Tel Aviv University

About: Tel Aviv University is a education organization based out in Tel Aviv, Israel. It is known for research contribution in the topics: Population & Medicine. The organization has 47791 authors who have published 115959 publications receiving 3904391 citations. The organization is also known as: TAU & Universiṭat Tel-Aviv.

PREDICT: a method for inferring novel drug indications with application to personalized medicine.

Abstract: Inferring potential drug indications, for either novel or approved drugs, is a key step in drug development. Previous computational methods in this domain have focused on either drug repositioning or matching drug and disease gene expression profiles. Here, we present a novel method for the large-scale prediction of drug indications (PREDICT) that can handle both approved drugs and novel molecules. Our method is based on the observation that similar drugs are indicated for similar diseases, and utilizes multiple drug–drug and disease–disease similarity measures for the prediction task. On cross-validation, it obtains high specificity and sensitivity (AUC=0.9) in predicting drug indications, surpassing existing methods. We validate our predictions by their overlap with drug indications that are currently under clinical trials, and by their agreement with tissue-specific expression information on the drug targets. We further show that disease-specific genetic signatures can be used to accurately predict drug indications for new diseases (AUC=0.92). This lays the computational foundation for future personalized drug treatments, where gene expression signatures from individual patients would replace the disease-specific signatures.

Constant depth circuits, Fourier transform, and learnability

Abstract: In this paper, Boolean functions in ,4C0 are studied using harmonic analysis on the cube. The main result is that an ACO Boolean function has almost all of its "power spectrum" on the low-order coefficients. An important ingredient of the proof is Hastad's switching lemma (8). This result implies several new properties of functions in -4C(': Functions in AC() have low "average sensitivity;" they may be approximated well by a real polynomial of low degree and they cannot be pseudorandom function generators. Perhaps the most interesting application is an O(n POIYIOg(n ')-time algorithm for learning func- tions in ACO. The algorithm observes the behavior of an AC'" function on O(nPO'Y'Og(n)) randomly chosen inputs, and derives a good approximation for the Fourier transform of the function. This approximation allows the algorithm to predict, with high probability, the value of the function on other randomly chosen inputs.

Efficient Unbound Docking of Rigid Molecules

Abstract: We present a new algorithm for unbound (real life) docking of molecules, whether protein-protein or protein-drug. The algorithm carries out rigid docking, with surface variability/flexibility implicitly addressed through liberal intermolecular penetration. The high efficiency of the algorithm is the outcome of several factors: (i) focusing initial molecular surface fitting on localized, curvature based surface patches; (ii) use of Geometric Hashing and Pose Clustering for initial transformation detection; (iii) accurate computation of shape complementarity utilizing the Distance Transform; (iv) efficient steric clash detection and geometric fit scoring based on a multi-resolution shape representation; and (v) utilization of biological information by focusing on hot spot rich surface patches. The algorithm has been implemented and applied to a large number of cases.