. We present a generic method based on complex modulation for true in situ wavefront correction that allows compensation of all aberrations along the entire optical train. The power of the method is demonstrated for the field of micromanipulation, which is very sensitive to wavefront distortions. We present direct trapping with optimally focused laser light carrying power of a fraction of a milliwatt as well as the first trapping through highly turbid and diffusive media. This opens up new perspectives for optical micromanipulation in colloidal and biological physics and may be useful for various forms of advanced imaging.</p